literature review on drones

A review on drones controlled in real-time

• Open access
• Published: 05 January 2021
• Volume 9 , pages 1832–1846, ( 2021 )

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• Vemema Kangunde   ORCID: orcid.org/0000-0001-7169-7632 1 ,
• Rodrigo S. Jamisola Jr. 1 &
• Emmanuel K. Theophilus 1  

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This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

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1 Introduction

A drone, also known as unmanned aerial vehicle (UAV), is an aircraft without a human pilot on board [ 1 , 2 ]. There has been a rapid development of drones for the past few decades due to the advancement of components such as micro electro-mechanical systems (MEMS) sensors, microprocessors, high energy lithium polymer (LiPo) batteries, as well as more efficient and compact actuators [ 3 , 4 , 5 ]. Drones are now present in many daily life activities [ 2 , 6 , 7 , 8 ]. They are used in many applications such as inspecting pipelines and power lines, surveillance and mapping, military combat, agriculture, delivery of medicines in remote areas, aerial mapping, and many others [ 2 , 9 , 10 , 11 , 12 ]. See Figs.  1 and 2 for some drones applications. Robotic manipulators, found in many applications [ 13 , 14 , 15 ], have in recent years been implemented on UAV platforms [ 16 , 17 , 18 ] for tasks such as aerial manipulation, grasping, and cooperative transportation. The unstable dynamics of the robotic arm, which increase control complexity of UAVs, have widely been studied in the literature [ 19 , 20 , 21 , 22 ].

UAVs technology is rapidly growing while UAV solutions are being proposed at faster rates as various needs arise. Drone features are determined by specific UAV applications as well as competition in the commercial market [ 23 , 23 , 24 , 25 ]. In [ 26 ], a review of the most recent applications of UAVs in the cryosphere was conducted. Compared to conventional spaceborne or airborne remote sensing platforms [ 27 , 28 , 29 ], UAVs offer more advantages in terms of data acquisition windows, revisits, sensor types, viewing angles, flying altitudes, and overlap dimensions [ 26 , 30 , 31 , 32 ]. The review shows that across the world, applications used various multirotor and fixed-wing UAV platforms. Red, green, blue (RGB) sensors were the most used, and applications utilised quality video transmission to the ground control station. The study in [ 33 ] demonstrates how versatile and fast-growing is the adoption of UAV solutions in daily life scenarios. They propose the design of a system capable of detecting coronavirus automatically from the thermal image quickly and with less human interactions using IoT-based drone technology. The UAV system is equipped with two cameras: an optical camera and a thermal camera. It conveys to the ground control station (GCS) the image of the person, the global positioning system (GPS) location as well as a thermal image of the hot body detected. The system combines IoT, virtual reality, and live video feedback to control the camera for monitoring people.

Picture reprinted from https://aibirduav.diytrade.com

The KC2800 is a fixed-wing drone used for surveillance and mapping

Picture reprinted from https://www.indiamart.com

Quadrotor drone spraying pesticide on crops

Picture reprinted from  https://thewiredshopper.com

Picture reprinted from https://thewiredshopper.com

On the other hand, apart from advancements in custom-made drones, commercial drone manufacturers are actively improving their products. Latest, more advanced drones are presented at https://thewiredshopper.com , see Figs. 3 and 4 . DJI Phantom 4, for example, is equipped with an automatic collision avoidance system. It has a sport mode that disables collision detection and enables fast speeds. It also has an active tracking technology that enables the selection of another moving object, like a car or another drone, and the Phantom 4 will autonomously follow it without assistance from the human pilot. The drone is equipped with a 3-axis camera and can record 4K resolution video at 30 fps and 1080p resolution at 12 fps. It will take 12-megapixel images in Adobe DNG raw format. It has gimbal stabilization technology and a built-in video editor. Other latest drones in the market include the AirDog drone by AirDog, 3DR Solo Drone by 3DRobotics, and Yuneec Typhoon H by Yuneec. A UAV’s operational environment is highly dynamic due to unpredictable changes in weather conditions affecting the air space. For drones to be reliable, their flight controllers must adapt to these environmental changes in real-time. Control of highly maneuverable UAVs has been extensively studied for the past decades.

2 Drone hardware overview

A UAV is controlled by an embedded computer called the Flight Control System (FCS) or flight controller [ 34 , 35 , 36 ], basically consisting of a control software loaded into a microcontroller. The microcontroller reads information from on-board sensors, such as accelerometers, gyroscopes, magnetometers, pressure sensors, GPS, etc.,as well as input from the pilot, perform control calculations, and control the motors on the UAV [ 37 , 38 ]. The FCS as well as the set of sensors would be mounted on the drone air frame. Drone air frames, typically made of strong, light composite materials, are mostly relatively small with limited space for avionics [ 39 , 40 ]. A set of sensors, such as TV cameras, infrared cameras, thermal sensors, chemical, biological sensors, meteorological sensors etc., used to gather information during drone applications need to be lightweight to reduce UAV payload [ 41 , 42 , 43 , 44 ]. The information gathered from the sensors can be partially processed on-board or transmitted to the ground station for further processing [ 45 , 46 , 47 ]. An on-board controller, separate from the flight controller, can be used to operate the payload sensors [ 48 , 49 , 50 ]. Figure 5 shows the Cc3d open source flight controller used as a UAV flight controller.

The Pixhawk flight controller is an open-source hardware project equipped with sensors necessary for flight control [ 51 , 52 , 53 ]. It includes a CPU with RAM as well as gyroscope, compass, 3-axis accelerometer, barometric pressure, and magnetometer [ 54 , 55 ]. The Paparazzi flight controller, developed by Ecole Nationale de lAviation Civil (ENAC) UAV Lab since 2003 [ 34 ], is the first and oldest open-source drone hardware and software project. In March 2017 ENAC Lab released the Paparazzi Chimera autopilot. A detailed survey on open-source flight controllers was disclosed by Ebeid et.al in [ 34 ]. An autopilot software is used for drone automatic flight control [ 56 ]. On the other hand, drones can be operated remotely through a remote controller [ 57 , 58 , 59 ].

Picture reprinted from https://www.google.com/search?q=Cc3d++flight+controller

UAV hardware components

2.1 State observation

The FCS requires information on UAV states such as attitude, position, and velocity for control implementation [ 60 ]. The commonly used state observer is the inertial guidance system. Other attitude determination devices such as infrared or vision based sensors can be used [ 61 , 62 ]. The inertial guidance system (IGS), also referred to as inertial navigation system (INS) [ 63 ] consists of the inertial measurement unit (IMU) and the navigation computer. The IMU has three orthogonal rate-gyroscopes, three orthogonal accelerometers and sometimes 3-axis magnetometer to determine angular velocity, linear acceleration and orientation respectively [ 64 ]. Inertial guidance systems are entirely self reliant within a vehicle where they are used. They do not rely on transmission of signals from the vehicle or reception of signals from external sources. Inertial guidance systems can be used to estimate the location of the UAV relative to its initial position using a method known as dead reckoning [ 65 ]. Global navigation satellite system (GNSS) provides location estimates using at least four satellites [ 65 ].

2.2 State estimation

State estimation feedback is required for UAV control, such estimates are usually for attitude, position, and velocity [ 66 ]. On board sensor readings are fed to the UAV autopilot system to generate UAV state estimates [ 67 ]. The need for state estimation is due to the fact that data from measurement sensors is prone to uncertainties due to atmospheric disturbances, vibrations noise, inaccuracy of coordinate transformations, and missing measurements [ 68 ]. Sensors such as the GPS suffers from signal obstruction and reflections caused by nearby objects leading to missing or inadequate information [ 69 ].

To compensate for uncertainties and lack of information from individual sensors, multiple sensor data fusion can be employed to incorporate advantages of different types of sensors [ 70 ]. The altitude heading and reference system combines gyroscope, accelerometer, magnetometer, GPS and pressure sensors to measure UAV states. Sensor data for state estimates need to be updated at a relatively high frequency, normally above 20 Hz for small UAVs. Kalman filtering can be employed to make optimal estimations for sensors with lower update frequencies, such as the GPS, which typically has an update frequency of 4 Hz. Kalman filtering can also be used to process gyroscope readings which are susceptible to noise and drift. The other technique to improve gyroscopic readings is to model the gyroscope random noise and then offsetting it according to the model, this is referred to as model compensation [ 71 ].

2.3 Controller design for autopilots

Most current commercial and research autopilots focus on GPS-based waypoints navigation to follow a desires path [ 72 ]. Waypoint navigation is essential for autonomous control of UAVs for UAV tasks beyond the pilot’s sight. The pilot could control the UAV from the GCS using a graphical User Interface (GUI), the location as well as other needed information about the UAV would be displayed at the the GCS [ 45 ]. The path following control of a UAV involves the control of roll, pitch, altitude and air speed for trajectory tracking and waypoint navigation [ 73 ]. GPS waypoint navigation involves providing sequential GPS coordinates that contains locations and heights of the UAV flight [ 72 ]. The set of pr-programmed GPS waypoints then becomes the path for the UAV to follow [ 74 ]. In

2.4 Microcontrollers used

An FCS has sensor packages for state determination, on-board processors for control and estimation uses, and peripherals for communication links and data transfer. For small UAV applications , small, light weight, and often low power consumption hardware components for the FCS are preferable. Successful UAV control requires sensors used for attitude estimation to have good performance especially in mobile and temperature-varying environments [ 75 ]. Arduino is an open-source electronics platform found in a wide variety of application projects. The board is capable of reading inputs from various sensors and generates required outputs. It comes comes with different processors and board sizes. Arduino Nano was used in [ 76 ] to develop an instrumentation system to collect flight data such as airspeed, orientation, and altitude, e.t.c. The system will then transmit the flight data over a radio frequency module.

2.5 Rotors configuration

There are different types of drones, they can generally be categorised as single rotor helicopter, fixed wing and multi-rotor drones [ 77 , 78 ]. Nowadays researchers endeavors to combine the advantages of fixed wing and multi-rotor drones [ 77 ]. Fixed wing drones are renowned for their endurance whereas helicopters and multirotors have the the advantage of VTOL as well as hovering. Quad-rotor drones are most common and belongs to the multi-copter family [ 77 ]. The quad-rotor unmanned aerial vehicle (UAV) are drones with four rotors typically designed in a cross configuration with two pairs of opposite rotors rotating clockwise and the other rotor pair rotating counter-clockwise to balance the torque. The roll, pitch, yaw and up-thrust actions are controlled by changing the thrusts of the rotors using pulse width modulation (PWM) to give the desired output [ 79 ]. Typically, the structure of a quad-rotor is simple enough, which comprises four rotors attached at the ends of arms under a symmetric frame. The dominating forces and moments acting on the quadrotor are given by rotors, driven with motors, mostly brushless DC motors. There are two basic types of quad-rotor configurations; plus and cross configurations [ 80 ]. The difference between these configurations is where the front of the quadcopter is located. To counteract reactional torque due to propeller rotation, two diagonal pair of motors (1 and 2) rotate anticlockwise while the other pair, motors (3 and 4), rotate clockwise [ 80 ]. In contrast to the plus configuration, for the same desired motion, the cross-style provides higher momentum which can increase the maneuverability performances, each move requires all four blades to vary their rotation speed [ 81 ]. However, the attitude control is basically analogous. Figure  6 shows the quadrotor cross and plus configurations respectively. The red cross depicts direction to the front of the quadrotor, in this case to the right of the pictures in the figure.

Picture reprinted from [ 82 ]

Quadrotor cross and plus Configuaration

The quad-rotors translational motion depends on the tilting of rotor craft platform towards the desired orientation. Hence, it should be noted that the translational and rotational motion are tightly coupled because the change of rotating speed of one rotor causes motion in three degrees of freedom. This is the reason that allows the quad-rotor with six degrees of freedom (DOF) to be controlled by four rotors; therefore the quad-rotor is an under actuated system [ 83 ]. In principle, a quad-rotor is dynamically unstable and therefore proper control is necessary to make it stable. Despite the unstable dynamics, it has good agility. The instability comes from the changing rotor craft parameters and the environmental disturbances such as wind. In addition, the lack of damping and the cross-coupling between degrees of freedom make it very sensitive to disturbances.

2.6 Sensors used

Essential to drone flight is the Inertial Guidance System, this is an electronic system that continuously monitors position, velocity and acceleration by means of incorporated sensor set. It consists of 3-axis rate gyro and 3-axis accelerometer as well as a magnetometer. The IGS readings are filtered to estimate the attitude of the UAV. Recent developments in computing and MEMs technology has seen the decrease in size of IGS sensors [ 84 ]. Thus for small UAVs, a micro IGS can be used to provide a complete set of sensor readings [ 75 ]. Attitude information can also be estimated using infrared (IR) thermopile sensors. They work on the fact that the earth emits more IR than the sky by measuring the heat difference between two sensors on one axis to determine the angle of the UAV. Other sensors such as Vision sensors, either by themselves or combined with inertial measurements sensors can also be used for attitude estimation [ 85 ].

3 Required software components for real-time implementation

Real-time control requires hardware and software systems to be implemented together. Several definitions for real-time systems can be found in the literature. A good definition that we found states that; “a real-time system is one in which the correctness of a result not only depends on the logical correctness of a calculation but also upon the time at which the result is made available” https://www.ibm.com . There is a time requirement, referred to as a deadline, under which the system tasks must be performed. The primary objective is to ensure a timely and deterministic response to events. In the context of drone control, such tasks are normally intended to react to external events in real-time. Thus such real-time tasks are required to keep up with external changes affecting drone performance. Tasks required to meet their deadlines to avoid catastrophic consequences are called hard real-time tasks. When meeting the deadline is desirable but not mandatory, the task is considered soft real-time task [ 86 ].

3.1 Real-time operating systems

A real-time operating system (RTOS) provides services such as multitasking, scheduling, inter-task communication, etc., to facilitate the implementation of real time-time systems [ 87 ]. An RTOS is the key component needed to build a real-time system. Other software pieces such as compilers, linker, debugger and drivers are necessary to interface with system hardware: https://www.ni.com . RTOSs are employed in the development of many applications such as Internet of Things (IoT), automotive , medical suystems, robotics, industrial automation, avionics, and flight control systems [ 88 , 89 ]. RTOSs mainly focus on task predictability and efficiency, therefore have features to support timing constraints for application tasks [ 90 ]. There are several categories of RTOS; small, proprietary kernels as well as real-time extensions to commercial time-sharing operating systems such as Unix and Linux. The kernel is the core, an essential center of the RTOS, or any computer operating system. It is responsible for memory management, processing, and task management, and to interface with hardware and application software. Small, proprietary kernels are often used in embedded applications when very fast and highly predictable execution must be guaranteed. Meeting time constraints requires kernels to be small in size, which reduces RTOS overhead. Kernels must also have a fast context switch, support for multi-tasking, priority-based preemption, provide a bounded execution time for most primitives, and maintain a high-resolution real-time clock [ 90 ].

3.2 Scheduling and prioritisation

Appropriate task scheduling in real-time applications is the basic mechanism adopted by an RTOS to meet time constraints of tasks [ 90 ]. It is the responsibility of the application developer to choose an RTOS that will schedule and execute these tasks to meet their constraints. For a given application, if a set of tasks can be scheduled such that they all meet their deadline, then the tasks are said to schedulable [ 91 ] In priority-driven (PD) scheduling, priorities are assigned to tasks. A task with the closest deadline than any other task is considered the highest priority task [ 92 ]. Embedded time critical applications employ the real-time scheduler to ensure low latency and meeting time constraints. Numeric priorities are assigned to threads constituting tasks, and only the highest priority task is selected to run by the scheduler. A higher priority task can preempt a lower priority task at any point of its execution [ 93 ].

However task priorities can also be dynamic such that a low priority task may temporary elevates its priority to prevent interruption during execution of its critical section. Preemption thresholds can also be set by considering task priority as well as task urgency. Both priority and Urgency are quantified such that it is possible for urgency to take precedence when scheduling tasks [ 86 , 93 ]. Multithreaded parallel programming systems (MPPS) has a characteristic that data is shared among threads. It is important that access to shared data is controlled to avoid associated concurrency errors. As an example, suppose a task alters or updates a global variable, it is necessary for the task to have exclusive access to that variable while it is executing, otherwise concurrent access to the same variable by other tasks will lead to data races, leading to miscompilations.Access of shared data by one task at a time can be achieved by use of Mutual exclusion locks (mutexes) [ 93 ].

3.3 Sensor inputs and feedback control

The common drone platform has a specialised software running on a computer at the ground control station. It allows users to monitor and send control messages to affect drone’s state and actions remotely. Aboard the drone, the autopilot software combines operator inputs and sensor feedback information to directly control UAV actuators [ 94 ]. Sensors onboard the UAV provide feedback data essential to determine the drone’s position and attitude. A stereo camera was proposed for obstacle avoidance as well as velocity estimation in [ 95 ]. In [ 96 ], vision and IMU sensors were employed for automatic navigation and landing of an AR drone quadrotor. A landing marker was positioned in the drone frontal camera’s sight of view, see Fig.  7 . The landing marker position is the desired position \(X_d\) = ( \(x_{d_G}\) , \(y_{d_G}\) , \(z_{d_G}\) ), which corresponds to a height above the landing marker. Position \(X = (x_G, y_G, z_G)\) denotes the drone current location. The position error is then denoted as \(E = X_d - X\) , where  \(E = (e_x,e_y,e_z)\) . The symbols \(e_x,e_y,e_z\) are position errors in directions \(X_G\) , \(Y_G\) , and \(Z_G\) , respectively. The PID controller was applied to the position error in accordance with ( 1 ) and ( 2 ). The drone will land when above the marker, i.e., when the error  \(E =0\) .

Picture reprinted from [ 96 ]

Automatic navigation and landing of an AR drone quadrotor

3.3.1 Localisation using differential global positioning system (DGPS)

Differential global positioning system (DGPS) is extensively used for accurate localisation of drones. The scope of localization and mapping for an agent is the method to locate itself locally, estimate its state, and build a 3D model of its surroundings, by employing among others vision sensors [ 97 ]. Towards this direction, a visual pose-estimation system from multiple cameras on-board a UAV, known as multi-camera parallel tracking and mapping (PTAM), has been presented in [ 98 ]. This solution was based on the monocular PTAM and was able to integrate concepts from the field of multi-camera ego-motion estimation. Additionally, in this work, a novel extrinsic parameter calibration method for the non-overlapping field of view cameras has been proposed.

3.3.2 Mobile phone technology in UAV applications

UAV applications encompass many areas, including, aerial surveillance ,reconnaissance, underground mine rescue operations, and so on [ 25 , 99 ]. Some of these application areas are GPS denied, thus GPS can not provide the location for a UAV. Currently, vision sensors, laser scanners, and the IMU are the most common position sensors used for UAV self-localisation. In some applications, small UAVs are preferred for their cost and high maneuverability. Considering the limited load capacity and the cost of small UAVs, it cannot be equipped with sensors of high precision and large volume [ 100 ].

Micro-electro-mechanical system (MEMS) sensors are therefore preferred alternatives because they are small and cheap. On the other hand, mobile phones contain multi-sensors, multi-core processors, have a small volume, and lightweight. In [ 101 ], Nexus 4 smartphone developed by Google, was used as a flight controller. The phone is equipped with inbuilt MEMS sensors such as accelerometer, gyroscope, magnetometer, global navigation satellite system (GNSS), and barometer. The implementation exclusively used sensors and processors from the smartphone, see Figs.  8 and  9 . Mobile phone usage possibilities in UAV platforms are further elaborated in [ 102 ], where a smart phone is proposed for implementation of drone control algorithms. The usage of smart phones can reduce development time as it it cuts down the need for integration of different drone hardware components, instead the proposed solution uses smart phone inbuilt sensors [ 102 ].

Picture reprinted from [ 101 ]

Schematic diagram for on-board smartphone flight controller using Arduino Mega to interface with the electronic speed controllers (ESCs)

Quadcopter used in [ 101 ] with an on-board smartphone as flight controller

3.3.3 Communication to the ground control station

Communication to the ground control station allows drone pilots to remotely configure mission parameters, such as coordinates to cover during way-point navigation and the action to take at each way-point. Most existing drone platforms have the configuration shown in Fig.  10 . A specialized software runs at a ground-control station (GCS) to let users configure mission parameters. The Ground Control Station is a system made up of software and hardware necessary for UAV remote control. Hardware, such as the joystic, takes the pilot’s command which is transmitted to the drone via radio transmitter. The GCS software collects tellemetry data transmitted from the UAV and displays it the on the GCS user interface [ 103 ]. Communication networking is responsible for the information flow between GCS and UAV on a mission. It needs to be robust against uncertainties in the environment and quickly adapt to changes in the network topology. Communication is not only needed for disseminating observations, tasks, and control information but also needed to coordinate the vehicles more effectively toward a global goal. The goal could be tasks such as areal monitoring or detecting events within the shortest time, which are especially important in disaster situations. Some specific issues that need to be addressed [ 41 ] are connectivity, routing-and-scheduling, communication link models, and data transmission.

Picture reprinted from https://www.google.com/search?q=multirotor+UAV++ground+control+station+images

Platform for drone control from GCS

3.4 Real-time scheduling algorithms

Real-time scheduling aims to complete tasks within specific time constraints and avoiding simultaneous access to resources shared amongst application tasks. To guarantee real-time performance while meeting all timing, precedence and resource usage specifications requires employment of efficient scheduling algorithms supported by accurate schedulability analysis techniques [ 104 ]. Real-time scheduling algorithms can be implemented for uniprocessor or multiprocessor systems [ 105 , 106 , 107 ].

In the context of drone applications, an example could be implementing a flight control system using Arduino Uno or other single processor boards. The Arduino Uno uses the ATMEGA 328P processor (uni-processor), whereas embedded computers like the Rasberry-Pi uses a quad core ARM Cortex-A72 processor (multi-processor). Scheduling algorithms can be broadly divided into two major subsets: offline scheduling and online scheduling algorithms [ 104 ]. In offline scheduling algorithms, task scheduling is carried out before system execution, also known as pre-run time scheduling. The scheduling information is then employed during run-time. The YDS algorithm (named after the author) [ 108 ], which schedules tasks according to earliest deadline first (EDF) precedence [ 109 ] is an example of an offline scheduling algorithm. By contrast, online scheduling algorithms schedule tasks at run-time.An online scheduling algorithm that encoporates event-driven and periodic rolling strategies (EDPRS) is discussed in [ 110 ].

4 Types of controllers

UAV control requires an accurate and robust controller for altitude as well as velocity-and-heading [ 111 ].The altitude controller drives the UAV to fly at the desired altitude, including landing and take-off stages. The heading and velocity control enables UAV to fly through desired waypoints [ 112 ]. To achieve the above control requirements, different control strategies such as Fuzzy Logic,Linear Quadratic Regulator (LQG), Sliding Mode Control (SMC), Proportional Integral Derivative (PID), Neural Network (NN), e.t.c can be used. Robust control systems have been widely developed to address parametric uncertainties and external disturbance. In case of multirotor UAVs uncertainties arising from propeller rotation, blades flapping, change in propeller rotational speed and center of mass position dictates the need for a robust nonlinear controller [ 113 ]. In [ 113 ] robustness as well as compensation forsysten nonlinearities was adresses by combinig the nonlinear sliding mode control (SMC), robust backstepping controller and a nonlinear disturbance observer (NDO). The backstepping controller stabilised translational movement while the SMC controlled the rotational movement of the quadrotor.

The NDO provided all the estimates of disturbances ensuring robustness of the feedback controls. The PID controller was compared with a neural network controller, specifically the direct inverse control neural network (DIC-ANN) in [ 114 ]. The comparison was done in simulation, where both controllers were excited with the same reference altitude reference input and their performances plotted together.The simulation aimed to mimic a quadrotor flight in four phases comprising take-off and climb phase at \(0~<~t<~10~s\) , hovering phase at \(10~<~t~<20~s\) , climb in ramp phase at \(20~<~t<~22.5~s\) , and lastly the final altitude phase at \(22.5~<~t<~50~s\) . The comparison results showed that the DIC-ANN performed better than the PID controller in handling quadrotor altitude dynamics.Also at hovering conditions the DIC-ANN exhibited less steady state error as compared to the PID controller and the transient oscillations damped faster with the DIC-ANN showing that it handles nonlinearities better than the PID controller.

PID controllers are widely used in autopilots due to their ease of implementation, how ever they have limitations when operating in unpredictable and harsh environments. In [ 115 ] the performance of and acuracy of an attitude controller was investigated. The attitude controller is a neural network (NN) based controller trained through reinforcement learning (RL) state of the art algorithms, the Deep Deterministic Policy Gradient (DDPG), Trust Region Pocy Optimisation (TRPO), and the Proximal Policy Optimisation (PPO). The NN controller performance was compared to the performance of a PID controller to determine the appropriacy of NN controller in high precision, time-critical flight control. The contoller performance was evaluated in simulation using GYMFC environment. The results showed that RL can trail accurate attitude attitude controllers, also the controller trained with PPO outperformed a fully tuned PID controller on almost every metric.

The linear quadratic regulation (LQR) optimal control algorithm operates a dynamic system by minimizing a suitable cost function [ 79 ]. When the LQR is used with linear quadratic estimator (LQE) and Kalman filter, it is then referred to as the linear quadratic Gaussian (LQG) The LQG was applied in [ 116 ] for altitude control of a quadrotor micro aerial vehicles (MAVs). Ignoring air resistance, the linearized model for altitude control problem was obtained as ( 3 ), the state space model is represented by ( 4 ) , while the cost function is given by ( 5 ), also refered to in [ 116 ] as the quadratic form creterion. The control objective is to determine the control input U ( t ) to minimise cost function [ 79 ].

The linear Quadratic regulator with and integral with an integral term (LQTI) and a model predictive controller were employed to develop an automatic carrier landing system for a UAV [ 117 ]. The LQTI was applied to the coupled multi-input multi-output (MIMO) UAV dynamic model to reduce steady stare error while the model predictive controller was applied to the final phase landing of the UAV. Automatic carrier landing was performed sequentially by the two controllers. The LQTI controller was applied up to a few seconds before touch down followed by the MPC controller during the final stage of landing. The controller was verified via simulations on HSS Hydro toolbox. Simulation results indicated that the proposed carrier landing system can improve landing accuracy. The performance of the controllers indicted that the LQTI is suitable for calm sea environments while the MPC performs better even in rough sea environments [ 117 ]. Some implementations for UAV control employ the sliding-mode control (SMC) strategy. Sliding-mode control is a nonlinear control method that that utilises a high-frequency switching control signal to the system to command it to slide along a prescribed sliding manifold [ 118 , 119 ]. It encompasses a broad range of varying fields, from pure mathematical problems to application aspects [ 120 ] (Fig. 11 ).

An SMC based fault tolerant control design for underactuated UAVs was implemented on a quadrotor in [ 121 ]. The design approach separated system dynamics into two sub-systems, a fully actuated and an under-actuated subsystem. A Nonsingular Fast Terminal Sliding Mode Controller (NFTSMC) was then designed for the fully actuated subsystem, the Under-actuated Sliding Mode Controller (USSMC) was then derived for the under-actuated subsystem. The controller performance, on a quadrotor platform, demonstrated excellent robustness to actuator faults, disturbances. It had fast convergence and high precision tracking. Herrera et al. designed a sliding-mode controller and applied it in simulation of a quadrotor. They considered a PD sliding surface for vertical take-off and landing. Broad coverage of control algorithms for quadrotors can be found in [ 79 , 122 , 123 , 124 ]. Figures  12 , 13 and 14 shows the PID, LQG, and SMC controllers applied to a quadrotor respectively.

Picture reprinted [ 127 ]

Drone path planning from start 1 and Start 2 to Goal, shortest path taken from both starting points

5 Path planning

Missions of UAVs usually involve travelling from some initial point to a goal point [ 125 , 126 ]. A mission requires generating a path for the UAV to follow. Path planning is one of the main aspects of autonomous navigation [ 127 ]. The path planning problem is to produce a path or set of waypoints for the drone to follow while taking into account the environmental and physical constraints of the drone in order to achieve a collision free flight [ 128 , 129 ]. This is obstacle avoidance while executing the the UAV’s mission. Figure  11 depicts drone paths from start to goal position for two drones launched from different locations, each calculating its best path to reach the goal position.

In the literature pertinent to UAV path planning, several algorithms for measuring distances to obstacles and calculations of the drone’s path are suggested [ 130 , 131 , 132 ]. An optimal flight path planning mechanism to determine the best path of the UAV was developed in [ 133 ]. Consideration of environmental information such as geographical topology,location dependent wireless communication channel statistics and flight risk, sensor node deployment and worth of sensing information for different sensor types was made. The implementation aimed at determining the best path to maximise the value of gathered sensing information as well as to minimise flying time, energy consumption, and UAV operational risks. In [ 127 ], 3D propagation approximate Euclidean distance transformation algorithm was formulated to achieve safe path planning by calculating a 3D buffer around the obstacles. The algorithm prevents the drone from flying too close to obstacles by setting the minimal distance from obstacles according to the size of the drone. The algorithm is also used for drone path planning in [ 127 ]. It is worth noting that current techniques for UAVs path planning are application dependent. Different applications require different path-planning approaches.

A method to enhance massive unmanned aerial vehicles for mission critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting) is investigated in [ 134 ]. The method aims to achieve UAV fast travel while avoiding inter-UAV collision while executing their mission. The path planning problem is tackled by exploiting a mean-field game (MFG) theoretic control method. The method requires UAV state exchange only once at launch, thereafter each UAV controls its acceleration by locally solving two partial differential equations, the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Due to high computational burden posed by solving the partial differential equations, two machine learning models were used to approximate the solutions of the HJB and the FPK. The performance of the proposed method was validated on simulation, showing that the mean-field game method guarantees UAV collision avoidance. Also for the proposed approach, the effectiveness of the mean field game method is determined by the level of the HJB and FPK training.

Block diagram of PID controller applied to a quadrotor [ 79 ]

Block diagram of LQG controller applied to a quadrotor [ 79 ]

Block diagram of an SMC controller applied to a quadrotor [ 79 ]

6 UAV real-time control implementation

In order to implement real-time control for UAVs, tasks have to be defined. An RTOS is required for tasks scheduling, inter-task communication, and management of available resources such memory, and power consumption [ 135 , 136 , 137 ]. Each task is allocated a memory space, called a stack, in the microprocessor. This is enabled by the RTOS kernel’s support for multi-threading [ 138 , 139 ]. Scheduling and prioritisation of tasks, as well as the update frequency of the sensors providing essential data for task execution, ensure that application time constraints are met [ 140 ]. In [ 141 ], an embedded RTOS (RT-Thread) is applied to a quadcopter to address problems of real-time response, heavy workload and difficulty in control. Practical tests in this work indicated that quadcopter control system based on RT-Thread responded real-timely, and ensured smooth flight with a PID control algorithm.

The application tasks defined in this work are attitude information acquisition, attitude information fusion, and PID control. The latter is for quadcopter control. The application task is developed on top of RT-Thread RTOS running on STM32F407VGT6 microprocessor. The processor is equipped with high-performance ARM Cortex-M4 core with maximum system frequency of 168MHz, an FPU (floating-point unit), 1 Mbyte of flash, and 192 Kbytes of SRAM. It has peripherals such as ADC, SPI, USART, controller area network (CAN) bus, DMA, etc. High operating frequencies and high-speed memory provide high computational power to enable quadcopter complex calculations to be performed. Also additional peripherals reduce the need for external IC and reduce computational burden from the microprocessor. The implementation in [ 142 ] uses a dual processor configuration.

One processor is used for telemetry and another for control of a custom quadcopter used as a test-bed. The telemetry processor executes software tasks such as communicating reconfiguration and monitoring data with the GCS, data collection from sensors, and wirelessly transmitting data to the GCS. The tasks are managed by \(\mu \) C/OS-II™, an RTOS. The control processor runs the PID controller algorithm for the quadcopter stabilization and navigation. This task was achieved through several tasks allocated to the control processor. Tasks include reading GPS, compass, IMU, and altitude sensor data received from the telemetry processor. Other tasks include implementation of the roll, pitch, yaw, and altitude PID control loops, and communicating reconfiguration and monitoring data with the telemetry processor via CAN bus. Figure  15 shows the PID controllers used in the implementation.

Picture reprinted from [ 142 ]

PID control loops implemented by the control processor

7 Essential components for UAV real-time applications

7.1 real-time operating system (rtos).

The literature pertaining to real-time implementation of drone control systems is relatively limited, and the number of reported studies on UAV scheduling has been minimal [ 143 ]. The main feature of real-time implementation in drones control is that an embedded RTOS, also referred to as UAV operation system in some literature, is required [ 67 , 144 ]. The RTOS provides a real-time kernel on which the control program running on a micro-controller is implemented. The real-time kernel guarantees application tasks meet their time constraints by employing the UAV scheduling system [ 143 ]. Consequently, a Real-Time Operating System (RTOS) that provides operating environments for various mission services on UAVs is crucial [ 145 ]. The commonly used RTOS for UAVs is FreeRTOS, and an empirical study of this RTOS was conducted in [ 145 ]. The study looked at aspects such as functionality changes during the evolution of FreeRTOS. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0 were considered.

7.2 Microcontroller

The microcontroller is the UAV onboard processing unit for UAV computations and UAV state monitoring [ 146 , 147 ]. It is selected such that it matches application task requirements. Considerations such as computational speeds and communication with onboard sensors have to be made. Palossi et al. [ 146 ] extended the hardware and software of a 27 grams nano-size, commercial off-the-shelf (COTS) quadrotor, the crazyflie 2.0, to achieve object tracking capability. The quadrotor platform consists the STM32F405 microcontroller as the main onboard processing unit, the Nordic nRF51 module for wireless communication. The STM32 is an ARM Cortex-M4F microcontroller, operating at 168MHz. The on-board sensing is performed by a 9-axis IMU, the MPU-9250 with a gyroscope, an accelerometer, a magnetometer, and an ST LPS25H pressure sensor with a typical accuracy of \(\pm 1\) meter. The vehicle is powered by a 240mAh Li-Po battery.

Reprinted from [ 150 ]

Sensors connected to microcontroller

7.3 Sensors and actuators

In UAV applications several sensors and actuators are connected to the microprocessor for UAV control. Table 1 highlights the vital components for real-time implementation of UAV control, the table also lists various sensors used. Figure  16 shows the UAV onboard sensors used in a fire fighting remote-sensing system in [ 150 ]. Various sensors as well as the overall connection network is depicted.

8 Conclusion

Real-time control of drones requires an embedded RTOS for implementation. The RTOS provides facilities such as multi-threading, scheduling and priority assignment. These support real-time response of the drone control system to feedback from GPS and IMU. The drone control system subsequently apply the corresponding motor speeds to achieve the desired drone’s movements. Multitasking enables tasks, such as position and orientation feedback, path-planning, and control implementation to run in parallel. This facilitates real-time response of the drone. Tasks may need results from other tasks for their computations. Scheduling and prioritisation of tasks ensures that at any point in time critical tasks are given computational resources by the microprocessor. For example obstacle avoidance is the highest priority task to ensure that the drone does not collide with other drones as well as other obstacles.

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Acknowledgements

The authors would like to acknowledge the funding support on this work from the Botswana International University of Science and Technology (BIUST) Drones Project with project number P00015. The authors would also like to thank Boyce Segweni for his help in the preparation of this manuscript.

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Kangunde, V., Jamisola, R.S. & Theophilus, E.K. A review on drones controlled in real-time. Int. J. Dynam. Control 9 , 1832–1846 (2021). https://doi.org/10.1007/s40435-020-00737-5

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Literature Review on Drones Used in the Surveillance Field

The current study reviews the available literature about UAVs employed in the surveillance field for indoor and outdoor spaces. At first, we differentiated between HTA and LTA. After a first analysis, we compared the two categories, and we proposed active solutions. In our opinion, it would be possible to put them into practice by implementing a system which would operate both copter drones and blimps. These, in fact, present advantages and disadvantages which overcome one another, giving the possibility of such dual systems. This study is developed inside a broader analysis on how to provide the right level of security of an automated port that would rely on an autonomous security system.

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Ethical Considerations Associated with “Humanitarian Drones”: A Scoping Literature Review

1 Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland

2 Digital Society Initiative, University of Zurich, Zurich, Switzerland

Markus Christen

Matthew hunt.

3 School of Physical and Occupational Therapy, McGill University, Montréal, Canada

4 Centre for Interdisciplinary Research in Rehabilitation, Montréal, Canada

Associated Data

Not Applicable.

The use of drones (or unmanned aerial vehicles, UVAs) in humanitarian action has emerged rapidly in the last decade and continues to expand. These so-called ‘humanitarian drones’ represent the first wave of robotics applied in the humanitarian and development contexts, providing critical information through mapping of crisis-affected areas and timely delivery of aid supplies to populations in need. Alongside these emergent uses of drones in the aid sector, debates have arisen about potential risks and challenges, presenting diverse perspectives on the ethical, legal, and social implications of humanitarian drones. Guided by the methodology introduced by Arksey and O’Malley, this scoping review offers an assessment of the ethical considerations discussed in the academic and gray literature based on a screening of 1,188 articles, from which we selected and analyzed 47 articles. In particular, we used a hybrid approach of qualitative content analysis, along with quantitative landscape mapping, to inductively develop a typology of ethical considerations associated with humanitarian drones. The results yielded 11 key areas of concern: (1) minimizing harm, (2) maximizing welfare, (3) substantive justice, (4) procedural justice, (5) respect for individuals, (6) respect for communities, (7) regulatory gaps, (8) regulatory dysfunction, (9) perceptions of humanitarian aid and organizations, (10) relations between humanitarian organizations and industry, and (11) the identity of humanitarian aid providers and organizations. Our findings illuminate topics that have been the focus of extensive attention (such as minimizing risks of harm and protecting privacy), traces the evolution of this discussion over time (i.e., an initial focus on mapping drones and the distinction of humanitarian from military use, toward the ethics of cargo drones carrying healthcare supplies and samples), and points to areas that have received less consideration (e.g., whether sustainability and shared benefits will be compromised if private companies’ interest in humanitarian drones wanes once new markets open up). The review can thus help to situate and guide further analysis of drone use in humanitarian settings.

Introduction

Globally, aid agencies widely use emerging technologies in humanitarian, development, and healthcare settings (Hunt et al., 2016 ; van Wynsberghe et al., 2018 ; Wang, 2020 ; Wang, 2021a ). One prominent type of technology is unmanned aerial vehicles (UAVs), also known as drones, 1 which represent the first wave of aerial robotics applied in humanitarian projects (Mesmar et al., 2016 ). They have been put to multiple uses across different humanitarian crises, including: damage inspection during the 2010 earthquake in Haiti, rescue logistics following Typhoon Haiyan in the Philippines in 2013, medical equipment delivery during the 2014 Ebola outbreak in West Africa, and topographic mapping in the aftermath of the 2015 Nepal earthquake.

Technological innovations in crisis response intersect with moral values, norms, and commitments, and may challenge humanitarian principles (Sandvik & Lohne, 2013 , 2014 ; Sandvik, 2015 ). Hence, analysis of ethical challenges associated with humanitarian innovation, including drones, is required for understanding what is at stake. Our own research on the use of drones for humanitarian and development purposes (Wang, 2020 ; Wang, 2021a ) indicates that ethical considerations associated with the humanitarian use of drones vary and extend beyond the “usual suspects” such as privacy, consent, and safety. In this work, we present a scoping review (Arksey & O’Malley, 2005 ; Levac et al., 2010 ) of the academic and gray literature to provide a comprehensive overview of how ethical considerations are discussed in the literature related to using drones in the humanitarian and development contexts.

We aim to inform the ongoing debate by mapping prevailing perspectives and identifying knowledge gaps with respect to ethical considerations in the humanitarian use of drones (Sandvik & Jumbert, 2016 ). Within this context, we are especially interested in identifying salient ethical considerations that have received less attention in the ongoing debate. More specifically, our objective is to assess how ethical considerations associated with the humanitarian use of drones are discussed in the academic and gray literature. To clarify the meaning of the “humanitarian use” of drones, we applied two criteria: (1) the use of drones is carried out through voluntary or solicited humanitarian assistance from the global aid sector; and (2) drones are operated by, or in collaboration with, humanitarian organizations to support aid provision.

We followed the methodology introduced by Arksey and O’Malley ( 2005 ). We developed our review protocol with support from two librarians with expertise related to bioethics and engineering. Prior to the final data collection, we pilot-tested and calibrated the protocol to ensure its applicability.

Research Question and Search Terms

The question guiding our scoping review was, “What is known about the ethical considerations associated with the humanitarian use of drones?” The three central notions in our review, therefore, are “drones,” “humanitarian use,” and “ethical considerations,” and are defined as follows:

The term “ drones ” refers to UAVs that are, in most cases, electrically powered aircraft of small size with limited flight range and duration. They fly above the ground (semi-)autonomously within or beyond a pilot’s visual line of sight (Floreano & Wood, 2015 ). There are various types of drones in terms of mechanical structures, such as fixed-wing, rotary-wing, and multi-copters (Christen et al., 2018 ). Most drones used in the humanitarian context are fixed-wing or multi-copters below 30 kg. Generally, such small drones have a number of remarkable socio-economic impacts. For instance, images collected by drones can fill a gap between expensive, weather-dependent, and low-resolution images provided by satellites, or car-based images limited to human-level perspectives and the accessibility of roads (Floreano & Wood, 2015 ). Thanks to their high versality and easy maneuverability, small drones have been rapidly deployed and steadily scaled up on a wide spectrum of civilian applications over the last decade (Wang, 2021a , 2021b ).

By “ humanitarian use ,” we refer to the deployment of drones by humanitarian actors 2 in three situations: (1) acute humanitarian crisis settings, including relief efforts during emergencies arising from events such as natural disasters, epidemic outbreaks, or mass population displacement 3 ; (2) immediate post-crisis settings , including post-disaster recovery and reconstruction efforts for populations affected by an ongoing or recent humanitarian crisis; and (3) long-term crisis-resilience or development settings , including activities related to medical commodity delivery or health supply chain management after a crisis to strengthen resilience and mitigate risks. As such, we excluded both the use of surveillance drones in armed conflicts (e.g., for detecting war crimes), and other types of civilian use of drones (e.g., for recreational, journalistic, agricultural, construction, or public safety purposes) from our review. The exclusion criteria in Table ​ Table2 2 clarify the differences between the humanitarian use of drones and military or civilian uses.

Exclusion criteria for article screening

Finally, with respect to “ ethical considerations ,” we concentrate on the ethical ramifications of drones used in the above-specified settings. We retained articles if they included implicit or explicit discussions about the humanitarian use of drones as either being consistent with, or infringing upon, moral values, responsibilities, or obligations considered important by the authors.

Identifying Relevant Studies

Aligned with this general understanding of the three central notions, we tested different combinations of primary search terms, starting with a set of more extensive keywords. We then included secondary and tertiary search terms to assess their impact on the search results, using the approach of the systematic inclusion of single terms. Table ​ Table1 1 shows the resulting search strings using the “AND” function. We adapted the use of these strings to the specificities of the selected databases.

Search strings used in the database searches

We removed the terms “remotely piloted aircraft” (RPA) and “remotely-piloted aircraft system” (RPAS) from the search string based on the testing results for a number of reasons, including the fact that they mostly yielded military applications of drones, which we deliberately excluded from our review (see Criterion D in Table 2).

To keep the literature search meaningful and manageable, we designed a set of parameters to help refine the search (Gough et al., 2012 ). We searched for articles, books and book chapters, and conference proceedings, as well as gray literature including policy documents, reviews, blog posts, and media reports of a minimal size. We excluded abstracts related solely to conference presentations, book reviews, PhD dissertations, and brief news releases. We calibrated the exclusion criteria in Table ​ Table2 2 through pilot testing.

We only included publications in English, primarily because it is the only common language in which all researchers involved are proficient. We set the search to begin in 2000 since the first use of drones for disaster relief purposes was reported in 2005 during the response to Hurricane Katrina (Greenwood et al., 2020 ). Further, existing literature reviews on drones, as well as our own preliminary database search, indicated that almost no papers referring to drones were published before 2000 (Christen et al., 2018 ).

We used a multi-stage screening strategy involving both inductive screening via search engine and associated websites, as well as deductive identification of relevant articles in academic databases. We searched three academic databases: Google Scholar , 4 Scopus , 5 and Web of Science . 6 Our pilot test pointed to the need to adapt the search strategy in Google Scholar due to the high volume of search results, a consequence of the fact that the search logic in Google Scholar is full-text and, in addition, reveals citations of relevant texts.

In order to identify gray literature, we performed an exploratory search using the Google search engine and targeted website searches on 31 websites of relevant humanitarian organizations. In addition, existing resources known to the authors, as well as ad hoc advice from our project partners, served as a further source to pinpoint relevant publications. Lastly, we subjected all papers included in the final dataset to snowballing (i.e., we screened the reference sections of the papers to identify additional relevant articles).

Selection of Articles

We conducted the search, selection, and snowballing between April and July 2020. Figure  1 presents the process using a diagram modified from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009 ). We included the full list of retained articles as supplementary information.

PRISMA flow chart outlining the search and selection process

For the database searches of academic literature in Scopus and Web of Science , we merged the results and removed duplicates. In Google Scholar , we employed a refined search string that excluded the terms “conflict” and “war.” Additionally, we merged the first 100 entries in Google Scholar (sorted by “relevance”) yielded by the original search string with this set. We then merged all three sets and removed duplicates.

For the exploratory Google searches to identify gray literature , the first and second authors checked the first 100 search results independently (corresponding to Google #1 and Google #2 in Fig.  1 ). We merged and discussed findings where only one person had chosen the entry. The first author then performed targeted website searching, and the results were added to the final dataset. All three authors contributed to the inclusion of relevant sentinel articles sourced from existing knowledge (corresponding to Targeted Search and Known Resources in Fig.  1 ).

Using the exclusion criteria outlined in Table ​ Table2, 2 , the first and second authors independently conducted a first round of screening of all articles (from both the academic and gray literature) retained based on title and abstract. The aim of the screening was to classify each paper either as eligible for full-text screening, or to attribute it to one of the eight exclusion criteria. We discussed cases of conflicting assessments until reaching a consensus. In the first round of screening, the first and second author identified and removed an additional 20 duplicates.

The first author performed the second round of screening on the full texts of all publications that passed the first round. The first author excluded articles if they were unavailable in full-text, or if the full text was not in English, except for the abstract (see the technical exclusion criteria I, J, and K in Table ​ Table2). 2 ). Finally, the first author performed snowballing on all articles included after the screenings in an iterative manner. The second author provided a second opinion whenever there was uncertainty about whether to include an article in the final set, and/or regarding the exclusion criteria.

Charting and Analyzing the Data

We extracted data from the final selection of articles using a data extraction table, organized around the following headings:

• Bibliometric information : publication date, author affiliation, and sources of articles.
• Contextual information : drone use case, the type of crisis, the location of drone use, and the humanitarian organization(s) involved.
• Substantive information : theories used related to ethics, and the conclusions drawn by the authors.

To identify ethical considerations, we employed a conventional content analysis approach whereby researchers develop inductive categorizations of the matters of concern, as opposed to applying pre-conceived notions (Hsieh & Shannon, 2005 ). We organized the content analysis based on an inductive, bottom-up identification of topical categories. To be comprehensive, we took an inclusive approach to interpreting “ethical considerations,” taking into account references to legal and social aspects that have a close link to ethics (as presented by the respective authors of the selected articles). To generate the categories, the first and second authors independently sketched and clustered into topics a list of descriptors taken from the text. They compared and merged the resulting classifications into a single typology. The third author then provided feedback.

Consultation

Finally, the typology was discussed during two expert consultation workshops held on October 15, 2020. The participants included scholars with expertise in humanitarian studies, sociology, ethics, anthropology, and law, as well as practitioners from international humanitarian organizations and the drone industry. We incorporated feedback obtained from this consultation process into the revision of the typology.

Figure  1 above indicates that a total of 1,188 articles were subjected to the first round of screening, yielding 124 papers to be analyzed in full-text. At the end of the selection process, we retained 47 articles as relevant to our study. Figure  2 below depicts the frequency of exclusion criteria, reflecting the broader scope of our search and selection process.

The distribution of exclusion criteria

Bibliometric Information

The selected articles span from 2012 to 2020. We observed a relatively low rate of publication in the first two years with one or two articles released annually, then a steady rise between 2014 and 2018, when 26 articles were published, with an average of five articles per year. Notably, there is a significant rise in 2019 when 12 articles were published (including one article by the first author), reaching the peak of knowledge production in the review period. In 2020, five articles had already been published (including one related to the COVID-19 pandemic) by the cut-off date of our database search on April 24th. This trend of a growing discussion around the ethics of humanitarian drones indicates an expanding awareness of ethics among scholars and practitioners working in the field, echoing the rise of the so-called “good drone” in the aid sector in recent years (Sandvik & Jumbert, 2016 ).

While authors from six continents are represented in our dataset, a high concentration of knowledge production is seen in Europe and North America, with 21 and 18 articles published from each region, respectively. The remainder includes four from Asia (China, India, Malaysia, Singapore), two from Africa (Madagascar, Malawi), and one each from Oceania (Australia) and South America (Brazil). Among the 21 articles from Europe, the first authors of seven articles are based in Switzerland, all from outside academia; the first authors of five articles are based in Norway, all affiliated with the same research institution. Of the 18 articles from North America, three are from Canada and 15 from the US, with the first authors of nine articles affiliated with academia and six with non-academic organizations. While the majority of our dataset comprises scholarly articles produced by authors affiliated with academic institutions, the authors of 16 articles were affiliated with organizations based in the UK, the US, Canada, Switzerland, and Malawi, including three United Nations (UN) organizations (International Civil Aviation Organization [ICAO], the UN Office for the Coordination of Humanitarian Affairs [OCHA], the UN Children’s Emergency Fund [UNICEF]), one governmental organization (US Agency for International Development [USAID]), four non-governmental organizations (NGOs) (Swiss Foundation for Mine Action [FSD], FHI 360, WeRobotics, Sentinel Project), and three policy think tanks or similar organizations (Conflict Dynamics International, New America, Trilateral Research).

The collected articles were published in journals linked to six areas of study and practice: 16 articles from humanitarian/development/aid, nine from international affairs/public policy, eight from medicine/public-health, seven from engineering, five from ethics, and two from aviation. While the articles were predominantly published in social sciences and humanities journals, amounting to 30 in total, technical areas (ranging from engineering and aviation to medicine) are also important disciplinary areas. Additionally, five articles are from ethics-oriented journals, of which four are at the intersection of ethics and engineering/robotics, and one between ethics and international affairs.

Contextual Information

Regarding the type of drone use case , there are 12 articles about imagery or mapping drones, ten about payload or cargo drones, six about both uses, and 19 that are unspecified. Articles referring to mapping drones were mostly produced around the period of 2014–2016, with a relatively even distribution throughout these years. In contrast, articles referring to cargo drones are mostly not seen until 2017 with a peak in 2019, of which 70% are related to healthcare or health emergencies. Before 2017, there were only four articles (one/year in 2012, 2013, 2015, and 2016, respectively) about cargo drones, and all from scholars who cautioned about the subtle dynamics between military drones and humanitarian or disaster drones, especially when used in regions previously affected by armed conflict.

In terms of type of crisis , 27 articles are unspecified; of the 20 articles in which a crisis can be identified, our dataset shows three main types: (1) medical emergencies, (2) healthcare, and (3) natural disasters, each representing 1/3 of the articles. One interesting use of drones in emergency situations is that of medical emergencies, such as snakebites or out-of-hospital cardiac arrest, where drones can be used to deliver anti-venom (AV) or an automated external defibrillator (AED). Moreover, epidemic or pandemic outbreaks, such as Ebola and COVID-19, present widescale medical emergencies, where drones have been deployed to facilitate relief work. However, since our primary focus is on the aid sector, which provides humanitarian relief work or development aid assistance, the medical emergency use of drones (related to AV and AEDs) is at the periphery of our research (although aid organizations do cover health emergencies during public health crises). Additionally, all seven articles about drones in natural disaster settings involve mapping drones, and all six articles about drones used in healthcare involve cargo drones.

With respect to location of drone use , 36 articles refer to unspecified or various locations; of the 11 articles where locations can be identified, five refer to Africa, two to the Americas, one to Europe, and one each to Asia and Australia. Interestingly, all four articles in which drones were used in the Americas and Europe were published in 2019 and 2020, and the two articles where drones were used in Oceania were published in 2017 and 2019, whereas the five articles in which drones were used in Africa were published somewhat evenly from 2014 onward. To some extent, this pattern reflects a connection between the location and timeline of drone activities; Africa has been an area of high activity for drones from the start, while Oceania, Europe, and the Americas have seen increased drone activity related to broader humanitarian use more recently.

Finally, all but 14 articles identified specific humanitarian organizations that used drones in different capacities, such as technical assistance or actual deployment and operations. Among the identified organizations, those mentioned more often than others were FSD, Médecins Sans Frontières (MSF, also known as Doctors Without Borders), the International Committee of the Red Cross (ICRC), OCHA, UNICEF, USAID, the World Food Programme (WFP), the World Health Organization (WHO), and the World Bank.

Substantive Information

An overwhelming majority of the articles do not include discussions of specific theoretical approaches. Only eight articles refer to theories (including one article that mentions two theoretical approaches). Two articles are based on the “value sensitive design” (VSD) framework, and two articles show influence of science and technology studies (STS) theories, such as the “actor network theory” (ANT) and the “diffusion of innovations.” Three articles cite humanitarian principles, and two articles refer to theories of relational ethics and robot/artificial intelligence (AI) ethics. Further, these eight articles are not just from academic sources, but also from a UN organization (UNICEF) and a think tank (Conflict Dynamics International). They also represent diverse disciplines and continents, cover all use cases and crisis types, and employ different methodological approaches.

Regarding the ethical theories mentioned in these articles, some scholars (e.g., Cawthorne, van Wynsberghe & Comes) cited bioethics principles, including beneficence, non-maleficence, autonomy, justice, and dignity. Others (e.g., Bellievau, Meiches, Tatsidou et al.) made reference to humanitarian principles, including humanity, neutrality, impartiality, and independence. Some (e.g., Kerasidou et al., Matus & Ruytenbeek, Sandvik) also addressed specific principles, such as informed consent, do no harm, and the equitable sharing of benefits (of commercial drone use). In addition, a few scholars referred to relational ethics (Matus & Ruytenbeek), robot/AI ethics principles (van Wynsberghe & Comes), and engineering ethics principles (e.g., Cawthorne & Cenci). Overall, there is a lack of theoretical grounding of the ethical concerns discussed in most articles .

Summary of Ethical Considerations

In sum, by using the conventional content analysis explained above and taking contextual and substantive information into account, we developed an initial typology inductively, which was then discussed and refined during the two expert consultation workshops. The revised typology suggests an overlap with the general ethical, legal, and social implications (ELSI) framework that is widely used for technology assessment work. In addition to the ethical considerations that emerged from the selected articles, we included legal considerations with respect mostly to regulation and governance, and social considerations, with a strong focus on the broader societal impacts of humanitarian innovation.

We acknowledge that this classification does not capture all subtleties associated with the richness and depth of ethical values such as “justice” or “respect,” and thus cannot be considered comprehensive with regard to all aspects discussed in the selected articles, as well as during the consultation workshops. Nevertheless, we consider it appropriate and sufficient to map out where the relevant issues lie. The first and second author independently identified and evaluated the tertiary-level focuses. We discussed cases with conflicting classifications until reaching a consensus.

A more detailed analysis of the ethical considerations, outlined in Table ​ Table3, 3 , reveals the following major trends (indicated in Fig.  3 ). Overall, regarding ethical considerations , “harm” seems most prominent, followed by “justice” and “respect.” Regarding “harm,” discussions center primarily on ensuring physical safety, in addition to promoting public welfare and individual benefits for affected populations. With respect to “justice,” issues tied to procedural justice are addressed less often compared to substantive justice, whereby the cost-effectiveness of drone operations and stakeholders’ general responsibility are stressed. Concerning “respect,” the community aspect is mentioned in relation to both acceptance and engagement, while the individual aspect sheds light on privacy and information security. As for legal considerations , the lack of airspace regulations appears to be a concern, alongside ambiguous or inadequate regulatory processes (e.g., bureaucracy hindering drone use). Finally, in terms of social considerations , public perception seems to be notably represented in the literature, alongside relations between humanitarian organizations and the drone industry, as well as the identity of humanitarian aid providers and aid organizations. In particular, issues linked to the effectiveness and accountability of humanitarian aid, and the reputational risks of the military origins of drones, appear to be causing the most concern.

ELSI classification for analyzing the humanitarian use of drones

The distribution of ethical, legal, and social considerations

The growing trend of increased publications on humanitarian drone use and ethics reflects the increased emphasis on humanitarian innovation (Sandvik et al., 2017 ; Scott-Smith, 2016 ) and its ethical implications (Betts & Bloom, 2014 ; Sheather et al., 2016 ), as well as the broader context of rising use of drone technology across diverse sectors (Eichleay et al., 2016 ; OCHA, 2014; Soesilo et al., 2016 ; Wang, 2019 , 2020 , 2021a , 2021b ). The bibliometric analysis of the collected articles indicated a strong growth in articles published across the review’s timespan, with the greatest number of articles released in 2019, the last complete year included. Based on this trend, and as suggested by publications early in 2020, we anticipate that this trend will continue, especially as interest in drone use appears to be strong in the humanitarian sector (Foundation for Responsible Martins, Lavallée, & Silkoset 2020 ; Knoblauch et al., 2019 ; Robotics, 2018; Tatsidou et al., 2019 ; USAID, 2017a , 2017b ; Wang, 2020 , 2021a ).

A further feature that may propel this trend is that evolving regulatory environments in many countries are becoming increasingly receptive to drone use (Mauluka, 2019 ; Washington, 2018 ). Regulatory approaches to drones are also likely to have contributed to another finding from the review: where the location of drone use was specified in the articles, it was most often located in Africa. Although the number of articles identifying a specific location is relatively small, this finding is suggestive of the unfurling regulatory dynamics between regions. Compared to more recent and gradual relaxation of the regulatory atmosphere in some settings in Europe and North America, many African countries have presented more regulatory openness for drone use, as well as the development of initiatives, such as a drone testing corridor in Malawi in collaboration with UNICEF, facilitating logistical arrangements for other organizations for flight testing in a safe environment (UNICEF, 2017 ). This reality points to an additional concern related to the use of drones in humanitarian settings: They may offer an ideal testing environment for commercial technologies that would not be possible in other settings, and which will make possible their deployment elsewhere in non-emergency and more lucrative markets (Sandvik et al., 2017 ). An open question remains about whether the interest of private companies in humanitarian drones will wane once these new markets open up, with implications for the sustainability of initiatives and the shared benefits of these programs.

As reflected in our review, the area of focus for discussing humanitarian drone use has also evolved. At the start of our review period, more discussion was occurring around the use of mapping drones, a technology that was beginning to be more widely applied and commercialized in the early 2010s. In the middle period of our review, discussion of cargo drones became more prominent. Even within discourse on cargo drones, there was a shift of emphasis over time: The focus moved from ethical concerns over the intersection of military and humanitarian drone usage to a focus on the implications (e.g., privacy, risk of harm) of using cargo drones for healthcare applications, such as delivering medical supplies or transporting biological samples. The latter development is linked to the emergence of the notion and regime of the “good drone.” Several scholars of humanitarian technologies have critically appraised the good drone paradigm (Choi-Fitzpatrick, 2014 ; Raymond, 2012 ; Sandvik, 2015 ), who have expressed concerns associated with this shift, including the underlying motivations that have propelled it forward, and especially concerns for what this reframing might obscure, including the commercial, public surveillance, and perhaps military implications of humanitarian drone technology development. It is possible that the ethical considerations of cargo drone use will evolve over time if there is a shift away from humanitarian organizations partnering with small companies to develop humanitarian drone delivery programs, and toward the use of commercial drone delivery as these services become available in different locales. While similar technologies might be used, such a transition would reshape issues related to data management and security, control, and responsibility.

With the increasing output of articles on this topic, authorship of these sources has remained primarily with individuals based in Europe and North America, and who are commonly affiliated with academia, large international NGOs and, to a lesser extent, think tanks. This distribution is likely broadly reflective of authorship in the realm of humanitarian innovation, and humanitarian action more generally. For example, in their review of research related to disasters in low- and middle-income countries, Roy, Thakkar and Shah found that over 75% of the authors of these papers were from high-income nations (2011). Authorship patterns and publication sources also show the cross-disciplinary nature of this topic. Publications appear in a wide range of venues, such as academic journals in fields like humanitarian studies, engineering, healthcare, and ethics. Also consistent with these intersections, and with broader interest in ELSI related to emergent technologies, there appears to be particular engagement with this topic from social scientists and ethicists working in a range of domains, including STS, engineering and science ethics, and humanitarian studies. Interdisciplinary perspectives provide novel insights into the debate; for instance, van Wynsberghe and Comes ( 2020 ) proposed that analysis based on humanitarian principles (mostly answering questions about aid provision) should be complemented by a technology-oriented approach (namely robot ethics) to enrich discussions on humanitarian drone ethics. However, we noted a relative paucity of the use of theories to guide exploration of ethical considerations related to humanitarian drones. In future research on this topic, closer engagement with diverse theoretical frameworks and approaches could help to enrich the ethical investigation of these technologies (Sherman, 1999 ).

Through an inductive process, we identified a set of ethical considerations related to humanitarian drone use, with three main areas of emphasis: (1) optimizing harm-benefit trade-offs, (2) upholding justice, and (3) respecting autonomy. In addition, we identified considerations tied to internal and external perceptions of humanitarians and humanitarian action, as well as for regulatory and legal aspects of drone use. Broadly, the three main ethical considerations that we identified reflect core ethical concerns pinpointed in spheres such as research ethics (Belmont, 1979). Each of the three ethical considerations has two or three dimensions, which allowed us to further clarify areas of focus in the literature. For example, under the category of justice, we distinguished substantive issues of distributive and social justice from concerns related to procedural justice, such as transparency and accountability in decision-making. Across all these categories, there is a strong focus on community-level considerations, as well as for individuals. Such distinctions are reflected, for example, in discussions on harms and benefits, where either may accrue at an individual level (e.g., privacy concerns) or at a collective level (e.g., the shared benefits of mapping a landslide area). These two levels are most prominent in relation to the demonstration of respect toward individuals and communities. Respect for individuals may manifest in practices such as seeking a person’s consent, whereas community engagement activities indicate respect for the broader group of people affected by the use of a drone in a particular locality.

It is interesting to consider our typology of ethical considerations in relation to articulations of principles for humanitarian innovation. An influential example of humanitarian innovation principles is those that were developed during a joint Humanitarian Innovation Project and World Humanitarian Summit (HIP-WHS) Oxford Workshop in 2015. The principles include: being guided by a humanitarian purpose, being committed to non-maleficence (do no harm), justice (in terms of equity and fairness regarding benefits, costs and risks), accountability, the provider/recipient relationship being the primary relationship of concern, upholding autonomy (expressed as promoting the rights, dignity, and capabilities of the recipient population), and experimentation (i.e., that piloting and trials be carried out in line with international research ethics standards). These seven principles correspond well with the ethical considerations identified in our review. Concerns for maximizing benefit and minimizing risk are reflected in the emphasis on humanitarian purpose and a “do no harm” approach. The Oxford principles include both justice as a substantive concern for the distribution of benefits, risks, and harm, as well as the procedural justice concern for accountability. Likewise, respect is stressed in terms of highlighting relationships between providers and recipients of assistance, and the expectation that all innovations be aimed at advancing the rights, dignity, and capabilities of the populations affected by crises. Finally, attention to experimentation and norms of research ethics can be linked to the legal/regulatory dimensions of drone usage in that both point to questions of due oversight and structures of governance.

Across the collected articles, legal considerations mostly emerged in relation to the regulatory and governance aspects, including a lack of specific types of regulation (most prominently concerning safety and airspace management), or inadequate processes that made the use of humanitarian drones less effective (e.g., due to bureaucratic hurdles). This points to a certain ambivalence with respect to the oft repeated claim that less strict drone regulation can be an advantage for promoting drone use in crisis settings. The collected articles suggest that a lack of legislation can also create uncertainty and a perceived risk of arbitrary decisions on the part of local authorities.

The final component of our classification structure relates to perceptions of humanitarian actors. This includes both concern for perceptions of humanitarians from the perspective of the communities they aim to serve, most starkly when there is a concern that associations with the military use of drones may lead to confusion about the roles and goals of humanitarian actors. This risk also applies beyond armed conflict settings to the broader uses of drones for counter-terrorism purposes (Eckenwiler et al., 2015 ). In these ways, concerns may arise around credibility, security, and access, as well as perceptions of neutrality. The review also points to the ways that technology influences the relationship between humanitarian providers and populations affected by crises, and how this could lead to technological distancing between them. It is instructive to note that the HIP-WHS Oxford principles ( 2015 ) cited earlier specifically emphasize the importance of user-driven and participatory approaches for humanitarian innovation. These approaches are also important in settings where drones are being introduced, potentially guarding against both of these concerns (Wang, 2020 , 2021a ). Moreover, participatory approaches may be very valuable when developing new ethics guidelines for humanitarian drone use, including engaging diverse stakeholders involved in and affected by these activities (Wang et. al., forthcoming).

Limitations

The rigor of the review was supported by steps including consultation with academic librarians, refinements to the protocol based on pilot searches, blinded searching and selection of articles by two reviewers, and two expert consultation workshops to receive feedback on provisional findings. We also acknowledge several limitations associated with this review. First, it was challenging to create boundary definitions for the concept of “humanitarian use” and to operationalize this concept in our search and selection process. We adopted a more inclusive approach to this concept by including healthcare uses of drones in low-resource health system contexts. Second, regarding our search for the concept of “ethical considerations,” we used broad terms related to ethics and morality. As a result, we may not have identified papers focused on specific ethical considerations (e.g., issues of justice) if they were not indexed in relation to these broader categories; while our search identified considerations tied to regulations and perceptions of humanitarian action, texts centered on legal or social considerations without explicitly addressing ethics would not have been identified through our search strategy. The third limitation relates to carrying out a comprehensive review of gray literature sources. This is particularly challenging in the humanitarian sector given the extensiveness of gray literature in this domain. We identified and conducted targeted searches of 31 organizational websites; we also carried out general web searches, but it is likely that we failed to identify some relevant gray literature sources through this process. The final limitation is that we restricted our search to sources written in English. While over 90% of the articles pinpointed during the pilot search were in English, it is likely that additional relevant articles were published in other languages, but were not identified based on this search parameter.

“Humanitarian drones” have been increasingly used to support relief and reconstruction efforts in situations of disasters, epidemics, and population displacement, or to overcome structural barriers to healthcare delivery in low-resource settings. This scoping review presents a portrait of the expanding literature from 2012 through early 2020 related to the humanitarian use of drones, and how ethical considerations are understood and conceptualized across academic and gray literature sources. While pointing to key areas of ethical discussion related to humanitarian drone use, our review also shows that there are competing visions for the ethical implications of humanitarian drones across and within different crisis settings, and how these issues can best be addressed by different stakeholders. Our findings can also be situated within the rise of the humanitarian innovation movement, which emerged just prior to the time period of this review (HIF-ALNAP, 2019 ), and which has led to a growing and diverse literature in its own right, including many papers that critically examine ethical issues associated with innovative practices, processes and products, as well as efforts to develop ethics guidelines for innovation projects. Our findings shed light on what explicit and implicit ethical values are present, and how these values are being articulated and interpreted in the existing academic and gray literature. In addition to deepening understanding of ethics and humanitarian drones, our review can contribute to orienting work on the ethics of humanitarian innovation, including the development of frameworks and ethics guidelines that are value-sensitive and context-specific.

Acknowledgements

In developing the ideas presented in this paper, the authors received insightful feedback from Ms. Genevieve Gore and Ms. Tara Mawhinney (McGill University, Canada), to whom we are immensely indebted. We are also grateful to all participants who attended our consultation workshops, including Ms. Dominika Bednarova (Medair, Switzerland), Dr. Thomas Burri (University of St. Gallen, Switzerland), Dr. Gloria Gonzalez Fuster (Free University of Brussels, Belgium), Dr. Elysée Nouvet (Western University, Canada), Mr. Louis Potter (MSF Innovation Unit, Sweden), Dr. John Pringle (McGill University, Canada), Dr. Andreas Alois Reis (World Health Organization, Switzerland), and Dr. Sara de la Rosa (RUAG, Switzerland), whose constructive feedback helped us improve the manuscript, and without whom this scoping review would not have been as comprehensive. We finally thank Dr. Heather Collister for her general editorial assistance, Dr. Daniel Drewniak (University of Zurich, Switzerland) for providing valuable comments to the review protocol, and Dr. Pei-Hua Huang for helping with the pilot-testing of the protocol.

Authors’ contributions

All authors contributed to the review’s conception and design. NW and MC carried out the database searches, article selection, and analysis. MH provided feedback as the analysis was being conducted. All authors wrote sections of the manuscript, and provided substantive input on other sections. All authors read and approved the final manuscript.

Open Access funding provided by Universität Zürich. This research is supported by a grant from the Swiss Network for International Studies (SNIS), and a grant from the Swiss National Science Foundation (SNSF), who generously funded our research project “ Value Sensitive Humanitarian Innovation (VSHI): Integrating Values in the Humanitarian Use of Drones ”.

Data Availability

Code availability, declarations.

The authors have no conflicts of interest to declare that are relevant to the content of this article.

1 Within the scope of this contribution, we use the terms “drones” and “unmanned aerial vehicles” (UAVs) interchangeably.

2 By “humanitarian actor,” we refer to governmental, non-governmental or private organizations, agencies and inter-agency networks that enable national or international humanitarian assistance to be channeled to locations and populations in need of relief efforts or aid supplies.

3 In our review, we regard emergencies resulting from armed conflicts, which require relief work from aid organizations, as humanitarian crises. However, we did not focus on armed conflicts themselves, during which drones may be used as weapons or as peacekeeping intervention measures. See Table ​ Table2 2 for exclusion criteria.

4 Google Scholar: https://scholar.google.com/ .

5 Scopus (Elsevier’s abstract and citation database): https://www.scopus.com/search/form.uri?display=basic .

6 Web of Science (a citation database provided by Clarivate Analytics): https://apps.webofknowledge.com/WOS_GeneralSearch_input.do?product=WOS&search_mode=GeneralSearch .

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The countries least accepting of autistic individuals.

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BERLIN, GERMANY - AUGUST 07: Two men in business business suits shaking hands on August 07, 2014 in ... [+] Berlin, Germany. (Photo by Thomas Trutschel/Photothek via Getty Images)

A new survey of 306 autistic individuals residing in eight countries found that participants from Japan and Belgium experienced the lowest levels of societal acceptance.

The study participants were from the USA, UK, Canada, South Africa, New Zealand, Australia, Japan, and Belgium. While autistic people living in Canada, the UK, and South Africa reported slightly higher levels of feeling accepted, across all countries, only 23.5% of the participants reported that society accepted them as an autistic person.

“Autism acceptance can be defined as an individual feeling accepted or appreciated as an autistic person, with autism positively recognized and accepted by others and the self as an integral part of that individual,” the researchers explained.

“Throughout life, autistic people experience a higher risk of psychiatric and mental health disorders and elevated risks of premature mortality by nearly two decades compared to their non-autistic peers,” they wrote in the study. Depression and anxiety are the most common psychiatric co-morbidities for autistic adults, with prevalence rates reaching as high as 47% and 54%, respectively.

“There are many factors that could contribute to elevated mental health difficulties in autism. For example, since the prevalence of alexithymia (a subclinical condition characterized by difficulties identifying, expressing, and differentiating emotions) is higher in the autistic [49.93%] than the non-autistic population (4.89%),” they noted.

Another major factor, they highlighted, is that autistic individuals belong to a marginalized minority group and face “minority stressors” which include everyday discrimination and internalized stigma.

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“The majority of autistic individuals in the UK feel that society does not accept [43%] or only sometimes accepts [48%] them as an autistic person. Despite this, until recently, research had not directly examined autistic people’s experiences of autism acceptance and how this relates to mental health difficulties,” they added.

Autistic individuals are frequently forced to camouflage their autistic traits to avoid being rejected or discriminated against. Also known as “camouflaging” it is defined as “the use of strategies by autistic people to minimize the visibility of their autism in social situations.”

For example, autistic individuals manage social situations by learning how to use eye contact or develop scripts to help them navigate social interactions. They also deliberately suppress stimming behaviors that help them self-regulate their emotions and bodies in overwhelming environments with loud noises and harsh lights. Stimming behaviors are repetitive movements like hand flapping, rocking back and forth while standing or sitting, pacing around, and/or jumping.

While most autistic people camouflage their traits, it is far more common among autistic women and those higher in autistic traits. “A burgeoning literature suggests that camouflaging may be a risk factor for depression and anxiety in autism irrespective of gender. Those who experience more social stressors (such as a lack of autism acceptance) are more likely to camouflage their autistic traits, which in turn results in higher levels of anxiety and depression,” the researchers highlighted in their study.

In a press release, the lead author of the study, Connor Keating, of the University of Birmingham’s School of Psychology, said: “These findings underscore the crucial need to combat the stigma surrounding autism and reduce the pressure on autistic individuals to conceal their identity.”

The study was published in PLoS One on March 20, 2024.

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