Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs.

This work focuses on the development of an effective collision avoidance algorithm that detects and avoids obstacles autonomously in the vicinity of a potential collision by using a single ultrasonic sensor and controlling the movement of the vehicle. The objectives are to minimise the deviation from the vehicle’s original path and also the development of an algorithm utilising one of the cheapest sensors available for very lost cost systems. For instance, in a scenario where the main ranging sensor malfunctions, a backup low cost sensor is required for safe navigation of the vehicle while keeping the deviation to a minimum. The developed algorithm utilises only one ultrasonic sensor and approximates the front shape of the detected object by sweeping the sensor mounted on top of the unmanned vehicle. In this proposed approach, the sensor is rotated for shape approximation and edge detection instead of moving the robot around the encountered obstacle. It has been tested in various indoor situations using different shapes of objects, stationary objects, moving objects, and soft or irregularly shaped objects. The results show that the algorithm provides satisfactory outcomes by entirely avoiding obstacles and rerouting the vehicle with a minimal deviation.

Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes’ states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm’s formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV’s resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. As a solution for this, a multi-layered and distributed control system is proposed for the swarm of drones in the formation of hierarchical levels based on the leader–follower approach. The complexity of developing a large system can be reduced in this way. To ensure the tracking performance and response time of the ensemble system, nonlinear and linear control designs are presented; (a) Sliding Mode Control connected with a Proportional-Derivative controller and (b) Linear Quadratic Regular with integral action respectively. The safe travel distance strategy for collision avoidance is introduced and integrated into the control designs for maintaining the hierarchical states in the formation. Both designs provide a rapid adoption with respect to their settling time without introducing oscillations for the dynamic flight movement of vehicles in the cases of (1) nominal, (2) plant-model mismatch, and (3) external disturbance inputs. Also, the nominal settling time of the swarm is improved by 44% on average when using the nonlinear method as compared to the linear method. Furthermore, the proposed methods are fully distributed so that each UAV autonomously performs the feedback laws in order to achieve better modularity and scalability.

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 dB. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.