InterUAV Collision Avoidance using DeepQLearning in Flocking Environment In the past twenty years, Unmanned Aerial Vehicles (UAVs) have demonstrated their ability in supporting both military and civilian applications. It has proved itself useful in tasks which are dangerous or too costly with usual methods. Certain applications also contains tasks which can be processed faster by using multiple UAVs that work together and perform smaller tasks which can be further combined to get the intended result. However, to achieve this, we need autonomous coordination among the multi UAVs. Multiple UAVs flying as a swarm, incorporates a pattern or a shape that has to be maintained through out the flight. But there can be cases where the pattern or shape of swarm has to be changed. Under such circumstances, collision has to be avoided between the UAVs as they travel to their new position forming a new shape. We have proposed an algorithm which helps in finding optimal goal positions for the UAVs to flock and to find the trajectory which supports collision free movement to its assigned position. We also ensure that the total distance travelled by individual UAV is minimized. The optimal goal position assignment is done using Hungarian Algorithm. Deep Q Learning method is used to find the optimal flight parameters for a collision free trajectory for the UAVs to reach its goal. Results from simulation show that the algorithm is sufficiently fast for practical applications as optimal assignments and flight parameters were computed for 50 UAVs in less than 1s.

Three Dimensional Collision Avoidance for Multi Unmanned Aerial Vehicles Using Velocity Obstacle Recently, multiUAV systems are attracting growing interests. It offers wide range of applications in civilian and military environment, carrying out dangerous missions that manned aircraft cannot offer. Currently, an important challenge is the collision avoidance algorithm. The idea is to use the collision avoidance algorithm to control the multiUAV systems. This will guarantee the safety of the UAVs. The UAVs will complete the missions without colliding with any moving or static obstacles. This topic has motivated the development of various collision avoidance algorithms. In this paper, we proposed some improvements on the threedimensional velocity obstacle algorithm proposed in Jenie et al. (J. Guid. Control Dyn. 39(10), 2312xe2x80x932323 25). Our improvements are threefold. First, we indicate the limitations of the original 3D collision avoidance method and present the modifications on the algorithm. Second, we develop the velocity obstacle method to be capable of handling cube obstacles in 3D space. Third, a real flight test is conducted to verify the effectiveness of the proposed threedimensional velocity obstacle method.