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.

Rapid Collision Detection for Multicopter Trajectories We present a continuoustime collision detection algorithm for quickly detecting whether certain polynomial trajectories in time intersect with convex obstacles. The algorithm is used in conjunction with an existing multicopter trajectory generation method to achieve rapid, obstacleaware motion planning in environments with both static convex obstacles and dynamic convex obstacles whose boundaries do not rotate. In general, this problem is difficult because the presence of convex obstacles makes the feasible space of trajectories nonconvex. The performance of the algorithm is benchmarked using Monte Carlo simulations, and experimental results are presented that demonstrate the use of the method to plan collisionfree multicopter trajectories in milliseconds in environments with both static and dynamic obstacles.