Three‐dimensional obstacle avoidance for UAV based on reinforcement learning and RealSense

Abstract

With the increasingly widespread application of unmanned aerial vehicle (UAV), safety issues such as effectiveness of obstacle avoidance have been paid more attentions. The classical obstacle avoidance algorithms are mostly suitable for mobile robots, but these algorithms are not ideal for UAV using in three‐dimensional space. Most of the three‐dimensional obstacle avoidance algorithms which are more effective using RGB image data as input. Thus, a large amount of image data is involved in complex computing process. This study proposes an effective obstacle avoidance algorithm for UAV with less input data and fewer sensors based on RealSense and reinforcement learning. It combines the feature map of the depth image of RealSense as the input data of reinforcement learning and the current direction of flight of UAV to calculate the direction and angle of avoiding. The proposed algorithm that implements real‐time obstacle avoidance for UAV has been verified by simulation and tested in three‐dimensional space scenario.