End-to-end UAV obstacle avoidance decision based on deep reinforcement learning

Abstract

Aiming at the problem that the traditional UAV obstacle avoidance algorithm needs to build offline three-dimensional maps, discontinuous speed control and limited speed direction selection, we study the end-to-end obstacle avoidance decision method of UAV continuous action output based on DDPG(deep deterministic policy gradient) deep reinforcement learning algorithm. Firstly, an end-to-end decision control model based on DDPG algorithm is established. The model can output continuous control variables, namely UAV obstacle avoidance actions, according to the continuous state information perceived. Secondly, the training verification is carried out on the platform of UE4 + Airsim. The results show that the model can realize the end-to-end UAV obstacle avoidance decision. Finally, the 3DVFH(three dimensional vector field histogram) obstacle avoidance algorithm model with the same data source is compared and analyzed. The experiment shows that DDPG algorithm has better optimization effect on the obstacle avoidance trajectory of UAV.

Keywords

• uav
• obstacle avoidance
• deep deterministic policy gradient (ddpg)
• reinforcement learning