Currently,multi-UAV collision detection and avoidance is facing many challenges,such as navigating in cluttered environments with dynamic obstacles while equipped with low-cost perception devices having a limited fiel...Currently,multi-UAV collision detection and avoidance is facing many challenges,such as navigating in cluttered environments with dynamic obstacles while equipped with low-cost perception devices having a limited field of view(FOV).To this end,we propose a communication-aided collision detection and avoidance method based on curriculum reinforcement learning(CRL).This method integrates perception and communication data to improve environmental understanding,allowing UAVs to handle potential collisions that may go unnoticed.Furthermore,given the challenges in policy learning caused by the substantial differences in scale between perception and communication data,we employ a two-stage training approach,which performs training with the network expanded from part to whole.In the first stage,we train a partial policy network in an obstacle-free environment for inter-UAV collision avoidance.In the second stage,the full network is trained in a complex environment with obstacles,enabling both inter-UAV collision avoidance and obstacle avoidance.Experiments with PX4 software-in-the-loop(SITL)simulations and real flights demonstrate that our method outperforms state-of-the-art baselines in terms of reliability of collision avoidance,including the DRL-based method and NH-ORCA(Non-Holonomic Optimal Reciprocal Collision Avoidance).Besides,the proposed method achieves zero-shot transfer from simulation to real-world environments that were never experienced during training.展开更多
基金supported by National Natural Science Foundation of China(U23B2032 and U2241214)Postgrad-uate Scientific Research Innovation Project of Hunan Province(CX20220055).
文摘Currently,multi-UAV collision detection and avoidance is facing many challenges,such as navigating in cluttered environments with dynamic obstacles while equipped with low-cost perception devices having a limited field of view(FOV).To this end,we propose a communication-aided collision detection and avoidance method based on curriculum reinforcement learning(CRL).This method integrates perception and communication data to improve environmental understanding,allowing UAVs to handle potential collisions that may go unnoticed.Furthermore,given the challenges in policy learning caused by the substantial differences in scale between perception and communication data,we employ a two-stage training approach,which performs training with the network expanded from part to whole.In the first stage,we train a partial policy network in an obstacle-free environment for inter-UAV collision avoidance.In the second stage,the full network is trained in a complex environment with obstacles,enabling both inter-UAV collision avoidance and obstacle avoidance.Experiments with PX4 software-in-the-loop(SITL)simulations and real flights demonstrate that our method outperforms state-of-the-art baselines in terms of reliability of collision avoidance,including the DRL-based method and NH-ORCA(Non-Holonomic Optimal Reciprocal Collision Avoidance).Besides,the proposed method achieves zero-shot transfer from simulation to real-world environments that were never experienced during training.
