School of Mechatronical Engineering, Beijing Institute of Technology, 5th South Zhongguancun Street, Beijing 100081, China.
Sensors (Basel). 2023 Mar 10;23(6):2997. doi: 10.3390/s23062997.
This paper proposes a feature fusion algorithm for solving the path planning problem of multiple unmanned aerial vehicles (UAVs) using GPS and communication denial conditions. Due to the blockage of GPS and communication, UAVs cannot obtain the precise position of a target, which leads to the failure of path planning algorithms. This paper proposes a feature fusion proximal policy optimization (FF-PPO) algorithm based on deep reinforcement learning (DRL); the algorithm can fuse image recognition information with the original image, realizing the multi-UAV path planning algorithm without an accurate target location. In addition, the FF-PPO algorithm adopts an independent policy for multi-UAV communication denial environments, which enables the distributed control of UAVs such that multi-UAVs can realize the cooperative path planning task without communication. The success rate of our proposed algorithm can reach more than 90% in the multi-UAV cooperative path planning task. Finally, the feasibility of the algorithm is verified by simulations and hardware.
本文提出了一种基于 GPS 和通信干扰条件的多无人机（UAV）路径规划问题的特征融合算法。由于 GPS 和通信的干扰，无人机无法获得目标的精确位置，导致路径规划算法失败。本文提出了一种基于深度强化学习（DRL）的特征融合近端策略优化（FF-PPO）算法；该算法可以将图像识别信息与原始图像融合，实现了无需目标位置精确的多无人机路径规划算法。此外，FF-PPO 算法采用了多无人机通信干扰环境下的独立策略，实现了无人机的分布式控制，使得多无人机可以在没有通信的情况下实现协同路径规划任务。我们提出的算法在多无人机协同路径规划任务中的成功率可以达到 90%以上。最后，通过仿真和硬件验证了算法的可行性。
