Multi-UAV Formation Obstacles Avoidance Path Planning Based on PPO Algorithm

The path planning of multi-UAV formations plays a crucial role in various scenarios, including rescue operations, reconnaissance missions, and target strikes. This paper investigates a method for multi-UAV formation obstacle avoidance route planning based on deep reinforcement learning. The proposed method allows UAVs to efficiently plan routes in complex and dynamic three-dimensional obstacle environments. The autonomous decision-making of multiple UAVs is realized by using the proximal policy optimization (PPO) algorithm. In addition, in order to achieve formation obstacle avoidance, we design a reward function to assess the advantages and disadvantages of UAV strategies. Finally, an environment with dynamic obstacles is constructed and trained using the method. Experimental results show that the method is feasible and generalizable.