Multiagent Formation Control and Dynamic Obstacle Avoidance Based on Deep Reinforcement Learning

Multiagent formation obstacle avoidance is a crucial research topic in the field of multiagent cooperative control, and deep reinforcement learning has shown remarkable potential in this domain. However, most existing studies are not fully distributed and often involve relatively simple scenarios. In this article, we propose an advanced method based on multiagent deep reinforcement learning to address formation and obstacle avoidance in dynamic obstacles environments. For handling complex environments with an unknown number of obstacles, we use long short-term memory (LSTM) networks to encode dynamic obstacles, thereby improving the efficiency of obstacle avoidance. Our method achieves formation and obstacle avoidance in scenarios with both dynamic and static obstacles, where agents coordinate through fully independent and autonomous decision-making. We utilize the multiagent proximal policy optimization (MAPPO) algorithm for centralized training and distributed execution, enhancing the agents' formation and obstacle avoidance capabilities in complex settings. Through simulation and real-world experiments, and by comparing with benchmark methods, we demonstrate significant improvements in formation effectiveness and obstacle avoidance success rates, showcasing the superiority and practicality of our proposed approach.