复杂动态环境下基于深度强化学习的AGV避障方法

To improve the collision avoidance capability of Automated Guided Vehicles (AGV) in the complex dynamic environment of smart factories,enable them to carry out material handling tasks more safely and efficiently following the global path, a local collision avoidance method based on deep reinforcement learning was proposed. The problem of collision avoidance of AGV was formulated as Partial Observational Markov Decision Process (POMDP) in which observation space, action space and reward function were expatiated. Tracking of the global path was a-chieved by setting different reward values. Then a Deep Deterministic Policy Gradient (DDPG) method was further implemented to solve collision avoidance policy. The trained policy was validated in various simulated scenarios, and the effectiveness was proved. The experimental results showed the proposed approach could respond to the complex dynamic environment and reduce the time and distance of collision avoidance.