Obstacle Avoidance Algorithm for Mobile Robot Based on Deep Reinforcement Learning in Dynamic Environments

Developing a friendly and efficient obstacle avoidance algorithm for mobile robot in dynamic environments is challenging in the scenarios where robot plans its paths without observing other obstacles' intents. Recent works have shown the power of deep reinforcement learning techniques to learn collision-free policies. However, many of them ignore the interactions between obstacles, which may cause unnatural trajectory. Meanwhile, the performance of these methods is not well in simulation environment. Combined with the interactions of obstacles, we propose a mobile robot obstacle avoidance algorithm based on deep reinforcement learning in dynamic environments. Firstly, we not only consider the direct impact of obstacles on robots, but also take the interaction between obstacles into account using angel pedestrian grid. Therefore, robot can extract more abundant environmental prior information. Subsequently, the temporal characteristics of obstacles are extracted through attention mechanism, then we can obtain the joint impact of obstacles on the obstacle avoidance strategy. Finally, the robot obtains the control output through value-based reinforcement learning. The feasibility and effectiveness of our proposed algorithm in dynamic environments is verified through simulation experiments.