Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments
/ Authors
Keqin Li, Jiajing Chen, Denzhi Yu, Dajun Tao, Xinyu Qiu, Jieting Lian, Baiwei Sun, Shengyuan Zhang, Zhenyu Wan, Ran Ji
and 2 more authors
/ Abstract
This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.
Journal: 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)