Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

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.

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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/iccasit62299.2024.10828100
Citations
17
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
5.93
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article

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Ru‐Rong Ji, Jiajing Chen, Jiaqi Han, Dezhi Yu et al.
17 citations
Advanced Manufacturing and Logistics Optimization
5.93
article

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Ru‐Rong Ji, Jiajing Chen, Jiaqi Han, Dezhi Yu, Dajun Tao, Zhenyu Wan, Bo Hong, Jieting Lian, Shengyuan Zhang, Xinyu Qiu, Fanghao Ni, Baiwei Sun, Keqin Li
article en
17 citations

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.

Northeastern University (US), Northern Arizona University (US), University of California, Irvine (US), Cornell University (US), Courant Institute of Mathematical Sciences (US), AMA Computer University (PH), New York University (US), Seattle University (US), Shanghai Institute of Technology (CN), Carnegie Mellon University (US), University of California, Berkeley (US)
Reduced inequalities
Openalex Percentile: Top 4%
Advanced Manufacturing and Logistics Optimization
5.93
DOISourceOpenAlex
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