Research on Pedestrian Avoidance Strategy for AGV Based on Deep Reinforcement Learning

Authors

He Wang, School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110000, China; Joint International Research Laboratory of Modern Construction Engineering Equipment and Technology, Shenyang Jianzhu University, Shenyang 110000, China
Jianing Xu, School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110000, China
Guangyu Yan, School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110000, China; Joint International Research Laboratory of Modern Construction Engineering Equipment and Technology, Shenyang Jianzhu University, Shenyang 110000, China

Abstract

Abstract: To ensure the safety and comfort of pedestrians during Automated Guided Vehicle (AGV) obstacle avoidance in smart factory environments, a deep reinforcement learning-based end-to-end obstacle avoidance method is proposed. The YOLOv8 module is introduced to extract pedestrian pose information, and a visual-based state space is designed. A reinforcement learning mechanism is formulated based on personal space theory, penalizing AGV behaviors such as entering pedestrian comfort space and collisions. A virtual simulation system is constructed, utilizing PPO algorithm along with LSTM network layer for obstacle avoidance strategy training and simulation experiments. Simulation results indicate that this obstacle avoidance strategy, under conditions of no environmental map establishment and visual input, can control the AGV to maintain a comfortable social distance from pedestrians during obstacle avoidance.

Recommended Citation

Wang, He; Xu, Jianing; and Yan, Guangyu (2025) "Research on Pedestrian Avoidance Strategy for AGV Based on Deep Reinforcement Learning," Journal of System Simulation: Vol. 37: Iss. 3, Article 4.
DOI: 10.16182/j.issn1004731x.joss.24-0088
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss3/4

First Page

595

Last Page

606

CLC

TP242; TP18

Recommended Citation

Wang He, Xu Jianing, Yan Guangyu. Research on Pedestrian Avoidance Strategy for AGV Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2025, 37(3): 595-606.

Corresponding Author

Yan Guangyu

DOI

10.16182/j.issn1004731x.joss.24-0088