Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning

• URL: http://arxiv.org/abs/2102.13265v1
• Date: Fri, 26 Feb 2021 02:17:13 GMT
• Title: Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning
• Authors: Zhiqian Zhou, Pengming Zhu, Zhiwen Zeng, Junhao Xiao, Huimin Lu, Zongtan Zhou
• Abstract summary: It is still an open and challenging problem for mobile robots navigating along time-efficient and collision-free paths in a crowd. Deep reinforcement learning is a promising solution to this problem. We propose a graph-based deep reinforcement learning method, SG-DQN. Our model can help the robot better understand the crowd and achieve a high success rate of more than 0.99 in the crowd navigation task.
• Score: 8.211771115758381
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: It is still an open and challenging problem for mobile robots navigating along time-efficient and collision-free paths in a crowd. The main challenge comes from the complex and sophisticated interaction mechanism, which requires the robot to understand the crowd and perform proactive and foresighted behaviors. Deep reinforcement learning is a promising solution to this problem. However, most previous learning methods incur a tremendous computational burden. To address these problems, we propose a graph-based deep reinforcement learning method, SG-DQN, that (i) introduces a social attention mechanism to extract an efficient graph representation for the crowd-robot state; (ii) directly evaluates the coarse q-values of the raw state with a learned dueling deep Q network(DQN); and then (iii) refines the coarse q-values via online planning on possible future trajectories. The experimental results indicate that our model can help the robot better understand the crowd and achieve a high success rate of more than 0.99 in the crowd navigation task. Compared against previous state-of-the-art algorithms, our algorithm achieves an equivalent, if not better, performance while requiring less than half of the computational cost.

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