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

2021-02-26 02:17:13
Zhiqian Zhou, Pengming Zhu, Zhiwen Zeng, Junhao Xiao, Huimin Lu, Zongtan Zhou

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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.13265

PDF

https://arxiv.org/pdf/2102.13265.pdf


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