mlsanityData to 7 Oct 2026

Multi-subgoal Robot Navigation in Crowds with History Information and Interactions

arXiv 2205.02003 ↗First posted 4 May 2022cs.RO cs.AIcited by 1

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Abstract (arXiv)

Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationships among all agents (robot and humans). Specifically, the next position point is planned for the robot by introducing history information and interactions in our work. Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly. Further consideration, in order to reduce the probability of unreliable next position points, the selection module is designed after policy network in the reinforcement learning framework. In addition, the next position point generated from the selection module satisfied the task requirements better than that obtained directly from the policy network. The experiments demonstrate that our approach outperforms state-of-the-art approaches in terms of both success rate and collision rate, especially in crowded human environments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

21 of 40 references.

References · 21

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9 more
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Citing papers per year: 2025: 1.

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  1. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approachescited by 2