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

TL;DR

This paper introduces a deep reinforcement learning-based multi-subgoal navigation method for robots in crowded environments, leveraging history and interaction data to improve planning and safety.

Contribution

It proposes a novel approach combining subgraph networks and graph neural networks to incorporate history and interactions, enhancing robot navigation in dynamic crowds.

Findings

Outperforms state-of-the-art in success rate

Reduces collision rate in crowded scenarios

Improves future scenario anticipation

Abstract

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.