Efficient exploration in crowds by coupling navigation controller and exploration planner
Publication Type
Journal Article
Publication Date
10-2022
Abstract
Autonomous exploration in scenes withmoving pedestrians is critical for deploying autonomous mobile robots in populated places such as malls, airports, and museums. The existence of dynamic obstacles poses challenges on achieving an efficient, safe, and robust exploration system: the robot may get stuck in the pedestrians without making progress in scene coverage; it may collide with humans and hurt them; the human-robot collision will fail the exploration process or cause large drift and artifacts in simultaneous localization and mapping (SLAM). In this work, we propose a framework that can solve these challenges by tightly coupling a reinforcement learned navigation controller and a hierarchical exploration planner enhanced with a recovery planner. The navigation controller provides a value function describing the distribution of crowds around the robot, which will be leveraged by exploration planner and recovery planner to minimize the human-robot interruptions. We evaluate the proposed exploration framework against several methods on a set of indoor benchmarks with pedestrians, verifying the advantages of our method in terms of exploration efficiency, navigation safety, and SLAM quality.
Keywords
Collision avoidance, motion and path planning
Discipline
Information Security
Research Areas
Information Systems and Management
Publication
IEEE Robotics and Automation Letters
Volume
7
Issue
4
First Page
12126
Last Page
12133
ISSN
2377-3766
Identifier
10.1109/LRA.2022.3212670
Publisher
Institute of Electrical and Electronics Engineers
Citation
ZHENG, Zhuoqi; HE, Shengfeng; and PAN, Jia.
Efficient exploration in crowds by coupling navigation controller and exploration planner. (2022). IEEE Robotics and Automation Letters. 7, (4), 12126-12133.
Available at: https://ink.library.smu.edu.sg/sis_research/7854
Additional URL
https://doi.org/10.1109/LRA.2022.3212670