Efficient exploration in crowds by coupling navigation controller and exploration planner

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 s...

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Main Authors: ZHENG, Zhuoqi, HE, Shengfeng, PAN, Jia
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7854
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-88572023-06-15T09:00:05Z Efficient exploration in crowds by coupling navigation controller and exploration planner ZHENG, Zhuoqi HE, Shengfeng PAN, Jia 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. 2022-10-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/7854 info:doi/10.1109/LRA.2022.3212670 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Collision avoidance motion and path planning Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Collision avoidance
motion and path planning
Information Security
spellingShingle Collision avoidance
motion and path planning
Information Security
ZHENG, Zhuoqi
HE, Shengfeng
PAN, Jia
Efficient exploration in crowds by coupling navigation controller and exploration planner
description 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.
format text
author ZHENG, Zhuoqi
HE, Shengfeng
PAN, Jia
author_facet ZHENG, Zhuoqi
HE, Shengfeng
PAN, Jia
author_sort ZHENG, Zhuoqi
title Efficient exploration in crowds by coupling navigation controller and exploration planner
title_short Efficient exploration in crowds by coupling navigation controller and exploration planner
title_full Efficient exploration in crowds by coupling navigation controller and exploration planner
title_fullStr Efficient exploration in crowds by coupling navigation controller and exploration planner
title_full_unstemmed Efficient exploration in crowds by coupling navigation controller and exploration planner
title_sort efficient exploration in crowds by coupling navigation controller and exploration planner
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7854
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