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Social crowd navigation of a mobile robot based on human trajectory prediction and hybrid sensing

Social crowd navigation of a mobile robot based on human trajectory prediction and hybrid sensing This paper propose a hierarchical path planning algorithm that first captures the local crowd movement around the robot using RGB camera combined with LiDAR and predicts the movement of people nearby the robot, and then generates appropriate global path for the robot using the global path planner with the crowd information. After deciding the global path, the low-level control system receives the prediction results of the crowd and high-level global path, and generates the actual speed control commands for the robot after considering the social norms. With the high accuracy of computer vision for human recognition and the high precision of LiDAR, the system is able to accurately track the surrounding human locations. Through high-level path planning, the robot can use different movement strategies in different scenarios, while the crowd prediction allows the robot to generate more efficient and socially acceptable paths. With this system, even in a highly dynamic environment caused by the crowd, the robot can still plan an appropriate path reach the destination without causing psychological discomfort to others successfully. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Social crowd navigation of a mobile robot based on human trajectory prediction and hybrid sensing

Chen, Hao-Yun; Huang, Pei-Han; Fu, Li-Chen
Autonomous Robots , Volume 47 (4) – Apr 1, 2023
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References (36)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-023-10103-x
Publisher site
See Article on Publisher Site

Abstract

This paper propose a hierarchical path planning algorithm that first captures the local crowd movement around the robot using RGB camera combined with LiDAR and predicts the movement of people nearby the robot, and then generates appropriate global path for the robot using the global path planner with the crowd information. After deciding the global path, the low-level control system receives the prediction results of the crowd and high-level global path, and generates the actual speed control commands for the robot after considering the social norms. With the high accuracy of computer vision for human recognition and the high precision of LiDAR, the system is able to accurately track the surrounding human locations. Through high-level path planning, the robot can use different movement strategies in different scenarios, while the crowd prediction allows the robot to generate more efficient and socially acceptable paths. With this system, even in a highly dynamic environment caused by the crowd, the robot can still plan an appropriate path reach the destination without causing psychological discomfort to others successfully.

Journal

Autonomous Robots – Springer Journals

Published: Apr 1, 2023

Keywords: Crowd navigation; Social navigation; Pedestrian trajectory prediction

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