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Estimated path information gain-based robot exploration under perceptual uncertainty

Published online by Cambridge University Press:  06 January 2022

Jie Liu ,
Chaoqun Wang ,
Wenzheng Chi Open the ORCID record for Wenzheng Chi [Opens in a new window] ,
Guodong Chen  and
Lining Sun
Jie Liu
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Chaoqun Wang
Affiliation:
Department of Automation, Shandong University, Jinan, China
Wenzheng Chi*
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Guodong Chen
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
Lining Sun
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou215021, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Keywords

robot explorationfrontier detectionpath information gainperceptual uncertainty
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2748 - 2764
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

This project is partially supported by National Key R&D Program of China grant #2019YFB1310003, National Science Foundation of China grant #61903267 and China Postdoctoral Science Foundation grant #2020M681691 awarded to Wenzheng Chi.

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