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Combining spatial clustering and tour planning for efficient full area exploration

Published online by Cambridge University Press:  13 September 2024

Jiatong Bao Open the ORCID record for Jiatong Bao [Opens in a new window] ,
Sultan Mamun ,
Jiawei Bao ,
Wenbing Zhang ,
Yuequan Yang  and
Aiguo Song
Jiatong Bao*
Affiliation:
School of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, China
Sultan Mamun
Affiliation:
School of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, China
Jiawei Bao
Affiliation:
School of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, China
Wenbing Zhang
Affiliation:
School of Mathematical Sciences, Yangzhou University, Yangzhou, China
Yuequan Yang
Affiliation:
School of Information Engineering and the School of Artificial Intelligence, Yangzhou University, Yangzhou, China
Aiguo Song
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China
*
Corresponding author: Jiatong Bao; Email: jtbao@yzu.edu.cn
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Abstract

Autonomous exploration in unknown environments has become a critical capability of mobile robots. Many methods often suffer from problems such as exploration goal selection based solely on information gain and inefficient tour optimization. Recent reinforcement learning-based methods do not consider full area coverage and the performance of transferring learned policy to new environments cannot be guaranteed. To address these issues, a dual-stage exploration method has been proposed, which combines spatial clustering of possible exploration goals and Traveling Salesman Problem (TSP) based tour planning on both local and global scales, aiming for efficient full-area exploration in highly convoluted environments. Our method involves two stages: exploration and relocation. During the exploration stage, we introduce to generate local navigation goal candidates straight from clusters of all possible local exploration goals. The local navigation goal is determined through tour planning, utilizing the TSP framework. Moreover, during the relocation stage, we suggest clustering all possible global exploration goals and applying TSP-based tour planning to efficiently direct the robot toward previously detected but yet-to-be-explored areas. The proposed method is validated in various challenging simulated and real-world environments. Experimental results demonstrate its effectiveness and efficiency. Videos and code are available at https://github.com/JiatongBao/exploration.

Keywords

mobile robotautonomous explorationviewpoint plannerspatial clusteringtour planning
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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