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Inter-reconfigurable robot by modified informed sampling-based shortest path planning in cleaning and maintenance

Published online by Cambridge University Press:  19 May 2025

Anh Vu Le*
Affiliation:
Advanced Intelligent Technology Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Cong Hien Dinh
Affiliation:
Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Vinu Sivanantham
Affiliation:
ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore
Prabakaran Veerajagadheswar
Affiliation:
ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore
Do Quang Huy
Affiliation:
Computational Mechanics, Institute of Mechanics Faculty of Mechanical Engineering, Otto von Guericke University, Germany
Bui Vu Minh
Affiliation:
Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
Guangming Chen
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Rajesh Elara Mohan
Affiliation:
ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore
*
Corresponding author. Anh Vu Le; Email: leanhvu@tdtu.edu.vn

Abstract

Connecting individual robots to form an inter-reconfigurable system with a flexible base size enhances the ability to access and cover areas for cleaning and maintenance tasks. Given that increased configuration complexity expands the search space dimension, an optimal routing solution ensuring efficiency is essential. In this paper, we present an inter-reconfigurable multi-robot system capable of adjusting the bases of its two units, along with an optimal path planning approach for confined spaces based on a modified informed rapidly-exploring random tree algorithm by a greedy set (RIRRT*). We validate the navigation of the proposed inter-reconfigurable platform using RIRRT* for four informed dimensional search spaces as a case study in both simulated and real-world environments. The proposed path planning method for the inter-reconfigurable system outperformed conventional strategies, achieving significant reduction in both execution time and energy utilization.

Information

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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