Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem

Ash Wan Yaw Sang, Zhenyuan Yang, Lim Yi, Chee Gen Moo, Rajesh Elara Mohan and Anh Vu Le ()
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Ash Wan Yaw Sang: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Zhenyuan Yang: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Lim Yi: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Chee Gen Moo: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Rajesh Elara Mohan: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Anh Vu Le: Communication and Signal Processing Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam

Mathematics, 2024, vol. 12, issue 6, 1-13

Abstract: Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.

Keywords: inter-reconfigurable robot; complete coverage path planner; Glasius bio-inspired neural network; double-pass coverage; multi-robot path planning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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