sensors Article Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot Anh Vu Le 1,2 , Prabakaran Veerajagadheswar 1, Phone Thiha Kyaw 3 , Mohan Rajesh Elara 1 and Nguyen Huu Khanh Nhan 2,∗ 1 ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore;
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[email protected] (M.R.E.) 2 Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam 3 Department of Mechatronic Engineering, Yangon Technological University, Insein 11101, Myanmar;
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[email protected] Abstract: One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot’s goal enables the complete coverage of the entire area by reconfigur- ing to different shapes according to the area’s needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving Citation: Le, A.V.; energy usage during navigation. This implies that the robot is able to cover larger areas entirely Veerajagadheswar, P.; Thiha Kyaw, P.; with the least required actions. This paper presents a complete path planning (CPP) for hTetran, Elara, M.R.; Nhan, N.H.K.