Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Research focuses on robots related to area coverage applications such as cleaning, painting, demining, lawn moving, and inspection is gaining significant momentum in recent years. The majority of such research platforms faces significant performance issues while accessing narrow and constrained spaces due to their fixed morphology. To this end, we have developed a novel self-reconfigurable robot platform named as “hTetran” inspired from Polyabolo which can change its morphology to any of the six tetrabolo shapes with an objective of maximizing the area coverage. This paper presents the system design, reconfiguration theory, and locomotion modules of the developed robot, including the adaptation of Polyabolo tiling theory as a coverage path planning strategy for autonomous navigation. The paper concludes with a set of experiments in a mocked office room setup that validates the efficiency of the proposed robot in terms of area coverage. Our experimental trials indicate that the “hTetran” brings out a higher area coverage performance in all considered experimental cases.