Htetran xe2x80x93 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 selfreconfigurable robot platform named as xe2x80x9chTetranxe2x80x9d 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 xe2x80x9chTetranxe2x80x9d brings out a higher area coverage performance in all considered experimental cases.

Evolutionary AlgorithmBased Complete Coverage Path Planning for Tetriamond Tiling Robots Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a selfreconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shapeshifting of the platform. Thus, during motion planning, the optimal navigation sequence of a selfreconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamondbased robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shapeshifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamondbased reconfigurable robots.