Optimal Morphologies of n-Omino-Based Reconfigurable Robot for Area Coverage Task Using Metaheuristic Optimization

Abstract

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal “morphology” is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., “n”. The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot’s optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of Smorphi using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms.