Optimal morphologies generation for an expandable robot using divide and conquer approach

Reconfigurable robots have gained significant interest in the cleaning and maintenance domain due to their shape-changing capability. However, continuous reconfiguration leads to increased energy consumption. To address this issue, we propose a framework for reconfigurable robots that uses a Divide-and-Conquer (DaC) approach. This approach divides a given map into the least number of n-sub-maps. Moreover, it identifies optimal morphologies of the reconfigurable robot for each sub-map using a metaheuristic optimization scheme, resulting in conquering, i.e., energy-efficient area coverage. A global coverage planner is introduced to perform complete area coverage, adaptable for varying footprint reconfigurable robots. The efficiency of our proposed framework is showcased by the e−Smorphi robot, which can adjust its dimensions, namely, length and width, from 250 to 350 mm, enabling a wide range of morphologies. Both simulation and real-world scenarios were used to validate the approach with two unique 2D maps and two different metaheuristic optimization algorithms (MACO and OMOPSO). The results indicate that our proposed approach effectively identifies energy-efficient optimal morphologies, demonstrating its potential for improving the energy efficiency of reconfigurable robots in area coverage tasks.