Energy-Efficient Coverage Path Planning for a Reconfigurable Robot

Reconfigurable robots have been introduced for many application domains, including area coverage. Energy-efficient complete coverage planning is foremost expected in robots used in coverage applications. This paper proposes a novel energy-efficient coverage path planner for a reconfigurable robot. The proposed method decides the reconfigurations to reduce energy usage while ascertaining the complete coverage, yielding an optimum coverage plan. A Modified Genetic Algorithm (MGA) with a unique chromosome representation is proposed for handling the reconfiguration and coverage order. A cell decomposition and clustering are proposed to divide the map into smaller cells and later merge them to form bigger cellular regions. The A* path planner, modified with new parameters to facilitate search and consider reconfiguration requirements, is proposed for inter-cluster movements. The proposed method has been compared with the state-of-the-art coverage methods of reconfigurable and fixed-shape robots through experiments. Based on the experimental results, the proposed method effectively reduces the total energy consumption of a reconfigurable robot while ensuring complete coverage in a given environment. With this approach, nearly 100% coverage was achieved, whereas existing methods failed to provide complete coverage in some cases. Path costs were reduced by 47% and 24% compared to existing approaches for reconfigurable and fixed robots, respectively, in cases where complete coverage was achievable.