Global and Local Area Coverage Path Planner for a Reconfigurable Robot

Abstract

Area coverage is essential for robots used in cleaning, painting, and exploration applications. Reconfigurable robots have been introduced to solve the area coverage limitation of fixed-shape robots. The existing global coverage algorithms of reconfigurable robots are limited to consideration of a limited set of predefined shapes for the reconfiguration and do not consider the exact geometrical shape of obstacles. Therefore, degraded coverage performance could be observed from the existing methods. On the other hand, the coverage methods that consider reconfiguring beyond a limited set of predefined shapes are limited to local coverage. Furthermore, these methods only consider a single reconfiguration for the coverage. Therefore, this paper proposes a novel coverage method for a reconfigurable robot consisting of both global and local path planners. The global path planner uses boustrophedon motion combined with the A * algorithm. The optimum grid positioning that maximizes the global coverage is determined through a Genetic Algorithm (GA). The local coverage planner performs continuous reconfig-uration of the robot to adequately cover obstacle zones while navigating through narrow spaces without collisions. A GA is used to determine the reconfiguration parameters of the robot at each instance of the local coverage. Simulation results confirm that the proposed method is effective in performing both global and local coverage path planning for improving the area coverage performance.