Online Complete Coverage Path Planning of a Reconfigurable Robot using Glasius Bio-inspired Neural Network and Genetic Algorithm

Abstract

Area coverage is crucial for robotics applications such as cleaning, painting, exploration, and inspections. Hinged reconfigurable robots have been introduced for these application domains to improve the area coverage performance. However, the existing coverage algorithms of hinged reconfigurable robots require improvements in the aspects; consideration of beyond a limited set of reconfigurable shapes, coordinated reconfiguration and navigation, and online decision-making. Therefore, this paper proposes a novel online Complete Coverage Path Planning (CCPP) method for a hinged reconfigurable robot. The proposed CCPP method is designed with two sub-methods, the Global Coverage Path Planning (GCPP) and Local Coverage Path Planning (LCPP). The GCPP method has been implemented, adapting a Glasius Bio-inspired Neural Network (GBNN) that performs online path planning considering a fixed shape for the robot. Obstacle regions that the GCPP would not adequately cover due to access constraints are covered by the LCPP method that considers concurrent reconfiguration and navigation of the robot. A genetic algorithm determines the reconfiguration parameters that ascertain collision-free coverage and access of obstacle regions. Experimental results validate that the proposed online CCPP method is effective in ascertaining the complete area coverage in heterogeneous environments, including dynamic workspaces. Furthermore, the deployment of the LCPP method can considerably improve the coverage.