Online Coverage Path Planning Scheme for a Size-Variable Robot

Coverage Path Planning (CPP) is an essential feature of robots deployed for applications such as lawn mowing, cleaning, painting, and exploration. However, most of the state-of-the-art CPP methods are proposed for fixed-morphology robots, and the coverage performance is limited by physical constraints such as the inaccessibility of narrow spaces. Apart from area coverage, productivity depends on coverage time and energy usage. A robot capable of varying its footprint size could be a solution for improving productivity in these aspects. In addition to that, the environments, where robots are deployed for coverage, are often subjected to changes causing uncertainties. Therefore, this paper proposes an online CPP scheme for a size-variable robot to improve coverage productivity. The navigation planning of the proposed Size-Variable CPP (VSCPP) scheme has been implemented by adapting a Glasius bio-inspired neural network that guides a robot in an efficient path for coverage while coping with dynamic changes. The size variation required for a situation is determined by analyzing a set of occupancy grid maps corresponding to the size steps of the robot. According to the results, the proposed VSCPP can ascertain coverage while coping with dynamic changes in an environment. The reduction of the coverage time due to the size variability is significant compared to a robot with no VSCPP scheme.