Safety aware robot coverage motion planning with virtual-obstacle-based navigation

A coverage motion planning (CMP) is a kind of coverage path planning, which requires the robot path to fill every grid of the workspace. It is an essential issue in plenty of robotic applications. Safety aware collision-free coverage motion planning of an autonomous vehicle is one of the major challenges in intelligent vehicle systems. Many studies have been focused on the obstacle avoidance to prevent “too close” or “too far” from obstacles, but difficult to obtain an optimal path. In this paper, a virtual obstacle (VO) based safety aware strategy integrated with a biologically inspired neural network (BNN) method is proposed for CMP in a non-stationary environment as safety consideration is greatly crucial in vehicle CMP. The real-time vehicle trajectory is planned through the varying neural activity landscape that represents the dynamic environment. The proposed model for vehicle navigation with safety consideration is capable of planning a real-time appropriate trajectory. The proposed approach is capable of overcoming the either “too close” or “too far” shortcoming. Simulation and comparison studies validate that the proposed model is capable of performing CMP mission to plan more reasonable and shorter collision-free trajectories in non-stationary and unstructured environments.