Safety Aware Collision-Free Region-Filling Operation of an Autonomous Vehicle

Luo, Chaomin

An region-filling operation (RFO) 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 region-filling operation

of an autonomous vehicle is one of the major challenges in

intelligent vehicle systems.  A virtual obstacle based safety aware strategy integrated with a biologically inspired neural network method is proposed for RFO in a non-stationary environment as safety consideration is greatly crucial in vehicle RFO. 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 “comfortable” trajectory. The proposed approach is capable of overcoming the either “too close” or “too far” shortcoming by utilizing proposed virtual obstacles (VO) methodologies. Simulation and comparison studies validated

that the proposed model is capable of performing RFO task to plan more reasonable and shorter collision-free trajectories

in non-stationary and unstructured environments.