Improving robot path planning efficiency with probabilistic virtual environment models

Probabilistic multiresolution occupancy grid modeling has recently been developed to map both 2D and 3D cluttered spaces. These models can be used to provide an enhanced representation of the cluttering state of space in a robot workspace. As a result, they reveal to be promising tools to improve classical potential field based robot path planning strategies. These approaches rely on a combination of repulsive and attractive potential fields to attract the robot toward a given goal while ensuring safe distance from the obstacles. This paper proposes an new approach to directly compute repulsive and attractive potential fields from the probabilistic occupancy models without the need for distance tables or wave propagation. Experimentation revealed that multiresolution probabilistic models encoded as quadtrees or octrees significantly reduce processing time and speed up robot operation.