Motion Planning of a Non-holonomic Vehicle in a Real Environment by Reinforcement Learning

摘要

In this work, we present a new algorithm that obtains a minimum-time solution in real-time to the optimal motion planning of a non-holonomic vehicle. The new algorithm is based on the combination of Cell-Mapping and reinforcement learning techniques. While the algorithm is performed on the vehicle, it learns its kinematics and dynamics from received experience with no need to have a mathematical model available. The algorithm uses a transformation of the cell-to-cell transitions in order to reduce the time spent in the knowledge of the vehicle's parameters. The presented results have been obtained executing the algorithm with the real vehicle and generating different trajectories to specific goals.