Combined Path and Trajectory Planning for Energy-Efficient Coordination of Automated Vehicles in Confined Areas

In this paper, we present a framework for combined path and motion trajectory planning for the purpose of coordinating fully automated vehicles in confined sites. The path planning component utilizes a Monte-Carlo tree search approach for computing the vehicle paths and the motion trajectory component utilizes a two-stage optimization-based algorithm that optimizes the state and input trajectories for all vehicles while avoiding inter-vehicle conflicts. The motion trajectories are tracked by a low-level controller and both the path and motion trajectories are recomputed based on the feedback signals. The performance of the framework is validated through numerical simulations and results show both improved energy efficiency and productivity.