A hierarchical approach for primitive-based motion planning and control of autonomous vehicles

摘要

A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce the computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, we develop a locally greedy algorithm with effective backtracking ability and compare this algorithm to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the l_2-induced norm as the performance measure is provided to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation.