Sampling-Based Motion Planning with Preordered Objectives

Motion planning for cyber-physical systems requires addressing numerous system objectives and constraints, including satisfying physical limitations, ensuring safety, reaching goal areas, or reducing energy consumption. Typically, it is only possible to achieve some of the objectives simultaneously since they may contradict. The objectives are usually weighted to specify which plans are preferred in such situations, resulting in a cumbersome tuning process. In this work, we use a weight-free prioritization of the objectives through preorders and introduce a novel sampling-based motion planner designed to efficiently generate trajectories optimizing preordered objectives. We ensure that only the smallest number of required objectives is evaluated to reduce computational time. Our approach can holistically define and solve many types of multi-objective optimization problems, and its usefulness is demonstrated for a Mars rover and an autonomous vehicle.