A Sampling-Based Partial Motion Planning Framework for System-Compliant Navigation along a Reference Path

In this paper a generic framework for sampling-based partial motion planning along a reference path is presented. The sampling mechanism builds on the specification of a vehicle model and a control law, both of which are freely selectable. Via a closed-loop forward simulation, the vehicle model is regulated onto a carefully chosen set of terminal states aligned with the reference path, generating system-compliant sample trajectories in accordance with the specified system and environmental constraints. The consideration of arbitrary state and input limits make this framework appealing to nonholonomic systems. The rich trajectory set is evaluated in an online sampling-based planning framework, targeting realtime motion planning in dynamic environments. In an example application, a Volkswagen Golf is modeled via a kinodynamic single-track system that is further constrained by steering angle/rate and velocity/acceleration limits. Control is implemented via state-feedback onto piecewise C0-continuous reference paths. Experiments demonstrate the planner's applicability to online operation, its ability to cope with discontinuous reference paths as well as its capability to navigate in a realistic traffic scenario.