KDF: Kinodynamic Motion Planning via Geometric Sampling-based Algorithms and Funnel Control

TL;DR

This paper introduces KDF, a framework combining geometric sampling-based planning with funnel control to address kinodynamic motion planning for complex systems, validated through simulations and experiments.

Contribution

It presents a novel integration of geometric planning and funnel control that does not require system dynamics knowledge, enabling broad applicability.

Findings

Successfully plans safe paths in complex environments.

Guarantees safe tracking of paths with funnel control.

Validated on a 6-DOF robotic arm through simulations and experiments.

Abstract

We integrate sampling-based planning techniques with funnel-based feedback control to develop KDF, a new framework for solving the kinodynamic motion-planning problem via funnel control. The considered systems evolve subject to complex, nonlinear, and uncertain dynamics (aka differential constraints). Firstly, we use a geometric planner to obtain a high-level safe path in a user-defined extended free space. Secondly, we develop a low-level funnel control algorithm that guarantees safe tracking of the path by the system. Neither the planner nor the control algorithm use information on the underlying dynamics of the system, which makes the proposed scheme easily distributable to a large variety of different systems and scenarios. Intuitively, the funnel control module is able to implicitly accommodate the dynamics of the system, allowing hence the deployment of purely geometrical motion planners. Extensive computer simulations and experimental results with a 6-DOF robotic arm validate the proposed approach.