KDF: Kinodynamic Motion Planning via Geometric Sampling-based Algorithms and Funnel Control
This addresses motion planning for robotics and autonomous systems with uncertain dynamics, offering a distributable solution, though it appears incremental as it combines existing techniques in a novel way.
The paper tackles the kinodynamic motion-planning problem for systems with complex, nonlinear, and uncertain dynamics by integrating geometric sampling-based planning with funnel control, resulting in a framework that guarantees safe path tracking without requiring dynamic information, validated through simulations and experiments with a 6-DOF robotic arm.
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.