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.

翻译：我们把基于取样的规划技术与基于漏斗的反馈控制结合起来,以开发KDF,这是一个通过漏斗控制解决动力运动规划问题的新框架。被考虑的系统在复杂、非线性动态和不确定动态下演化(又称差异限制 ) 。 首先,我们使用一个几何规划器在用户定义的扩展自由空间中获取高水平的安全路径。 其次,我们开发一个低层次的漏斗控制算法,保证系统安全跟踪路径。 计划者或控制算法都没有使用关于系统基本动态的信息,这使得拟议的计划很容易被大量不同的系统和情景所分配。 直觉看,漏斗控制模块能够隐含地容纳系统的动态,从而允许部署纯几何运动规划器。 广泛的计算机模拟和实验结果,使用6DOF机器人臂验证了拟议方法。