A Randomized Kinodynamic Planner for Closed-Chain Robotic Systems

Kinodynamic rapidly-exploring random tree (RRT) planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they hard to apply systems with closed-kinematic chains, like parallel robots, collaborative arms manipulating an object, or legged robots keeping their feet contact the environment. The state space such is implicitly-defined manifold that complicates design sampling and steering procedures, leads drift from if standard integration methods used. To address these issues, this article presents a kinodynamic RRT planner constructs atlas incrementally, uses generate states, dynamically steer system toward states. method exploits charts compute locally optimal controls based on linear quadratic regulators. also allows equations motion using local coordinates, which eliminates any results accurate trajectories. best our knowledge, first explicitly takes closed kinematic chains into account. In article, we illustrate performance significantly complex tasks involving planar spatial have lift throw load torque-limited actuators.