Kinodynamic planning with reachability prediction for PTL maintenance robot

Abstract

This article presents a novel method—Dynamic Environment Rapid Search Tree—for power transmission line maintenance robot, which is based on the learned field function of reachability and the genetic best-first policy. Dynamic Environment Rapid Search Tree uses a priori information to optimize the node selection strategy in the rigid–flexible coupling environment with slight perturbation and generates the joint path in the configuration space. While the search tree rapidly extends toward the goal configuration, it effectively avoids the obstacles and greatly reduces the expansion of the irrelevant region. Finally, the joint trajectory considering the dynamic constraints and cost function is given, which provides the reference positions and torques for the robot controller. Traditional planning algorithms are compared with our proposed method under two different operation modes, and the planner is demonstrated on the robot under real settings. The experiment results verify the feasibility and adaptiveness of the proposed algorithm and planner, even in slightly and continuously varying environment.