Global kinodynamic motion planning for an underactuated timber crane with stochastic trajectory optimization

Abstract

Efficient, collision-free motion planning is a crucial building block for autonomous timber cranes. These machines present unique challenges, such as hydraulic actuation constraints and passive joints — factors that are seldom addressed by current motion planning methods. This paper presents efficient global kinodynamic motion planning for timber cranes using the recently introduced via-point-based stochastic trajectory optimization (VP-STO) algorithm. We demonstrate the effectiveness by comparing the approach to LQR-RRT*, a sampling-based kinodynamic planner for underactuated systems, and a geometric RRT*, which solves a simplified geometric planning problem without kinodynamic constraints.