Autonomous large-scale machine operations require fast, efficient, and collision-free motion planning while addressing unique challenges such as hydraulic actuation limits and underactuated joint dynamics. This paper presents a novel two-step motion planning framework designed for an underactuated forestry crane. The first step employs GPU-accelerated stochastic optimization to rapidly compute a globally shortest collision-free path. The second step refines this path into a dynamically feasible trajectory using a trajectory optimizer that ensures compliance with system dynamics and actuation constraints. The proposed approach is benchmarked against conventional techniques, including RRT-based methods and purely optimization-based approaches. Simulation results demonstrate substantial improvements in computation speed and motion feasibility, making this method highly suitable for complex crane systems.

翻译：自主化大型机械作业需要快速、高效且无碰撞的运动规划，同时应对液压驱动限制和欠驱动关节动力学等独特挑战。本文提出一种专为欠驱动林业起重机设计的新型两步式运动规划框架。第一步采用GPU加速随机优化方法，快速计算全局最短的无碰撞路径。第二步通过轨迹优化器将此路径细化为动态可行的轨迹，确保符合系统动力学与驱动约束。所提方法以基于RRT的传统方法和纯优化方法为基准进行对比测试。仿真结果表明，该方法在计算速度和运动可行性方面均有显著提升，使其特别适用于复杂起重机系统。