用GPU加速规划林业起重机在复杂环境中的无碰撞路径
GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments
- 分两步规划：先用GPU快速找最短无碰撞路径
- 再优化轨迹，满足液压限制和非完整动力学约束
- 比传统方法快且更可行，适合复杂起重机系统
自主大型机械作业需要在考虑液压驱动限制和非完整关节动力学等独特挑战的前提下，实现快速、高效且无碰撞的运动规划。本文提出一种针对非完整林业起重机的新颖两阶段运动规划框架。第一阶段采用GPU加速的随机优化，迅速计算出全局最短的无碰撞路径；第二阶段通过轨迹优化器将该路径细化为满足系统动力学和执行机构约束的动态可行轨迹。所提方法与基于RRT的方法及纯优化方法进行对比评估。仿真结果表明，在计算速度和运动可行性方面均有显著提升，适用于复杂起重机系统。
原文摘要 · Abstract (English)
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.
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