针对细长机械臂设计高效碰撞检测算法，提升林用起重机运动规划速度。
Efficient Collision Detection for Long and Slender Robotic Links in Euclidean Distance Fields: Application to a Forestry Crane
- 利用机械臂细长特性设计专用碰撞检测方法
- 相比传统球体近似，计算效率显著提升
- 无需调整精度参数，适合实际野外作业
复杂户外环境中的无碰撞运动规划高度依赖外部传感器感知。常用方法将环境表示为体素化的欧几里得距离场，机器人则通常近似为球体。然而，对于林业起重机这类大型长臂机械臂，这种球体近似既低效又不准确。本文提出一种新型碰撞检测算法，专门利用此类机械臂的细长结构特征，显著提升运动规划算法的计算效率。与传统球体分解方法不同，本方法不仅提升效率，还自然避免了需额外调节近似精度参数的问题。通过真实林用起重机的激光雷达数据及模拟环境数据验证了算法有效性。
原文摘要 · Abstract (English)
Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate. This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm's effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.
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