森林クレーン向け長尺リンクの効率的衝突検出：ユークリッド距離場における新手法
Efficient Collision Detection for Long and Slender Robotic Links in Euclidean Distance Fields: Application to a Forestry Crane
森林クレーンのような長く細いリンクを持つマニピュレータの衝突検出を、従来の球近似より効率的かつ正確に行うアルゴリズムを提案し、実LiDARデータで検証した。
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著者: Marc-Philip Ecker, Bernhard Bischof, Minh Nhat Vu, Christoph Fröhlich, Tobias Glück, Wolfgang Kemmetmüller
分類: cs.RO
原文アブストラクト
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.
