連続陰的SDFに基づく任意形状ロボットの軌道最適化
Continuous Implicit SDF Based Any-shape Robot Trajectory Optimization
ロボットを陰的SDFで表現し、掃引体積への符号付き距離を効率的に計算することで、複雑な形状のロボットでも高精度かつ連続的な衝突評価を可能にする軌道最適化手法を提案した。
著者: Tingrui Zhang, Jingping Wang, Chao Xu, Alan Gao, Fei Gao
分類: cs.RO
原文アブストラクト
Optimization-based trajectory generation methods are widely used in whole-body planning for robots. However, existing work either oversimplifies the robot's geometry and environment representation, resulting in a conservative trajectory, or suffers from a huge overhead in maintaining additional information such as the Signed Distance Field (SDF). To bridge the gap, we consider the robot as an implicit function, with its surface boundary represented by the zero-level set of its SDF. Based on this, we further employ another implicit function to lazily compute the signed distance to the swept volume generated by the robot and its trajectory. The computation is efficient by exploiting continuity in space-time, and the implicit function guarantees precise and continuous collision evaluation even for nonconvex robots with complex surfaces. Furthermore, we propose a trajectory optimization pipeline applicable to the implicit SDF. Simulation and real-world experiments validate the high performance of our approach for arbitrarily shaped robot trajectory optimization.
