リーマン最適化による移動連続体ロボットの全身障害物回避
Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments
蛇や象の鼻の動きに着想を得て、A*と最小ジャーク計画による大域的軌道計画と、リーマン最適化を用いた局所的な形状制御を統合し、狭所・混雑環境で移動連続体ロボットの台車と腕を協調させて障害物を回避する枠組みを提案した。
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著者: Duo Tang, Ping Deng, Sophie Armanini, Yajing Shen, Toshio Fukuda, Cosimo Della Santina, Peng Lu
分類: cs.RO
原文アブストラクト
Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots in cluttered and confined environments. Existing approaches often address high-level path planning and local motion control separately, limiting coordinated base-arm motion, kinematic consistency, and collision avoidance. Our framework integrates long-horizon trajectory planning with real-time geometric refinement under kinematic and environmental constraints. The global layer combines A* search with minimum-jerk planning to generate smooth reference trajectories. The local layer uses a geometry-consistent inverse kinematics solver and Riemannian optimization to coordinate mobile-base motion and continuum deformation while avoiding obstacles. We evaluate the framework through simulated pipe-traversal tasks with multiple baseline planners and real-world experiments involving navigation among dense obstacles and manipulation in confined spaces. Results demonstrate improved motion consistency, feasibility, and adaptability, supporting coordinated whole-body motion of mobile continuum robots in constrained environments.