動的環境向け最適化ファブリックによる動作生成
Dynamic Optimization Fabrics for Motion Generation
最適化ファブリックを動的環境と非ホロノミックロボットに拡張し、移動障害物回避や目標軌道への収束を保証するとともに、MPCより高速な再計画（最大500Hz）を実現した。
著者: Max Spahn, Martijn Wisse, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and non-holonomic robots and prove that fundamental properties can be conserved. We show that convergence to desired trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. Additionally, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a non-holonomic mobile manipulator with 10 degrees of freedom and avoidance of a moving human, supporting the theoretical findings.
