俊敏な四足歩行ロボットのための連続時間安全計画・制御
Continuous-Time Safe Planning and Control for Agile Quadrupeds
四足歩行ロボットの全駆動SE(2)モデルを保ち、合成バーンスタイン多項式で軌道を表現することで、計画区間全体にわたり衝突回避と入力制約を保証するリアルタイム計画手法を提案した。
詳しい要約
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著者: Gage MacLin, Mason Peterson, Camilla Tabasso, Venanzio Cichella, Joshua Migdal, Tye Brady, Taskin Padir
分類: cs.RO
原文アブストラクト
Agile quadruped navigation in dynamic environments demands trajectories that are both dynamically feasible and provably collision-free. Most real-time planners achieve tractability through a convex reduction that requires simplified kinematic models, discarding motion that the robot can readily execute, and certify safety only at discretization points, leaving open possible inter-sample collisions with moving obstacles. We present a real-time motion planning framework that retains the fully-actuated $SE(2)$ model of a quadruped, with its anisotropic, heading-coupled velocity bounds, and accepts the resulting nonconvexity. By parameterizing trajectories as composite Bernstein polynomials, we exploit the convex hull property to certify collision avoidance and the input bounds over the entirety of each planning horizon. Solved at online replanning rates, the optimization runs in closed loop, commanding body velocities to the locomotion controller. We validate the approach in simulation against ablations and external baselines, and in hardware experiments on a quadruped navigating among unknown moving obstacles, recovering both lateral and heading-coupled motion that simplified models exclude.