システム不確実性下での非線形安全制約を考慮した微分動的計画法
Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties
不確実性を伴うシステムに対し、確率制約を制約タイトニングで決定論的制約に変換し、閉ループ不確実性伝播にフィードバックゲインを用いることで過度な保守性を避ける安全な軌道最適化手法Safe-CDDPを提案し、12自由度までのロボットシミュレーションと実機で有効性を示した。
著者: Gokhan Alcan, Ville Kyrki
分類: cs.RO
原文アブストラクト
Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, it is challenging to ensure that the constraints are not violated. In this paper, we propose Safe-CDDP, a safe trajectory optimization and control approach for systems under additive uncertainties and non-linear safety constraints based on constrained differential dynamic programming (DDP). The safety of the robot during its motion is formulated as chance constraints with user-chosen probabilities of constraint satisfaction. The chance constraints are transformed into deterministic ones in DDP formulation by constraint tightening. To avoid over-conservatism during constraint tightening, linear control gains of the feedback policy derived from the constrained DDP are used in the approximation of closed-loop uncertainty propagation in prediction. The proposed algorithm is empirically evaluated on three different robot dynamics with up to 12 degrees of freedom in simulation. The computational feasibility and applicability of the approach are demonstrated with a physical hardware implementation.
