安全性価値関数制約付きモデル予測制御による安全性と性能の同時最適化
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
モデル予測制御に安全性価値関数に基づく終端制約を追加し、計画期間を超えた安全性を保証しつつ高性能な軌道生成を実現。実機マニピュレータで検証し、従来手法より安全性と性能の両立を改善した。
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著者: Hao Wang, Nam Nguyen, Armand Jordana, Ludovic Righetti, Somil Bansal
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state and input constraints. Although Model Predictive Control (MPC) provides a principled framework for constrained optimal control, guaranteeing safety beyond its finite planning horizon remains a fundamental challenge. In this work, we augment MPC with a safety value function-based terminal constraint that enforces membership in a control-invariant safe set at the end of each planning horizon. This formulation enables real-time synthesis of trajectories that are both high-performing and provably safe. We show that, under an exact safety value function and a feasible initialization, the proposed MPC scheme is recursively feasible, thereby ensuring persistent safety. In contrast to traditional terminal set constructions that rely on local linearizations or conservative approximations, our approach incorporates a reachability-based safety value function for terminal constraints, yielding less conservative and more expressive safety guarantees. We validate the proposed framework through simulation and hardware experiments on a Flexiv Rizon 10s manipulator. Results demonstrate improved constraint satisfaction and robustness compared to standard state-constrained MPC and reactive safety filtering, while maintaining competitive task performance. The full implementation and experiments are available on the project website.
