DualGuard MPPI: サンプリングベースMPCとハミルトン・ヤコビ到達可能性を組み合わせた安全で高性能な最適制御
DualGuard MPPI: Safe and Performant Optimal Control by Combining Sampling-Based MPC and Hamilton-Jacobi Reachability
MPPIのサンプリング過程にハミルトン・ヤコビ到達可能性解析を統合し、生成される全サンプルの安全性を保証しつつ、同じサンプル数で効率的な探索と高性能な最適化を実現する制御フレームワークを提案した。
著者: Javier Borquez, Luke Raus, Yusuf Umut Ciftci, Somil Bansal
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Designing controllers that are both safe and performant is inherently challenging. This co-optimization can be formulated as a constrained optimal control problem, where the cost function represents the performance criterion and safety is specified as a constraint. While sampling-based methods, such as Model Predictive Path Integral (MPPI) control, have shown great promise in tackling complex optimal control problems, they often struggle to enforce safety constraints. To address this limitation, we propose DualGuard-MPPI, a novel framework for solving safety-constrained optimal control problems. Our approach integrates Hamilton-Jacobi reachability analysis within the MPPI sampling process to ensure that all generated samples are provably safe for the system. On the one hand, this integration allows DualGuard-MPPI to enforce strict safety constraints; at the same time, it facilitates a more effective exploration of the environment with the same number of samples, reducing the effective sampling variance and leading to better performance optimization. Through several simulations and hardware experiments, we demonstrate that the proposed approach achieves much higher performance compared to existing MPPI methods, without compromising safety.
