不確実な動的環境における衝突回避のためのチャンス制約付きサンプリングベースMPC
Chance-Constrained Sampling-Based MPC for Collision Avoidance in Uncertain Dynamic Environments
サンプリングベースMPCに確率的チャンス制約を組み込み、知覚・運動の不確実性下でもリアルタイムに複数障害物を回避できるC2U-MPPIを提案した論文。
著者: Ihab S. Mohamed, Mahmoud Ali, Lantao Liu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents the Chance-Constrained Unscented Model Predictive Path Integral (C2U-MPPI) framework, a robust sampling-based Model Predictive Control (MPC) algorithm that addresses these challenges by leveraging the U-MPPI control strategy with integrated probabilistic chance constraints, enabling more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics by directly applying non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by leveraging a deterministic form of probabilistic constraints and employing a layered dynamic obstacle representation, enabling real-time handling of multiple obstacles. Extensive experiments in simulated and real-world human-shared environments validate the effectiveness of our algorithm against baseline methods, showcasing its capability to generate feasible trajectories and control inputs that adhere to system dynamics and constraints in dynamic settings, enabled by unscented-based sampling strategy and risk-sensitive trajectory evaluation. A supplementary video is available at: https://youtu.be/FptAhvJlQm8.
