確率的ハイブリッドシステムのためのリスク考慮型MPPI
Risk-aware MPPI for Stochastic Hybrid Systems
状態依存の動的切り替えを伴う確率的ハイブリッドシステムに対し、Unscented変換を用いて不確実性を考慮したMPPIを提案し、移動ロボットの動的障害物回避に応用した。
著者: Hardik Parwana, Mitchell Black, Bardh Hoxha, Hideki Okamoto, Georgios Fainekos, Danil Prokhorov, Dimitra Panagou
分類: cs.RO
原文アブストラクト
Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen to predict future states under state-dependent disturbances. We employ recently proposed Unscented Transform-based methods to capture stochasticity in the states as well as the state-dependent switching surfaces. This is in contrast to previous works that perform switching based only on the mean of predicted states. We focus our motion planning application on the navigation of a mobile robot in the presence of dynamically moving agents whose responses are based on sensor-constrained attention zones. We evaluate our framework on a simulated mobile robot and show faster convergence to a goal without collisions when the robot exploits the hybrid human dynamics versus when it does not.
