シールドモデル予測パス積分：制御バリア関数を用いた計算効率の良いロバストMPCアプローチ
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions
MPPIに制御バリア関数を組み合わせ、外乱に強くCPUでもリアルタイムに動くShield-MPPIを提案し、自動運転レースで制約違反を大幅に削減した。
著者: Ji Yin, Charles Dawson, Chuchu Fan, Panagiotis Tsiotras
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesize optimal controls on-the-fly. In practice, however, MPPI encounters problems limiting its application. For instance, it has been observed that MPPI tends to make poor decisions if unmodeled dynamics or environmental disturbances exist, preventing its use in safety-critical applications. Moreover, the multi-threaded simulations used by MPPI require significant onboard computational resources, making the algorithm inaccessible to robots without modern GPUs. To alleviate these issues, we propose a novel (Shield-MPPI) algorithm that provides robustness against unpredicted disturbances and achieves real-time planning using a much smaller number of parallel simulations on regular CPUs. The novel Shield-MPPI algorithm is tested on an aggressive autonomous racing platform both in simulation and using experiments. The results show that the proposed controller greatly reduces the number of constraint violations compared to state-of-the-art robust MPPI variants and stochastic MPC methods.
