自律レースのための最適化ベース階層型運動計画
Optimization-Based Hierarchical Motion Planning for Autonomous Racing
同一車両モデルを2段階最適化に用い、上位で周回時間最小の軌道を計算し下位のNMPCが追従する階層制御を提案。実車レースで有効性を示し、従来比20%のラップタイム改善を達成した。
著者: José L. Vázquez, Marius Brühlmeier, Alexander Liniger, Alisa Rupenyan, John Lygeros
分類: cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.
