ガウス過程ベースのモデル予測制御による追い越し制御
Gaussian Process-Based Model Predictive Control for Overtaking
ガウス過程回帰でモデル誤差を学習しつつ、障害物車両との幾何学的制約をMPCに組み込み、高レベル経路計画なしで追い越し・障害物回避を実現する手法を提案した論文。
著者: Wenjun Liu, Chang Liu, Guang Chen, Peng Hang, Alois Knoll
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper proposes a novel framework for addressing the challenge of autonomous overtaking and obstacle avoidance, which incorporates the overtaking path planning into Gaussian Process-based model predictive control (GPMPC). Compared with the conventional control strategies, this approach has two main advantages. Firstly, combining Gaussian Process (GP) regression with a nominal model allows for learning from model mismatch and unmodeled dynamics, which enhances a simple model and delivers significantly better results. Due to the approximation for propagating uncertainties, we can furthermore satisfy the constraints and thereby safety of the vehicle is ensured. Secondly, we convert the geometric relationship between the ego vehicle and other obstacle vehicles into the constraints. Without relying on a higherlevel path planner, this approach substantially reduces the computational burden. In addition, we transform the state constraints under the model predictive control (MPC) framework into a soft constraint and incorporate it as relaxed barrier function into the cost function, which makes the optimizer more efficient. Simulation results reveal the usefulness of the proposed approach.
