非構造環境における自動運転車のための効率的な時空間軌道プランナ
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
車両の微分平坦性を利用して非ホロノミック制約を保ちつつ、安全走行回廊と符号付き距離近似で障害物を回避するリアルタイム時空間軌道最適化手法を提案した。
著者: Zhichao Han, Yuwei Wu, Tong Li, Lu Zhang, Liuao Pei, Long Xu, Chengyang Li, Changjia Ma, Chao Xu, Shaojie Shen, Fei Gao
分類: cs.RO
原文アブストラクト
As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community
