Optimization-based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework

Autor:	Chi, Xuemin, Liu, Zhitao, Huang, Jihao, Hong, Feng, Su, Hongye
Rok vydání:	2022
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	This paper introduces a hierarchical framework that integrates graph search algorithms and model predictive control to facilitate efficient parking maneuvers for Autonomous Vehicles (AVs) in constrained environments. In the high-level planning phase, the framework incorporates scenario-based hybrid A* (SHA*), an optimized variant of traditional Hybrid A*, to generate an initial path while considering static obstacles. This global path serves as an initial guess for the low-level NLP problem. In the low-level optimizing phase, a nonlinear model predictive control (NMPC)-based framework is deployed to circumvent dynamic obstacles. The performance of SHA* is empirically validated through 148 simulation scenarios, and the efficacy of the proposed hierarchical framework is demonstrated via a real-time parallel parking simulation. Comment: Update some typos and references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.13112 Zobrazit plný text záznamu View this record from Arxiv