[1]杨 帆,徐 彪. 多障碍物环境下智能车辆局部路径规划方法[J].控制与信息技术(原大功率变流技术),2019,(05):1.[doi:10.13889/j.issn.2096-5427.2019.05.200]
 YANG Fan,XU Biao. A Local Path Planning Method for Intelligent Vehicle in Multi-obstacle Environment[J].High Power Converter Technology,2019,(05):1.[doi:10.13889/j.issn.2096-5427.2019.05.200]
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 多障碍物环境下智能车辆局部路径规划方法()
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《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年05期
页码:
1
栏目:
出版日期:
2019-10-05

文章信息/Info

Title:
 A Local Path Planning Method for Intelligent Vehicle in Multi-obstacle Environment
作者:
 杨 帆徐 彪
 (湖南大学 机械与运载工程学院,湖南 长沙 410082)
Author(s):
 YANG Fan XU Biao
 ( College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan 410082, China )
关键词:
 局部路径规划 静态障碍物 结构化道路 A* 算法 离散方法
Keywords:
 local path planning static obstacle structured road A* algorithm discrete method
分类号:
TM912
DOI:
10.13889/j.issn.2096-5427.2019.05.200
文献标志码:
A
摘要:
 在多障碍物的结构化道路场景下,智能车辆需要在满足道路边界约束的同时实现连续避障。在给定全局参考路径的前提下,针对智能车辆在多障碍物环境下的局部路径规划问题,文章通过坐标系转换将规划问题转换至曲线坐标系下进行;建立相邻障碍物聚合模型以简化障碍物环境;采用离散方法生成障碍物路段的离散路径,并使用改进的A* 算法搜索得到连续避障的离散路径;最后通过三次样条插值曲线拟合离散路径得到实现连续避障的连续路径。仿真结果表明,在多障碍物环境下,该方法能有效生成无碰撞的连续避障局部路径,同时具有较好地平顺性。
Abstract:
 In the scene of structured road with multiple obstacles, intelligent vehicles need to meet the road boundary constraints and realize continuous obstacle avoidance at the same time. Under the premise of given global reference path, in view of the local path planning problem of intelligent vehicle under the obstacle environment, it converted the path planning problem to curve coordinates through coordinate system transformation. Adjacent aggregation models were built to simplify the obstacle environment, a discrete method was established to generate the discrete path of obstacles, and an improved A * algorithm search method was used to obtain obstacle avoidance of discrete paths. Finally, continuous paths for obstacle avoidance can be realized by cubic spline interpolation curve fitting of discrete path. Simulation results show that the proposed method can effectively generate a collision-free local obstacle avoidance path in the multipleobstacle environment, and has good smoothness.

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期:2019-08-12
作者简介:杨帆(1995—),男,硕士研究生,主要研究方向为智能车辆路径规划;
徐彪(1992—),男,硕士,主要研究方向为车辆动力学与控制、车车/ 车路协同控制、智能车辆决策。
基金项目: 汽车车身先进设计制造国家重点实验室重点项目(61775006)
更新日期/Last Update: 2019-09-28