基于优化快速搜索随机树算法的全局路径规划

The Global Path Planning Algorithm Based on Optimization RRT Algorithm

杨炜 ¹谭亮 ¹孙雪 ¹杜亚峰 ¹周晓冰²

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作者信息

1. 长安大学,西安 710064
2. 长安大学,西安 710064;一汽解放汽车有限公司商用车开发院,长春 130011
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摘要

为了改善传统快速搜索随机树(RRT)算法在全局路径规划中存在的平滑度差、具有潜在碰撞性等问题,提出了一种双重优化的RRT算法.在传统RRT算法基础上,引入自适应目标偏向策略以缩短采样时间,引入角度约束采样策略以适应车辆极限转角.得到初始路径后,建立二项优化函数(即降低路径曲率和远离障碍物),并将其作为基点进行梯度下降二次优化,生成可供车辆行驶、平滑性良好且碰撞概率低的路径,并进行仿真验证.结果表明:优化RRT算法相比于传统RRT算法、RRT-Connect算法和RRT*算法,平均曲率分别降低了 38.1％、36.4％和24.7％,曲率均方差分别降低了 38.4％、38.4％和 27.2％.

Abstract

In order to improve the shortcomings of poor smoothness and potential collision in traditional Rapidly-exploring Random Tree(RRT)algorithm for global path planning,the paper proposed a dual-optimization RRT algorithm.Based on the traditional RRT algorithm,an adaptive target bias strategy was introduced to shorten the sampling time,and an angle-constrained sampling strategy was introduced to adapt to the vehicle's maximum steering angle.After the initial path was obtained,a binary optimization function(reducing path curvature and avoiding obstacles)was established and used as a basis for gradient descent secondary optimization,generating a path that can be driven by vehicles with good smoothness and low collision probability,which was then simulated and verified.The results show that compared with RRT algorithm,RRT-Connect algorithm and RRT*algorithm,the optimized RRT algorithm reduces average curvature by 38.1％,36.4％and 24.7％,respectively;while reducing curvature variance by 38.4％,38.4％and 27.2％,respectively.

关键词

快速搜索随机树/全局路径规划/避障/梯度下降法

Key words

Rapidly-exploring Random Tree(RRT)/Global path planning/Obstacle avoidance/Gradient descent method

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基金项目

国家重点研发计划(2021YFE0203600)

陕西省自然科学基金青年基金(2017JQ6045)

出版年

2024

汽车技术

中国汽车工程学会长春汽车研究所

汽车技术

CSTPCDCSCD北大核心

影响因子：0.522

ISSN：1000-3703

被引量1

参考文献量14

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