复杂环境下基于改进Informed RRT的无人机路径规划算法

TP242.2; 针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed RRT*算法.首先,引入人工势场法使采样点按照势场下降的方式向目标点移动,以提高RRT树扩展的目的性和方向性.然后,考虑随机树在扩展过程中全局环境的复杂度,引入自适应步长调整策略以增加随机树在无障碍环境下的扩展速度,并在随机树扩展的过程中加入相关约束条件以确保生成路径的可行性.在找到第一条可达路径后,采用变化的椭圆或椭球采样域限制采样点选取和自适应步长的扩展范围,加快算法收敛到渐进最优的速度.最后,在复杂二维...

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Published in上海交通大学学报 Vol. 58; no. 4; pp. 511 - 524
Main Authors 刘文倩, 单梁, 张伟龙, 刘成林, 马强
Format Journal Article
LanguageChinese
Published 南京理工大学自动化学院,南京 210094%江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122 01.04.2024
Subjects
自适应步长
Informed RRT(IRRT)
elliptic sampling domain
人工势场法
椭圆采样域
path planning
adaptive step size
artificial potential field(APF)method
路径规划
Informed RRT
Online AccessGet full text
ISSN1006-2467
DOI10.16183/j.cnki.jsjtu.2022.442

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Abstract TP242.2; 针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed RRT*算法.首先,引入人工势场法使采样点按照势场下降的方式向目标点移动,以提高RRT树扩展的目的性和方向性.然后,考虑随机树在扩展过程中全局环境的复杂度,引入自适应步长调整策略以增加随机树在无障碍环境下的扩展速度,并在随机树扩展的过程中加入相关约束条件以确保生成路径的可行性.在找到第一条可达路径后,采用变化的椭圆或椭球采样域限制采样点选取和自适应步长的扩展范围,加快算法收敛到渐进最优的速度.最后,在复杂二维和三维环境下进行传统算法和改进算法的对比实验,仿真分析表明:改进算法可以在很少的迭代次数下找到更优的初始路径,更快地锁定椭圆或椭球采样域,从而给路径优化留出更多时间,算法规划效果更好.
AbstractList TP242.2; 针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed RRT*算法.首先,引入人工势场法使采样点按照势场下降的方式向目标点移动,以提高RRT树扩展的目的性和方向性.然后,考虑随机树在扩展过程中全局环境的复杂度,引入自适应步长调整策略以增加随机树在无障碍环境下的扩展速度,并在随机树扩展的过程中加入相关约束条件以确保生成路径的可行性.在找到第一条可达路径后,采用变化的椭圆或椭球采样域限制采样点选取和自适应步长的扩展范围,加快算法收敛到渐进最优的速度.最后,在复杂二维和三维环境下进行传统算法和改进算法的对比实验,仿真分析表明:改进算法可以在很少的迭代次数下找到更优的初始路径,更快地锁定椭圆或椭球采样域,从而给路径优化留出更多时间,算法规划效果更好.
Abstract_FL To address the problems of long planning time,redundant planning path,and even planning failure caused by local constraints in narrow spaces in the rapid exploring random trees(RRT)algorithm when unmanned aerial vehicle is planning a path in a complex environment,an improved Informed RRT*algorithm is proposed.First,the artificial potential field(APF)method is used to make the sampling points move to the target point in the way of potential field descending,which improves the purpose and directionality of RRT tree expansion.Considering the complexity of the global environment during tree expansion,an adaptive step size is introduced to accelerate the expansion speed of the RRT tree in an unobstructed environment.Then,relevant constraints are added in the process of random tree expansion to ensure the feasibility of the generated paths.After the first reachable path is found,variable elliptic or ellipsoidal sampling domain is used to limit the selection of sampling points and the expansion range of adaptive step size,so as to accelerate the convergence of the algorithm to the asymptotic optimization.Finally,the original algorithm and the improved algorithm are compared in two-dimensional and three-dimensional complex environment.The simulation results show that the improved algorithm can find a better reachable path with a small number of iterations,lock the elliptic or ellipsoidal sampling domain faster and leave more time for path optimization.The improved algorithm performs better in path planning.
Author 刘文倩
张伟龙
马强
单梁
刘成林
AuthorAffiliation 南京理工大学自动化学院,南京 210094%江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122
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Author_FL ZHANG Weilong
LIU Wenqian
SHAN Liang
LIU Chenglin
MA Qiang
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DocumentTitle_FL Unmanned Aerial Vehicle Path Planning Algorithm Based on Improved Informed RRTin Complex Environment
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Keywords 自适应步长
Informed RRT(IRRT)
elliptic sampling domain
人工势场法
椭圆采样域
path planning
adaptive step size
artificial potential field(APF)method
路径规划
Informed RRT
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PublicationTitle 上海交通大学学报
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PublicationYear 2024
Publisher 南京理工大学自动化学院,南京 210094%江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122
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Snippet TP242.2; 针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed...
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StartPage 511
Title 复杂环境下基于改进Informed RRT的无人机路径规划算法
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