应用改进APF-Informed-RRT^(*)算法的配送无人机航迹规划  

Delivery UAV trajectory planning based on improved APF-Informed-RRT^(*)algorithm

作　　者：刘玉梦 任彦[1] 王静宇[2] 赵利云 王琦 单俊茹 LIU Yumeng;REN Yan;WANG Jingyu;ZHAO Liyun;WANG Qi;SHAN Junru(School of Automation and Electrical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;School of Digtial and Intelligent Industry,Inner Mongolia University of Science and Technology,Baotou 014010,China;School of Science,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学自动化与电气工程学院,内蒙古包头014010 [2]内蒙古科技大学数智产业学院,内蒙古包头014010 [3]内蒙古科技大学理学院,内蒙古包头014010

出　　处：《中国测试》2025年第1期173-180,共8页China Measurement & Test

基　　金：国家自然科学基金(62063027,62063025);内蒙古自治区自然科学基金(2023MS06001);内蒙古自治区高等学校青年科技英才支持计划资助项目(NJYT22057,NJYT22069);自治区直属高校基本科研业务费项目(2023RCTD028,039)。

摘　　要：为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT^(*)算法融合,提出一种自适应步长增长策略的改进APF-Informed-RRT^(*)算法。首先在选择新节点时,考虑到障碍物和目标点的影响,提出一种自适应步长增长策略来解决采样的盲目性;其次采用三次B样条对拐点处进行平滑处理;最后分别采用RRT^(*)算法、Informed-RRT^(*)算法和改进APF-Informed-RRT^(*)算法在两种环境中进行仿真实验。结果表明,改进APF-Informed-RRT^(*)算法相较于RRT^(*)算法和Informed-RRT^(*)算法,在运行时间、迭代次数以及路径平滑上都得到提升。In order to accelerate the efficiency of the last mile delivery,a trajectory planning problem for delivery UAV is proposed.Aiming at the problems of blindness and unsmooth paths in the traditional rapidlysearch random tree(RRT)algorithm in trajectory planning,an improved APF-Informed-RRT^(*)algorithm with an adaptive step growth strategy is proposed by integrating the artificial potential field(APF)method with the Informed-RRT^(*)algorithm.Firstly,an adaptive step growth strategy is proposed to solve the blindness of sampling by considering the influence of obstacles and the target point when selecting new nodes.Secondly,three times B-splines are used to smooth the inflection points.Finally,the RRT^(*)algorithm,the Informed-RRT^(*)algorithm and the improved algorithm are used to carry out simulation experiments in two environments respectively.The results show that the improved algorithm has been improved in terms of running time,number of iterations and path smoothing compared to the RRT^(*)algorithm and the Informed-RRT^(*)algorithm.

关 键 词：末端物流配送 航迹规划 人工势场法 Informed-RRT^(*)算法 

分 类 号：TB9[一般工业技术—计量学] TH242[机械工程—测试计量技术及仪器]