摘要
近几十年,渐近最优快速搜索随机树(RRT*)算法受到广泛关注。为了解决其收敛速度慢、生成路径代价高的问题,提出一种改进APF(Artificial Potential Field)-Informed-RRT*融合算法进行无人机航迹规划。该算法结合Informed采样策略,将随机点约束在椭圆空间内,提高搜索效率。当新算法找到最近节点后,引入改进APF生成高质量的新节点。目标点及随机采样点对生长树的最近节点产生吸引力,障碍物对其产生排斥力,然后将合力方向作为随机树生长方向,解决局部最小值的问题,大大缩短了收敛时间。将该算法与RRT*,Informed-RRT*算法进行比较,结果表明了新算法的优越性和有效性。
In recent decades,the asymptotically optimal Rapidly-exploring Random Tree*(RRT*)algorithm has attracted extensive attention.In order to solve the problems of slow convergence and high costs of generating path,an improved APF-Informed-RRT*fusion algorithm for UAV trajectory planning is proposed.In combination with the Informed sampling strategy,the algorithm constrains the random points in the elliptic space,and improves the search efficiency.After the new algorithm finds the nearest node,the improved APF is introduced to generate high-quality new nodes.The target point and random sampling point are attractive to the nearest node of the growing tree,and the obstacle is repulsive to it.Then,the direction of resultant force is taken as the growth direction of the random tree to solve the problem of local minimum value and greatly decrease the convergence time.Compared with RRT*and Informed-RRT*,the new algorithm is superior and effective.
作者
盛春红
范珈铭
SHENG Chunhong;FAN Jiaming(Shenyang Institute of Science and Technology,Shenyang 110000,China;Shenyang Aerospace University,Shenyang 110000,China)
出处
《电光与控制》
CSCD
北大核心
2023年第6期1-7,共7页
Electronics Optics & Control
基金
国家自然科学基金(61973222,61906125)。
关键词
快速搜索随机树
Informed采样策略
人工势场
航迹规划
Rapidly-exploring Random Tree(RRT)
Informed sampling strategy
Artificial Potential Field(APF)
trajectory planning
