Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree

被引:1
|
作者
Huang, Tai [1 ,2 ]
Fan, Kuangang [2 ,3 ,4 ]
Sun, Wen [1 ,2 ]
Li, Weichao [2 ,3 ]
Guo, Haoqi [2 ,3 ,4 ]
机构
[1] Jiangxi Univ Sci & Technol, Sch Mech & Elect Engn, Hongqi St 86, Ganzhou 341000, Peoples R China
[2] Jiangxi Univ Sci & Technol, Magnet Suspens Technol Key Lab Jiangxi Prov, Hongqi St 86, Ganzhou 341000, Peoples R China
[3] Jiangxi Univ Sci & Technol, Sch Elect Engn & Automat, Hongqi St 86, Ganzhou 341000, Peoples R China
[4] Natl Rare Earth Funct Mat Innovat Ctr, Huilong St 6, Ganzhou 341000, Peoples R China
基金
中国国家自然科学基金;
关键词
path planning; rapidly-exploring random tree; potential field; greedy strategy; root node iteration; unmanned aerial vehicles; ASTERISK;
D O I
10.3390/drones7050331
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.
引用
收藏
页数:18
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