UAV Path Planning Based on Improved A* and DWA Algorithms

被引:61
作者
Bai, Xiong [1 ]
Jiang, Haikun [2 ]
Cui, Junjie [1 ]
Lu, Kuan [1 ]
Chen, Pengyun [1 ]
Zhang, Ming [3 ]
机构
[1] North Univ China, Sch Mechatron Engn, Taiyuan 030051, Peoples R China
[2] Shandong Prod Qual Inspect Res Inst, Jinan 250000, Peoples R China
[3] North Univ China, Sch Energy & Power Engn, Taiyuan 030051, Peoples R China
基金
中国国家自然科学基金;
关键词
Motion planning - Antennas;
D O I
10.1155/2021/4511252
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
This work proposes a path planning algorithm based on As and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A* algorithm is improved based on UAV's flight environment information and motion constraints. Further, the DWA algorithm's limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV's safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path's efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved As and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.
引用
收藏
页数:12
相关论文
共 24 条
[1]  
Attoyibi M.M., P 2 INT C VOC ED TRA, DOI [10.2991/icovet-18.2019.57, DOI 10.2991/ICOVET-18.2019.57]
[2]   Distributed Path Planning for Controlling a Fleet of UAVs : Application to a Team of Quadrotors [J].
Belkadi, A. ;
Abaunza, H. ;
Ciarletta, L. ;
Castillo, P. ;
Theilliol, D. .
IFAC PAPERSONLINE, 2017, 50 (01) :15983-15989
[3]   A novel UAV path planning algorithm to search for floating objects on the ocean surface based on object's trajectory prediction by regression [J].
Boulares, Mehrez ;
Barnawi, Ahmed .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2021, 135
[4]   Path Planning of Messenger UAV in Air-ground Coordination [J].
Ding Yulong ;
Xin Bin ;
Chen Jie ;
Fang Hao ;
Zhu Yangguang ;
Gao Guanqiang ;
Dou Lihua .
IFAC PAPERSONLINE, 2017, 50 (01) :8045-8051
[5]  
Feng D., 2021, LOCAL PATH PLANNING
[6]   A novel coordinated path planning method using k-degree smoothing for multi-UAVs [J].
Huang, Liwei ;
Qu, Hong ;
Ji, Peng ;
Liu, Xintong ;
Fan, Zhen .
APPLIED SOFT COMPUTING, 2016, 48 :182-192
[7]  
Huo L., 2021, SENSORS-BASEL, V21, P9
[8]  
Jeauneau V., 2018, IFAC PAPERSONLINE, V51, P292
[9]   Improvement and Fusion of A* Algorithm and Dynamic Window Approach Considering Complex Environmental Information [J].
Ji, Xianyou ;
Feng, Shuo ;
Han, Qidong ;
Yin, Huangfei ;
Yu, Shaowei .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2021, 46 (08) :7445-7459
[10]   Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning [J].
Kala, Rahul ;
Shukla, Anupam ;
Tiwari, Ritu .
ARTIFICIAL INTELLIGENCE REVIEW, 2010, 33 (04) :307-327