A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems

被引：208

作者：

Li, Jiangiang ^{[1
]}

Deng, Genqiang ^{[1
]}

Luo, Chengwen ^{[1
]}

Lin, Qiuzhen ^{[1
]}

Yan, Qiao ^{[1
]}

Ming, Zhong ^{[1
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Guangdong, Peoples R China

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2016年 / 65卷 / 12期

基金：

美国国家科学基金会;

关键词：

Hybrid path planning; obstacle identification; unmanned air vehicle; unmanned ground vehicle; UAV; SEARCH;

D O I：

10.1109/TVT.2016.2623666

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.

引用

页码：9585 / 9596

页数：12

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