Full Text:   <150>

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CLC number: TP11

On-line Access: 2020-05-18

Received: 2019-11-30

Revision Accepted: 2020-03-08

Crosschecked: 2020-03-31

Cited: 0

Clicked: 329

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yan Shao

https://orcid.org/0000-0003-2522-4951

Zhi-feng Zhao

https://orcid.org/0000-0002-5479-7890

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.5 P.796-808

10.1631/FITEE.1900659


Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments


Author(s):  Yan Shao, Zhi-feng Zhao, Rong-peng Li, Yu-geng Zhou

Affiliation(s):  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   shaoy@zju.edu.cn, zhaozf@zhejianglab.com, lirongpeng@zju.edu.cn, yugeng.zhou@wfjyjt.com

Key Words:  Collective intelligence, Digital pheromones, Artificial potential field, Navigation algorithm


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Yan Shao, Zhi-feng Zhao, Rong-peng Li, Yu-geng Zhou. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 796-808.

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Abstract: 
Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism, by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals’ probabilistic behavioral decision-making schemes. Also, inspired by the flocking model in nature, considering the limitations of some individuals in perception and communication, we design a navigation algorithm model on top of Olfati-Saber’s algorithm for flocking control, by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage, detection and revisit efficiency, and the capability of obstacle avoidance.

基于数字信息素和领航算法的未知环境多智能体目标探测

邵燕1,赵志峰1,2,李荣鹏1,周裕庚3
1浙江大学信息与电子工程学院,中国杭州市,310027
2之江实验室,中国杭州市,311121
3浙江万丰科技开发股份有限公司,中国绍兴市,312000

摘要:在复杂且动态性强的环境中,指导多无人机系统协调运作是一项具有挑战性的技术。基于数字信息素和当前主流无人系统控制算法,提出一种有限先验知识下多无人机系统目标探测分布式算法。通过改进不同语义数字信息素的融合算法和个体行为决策方案,提出一种更合理、有效的信息素更新机制。同时,考虑到一些个体在感知和交流方面的局限性,以及受自然界蜂拥算法启发,在Olfati-Saber无人机群控制算法基础上,设计了新的领航算法模型。此外,使用矢量信息代替传统标量信息素,使无人机群具有更高探测效率。仿真结果表明,该算法在指定区域的探测覆盖率、目标获取及回访效率、避障能力等方面都有较好表现。

关键词:群体智能;数字信息素;人工势场;领航算法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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