Full Text:   <4565>

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CLC number: TP11

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2020-03-31

Cited: 0

Clicked: 6910

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

http://doi.org/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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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.

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