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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Yan Shao


Zhi-feng Zhao


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


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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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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