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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Yan SHAO conducted most of research simulations and analyses, while Zhi-feng ZHAO pointed out the key research directions. Yan SHAO drafted the manuscript. Rong-peng LI helped organize the manuscript. Yan SHAO, Zhi-feng ZHAO, Rong-peng LI, and Yu-geng ZHOU revised and finalized the paper.
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Yan SHAO, Zhi-feng ZHAO, Rong-peng LI, and Yu-geng ZHOU declare that they have no conflict of interest.
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Project supported by the National Key R&D Program of China (No. 2017YFB1301003), the National Natural Science Foundation of China (Nos. 61701439 and 61731002), the Zhejiang Key Research and Development Plan (Nos. 2019C01002 and 2019C03131), the Project sponsored by Zhejiang Lab (No. 2019LC0AB01), and the Zhejiang Provincial Natural Science Foundation of China (No. LY20F010016)
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Shao, Y., Zhao, Zf., Li, Rp. et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments. Front Inform Technol Electron Eng 21, 796–808 (2020). https://doi.org/10.1631/FITEE.1900659
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DOI: https://doi.org/10.1631/FITEE.1900659