摘要
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.
基金
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.2019C01002and 2019C03131)
the Pro ject sponsored by Zhejiang Lab(No.2019LC0AB01)
the Zhejiang Provincial Natural Science Foundation of China(No.LY20F010016)。