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 controlli...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.展开更多
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)+2 种基金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)。
文摘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.
