Abstract
The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-based allocating mechanism based on the auction process, including the design for three kinds of agents and the cooperation mechanism among different agents. Moreover, we proposed a new algorithm to calculate the bidding values of UAVs, by which the messages passed between UAVs can be reduced. On the basis of the assigning mechanism, we put up with a novel agent-based real-time task allocation algorithm named NECTAR for dynamic tasks in the UAV swarm. Furthermore, we conduct extensive experiments to evaluate the performance of our NECTAR, and the results indicate that NECTAR is able to solve the real-time task allocation for dynamic tasks and achieve high performance of the UAV swarm.
Highlights
With the rapid development of artificial intelligence, highend manufacturing, and robotics, the performance of Unmanned Aerial Vehicles (UAVs) is getting better and better
We put up with a novel agent-based task allocation algorithm based on an improved auction mechanism to achieve real-time task allocation for dynamic tasks in the UAV swarm. e proposed agent-based assigning mechanism can quickly respond to the dynamic tasks with little communication, and it is able to continuously assign because the agents collaborate with each other based on their local status
We mainly focus on the distributed task allocation method for the UAV swarm
Summary
With the rapid development of artificial intelligence, highend manufacturing, and robotics, the performance of Unmanned Aerial Vehicles (UAVs) is getting better and better. Due to the characteristics of tasks, the allocation method plays an essential role in obtaining high performance for the swarm. Lots of allocation algorithms for UAVs have been studied These algorithms do not sufficiently consider the characteristics of the UAV swarm, such as requirements of real-time response for dynamic tasks, lowbandwidth communication environment, and continuous task response. In order to select proper UAVs from the UAV swarm to undertake tasks and make the response time short, we Complexity comprehensively consider the characteristics of the UAV swarm, designing a novel agent-based assigning mechanism and, developing the auction-based task allocation algorithm for dynamic tasks. (iv) We put up with a novel intelligent task allocation method, named NECTAR, for dynamic tasks in the UAV swarm.
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