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Multi-UAV Collaborative Dynamic Task Allocation Method Based on ISOM and Attention Mechanism
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DOI：10.1109/TVT.2023.3341878.png)
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The problem of task allocation of Unmanned aerial vehicle (UAV) systems is a hot issue in scientific research. Aiming at overcome the shortcomings of existing algorithms in terms of load balancing and execution efficiency, an algorithm Improved Self-Organizing Mapping (ISOM) is proposed in this paper. Firstly, considering the two factors of the UAV flight distance and the mission execution time, the load balance of the UAV is designed. The sum of the track lengths of multiple UAVs and the total time required by multiple UAVs to complete all the tasks in the task area are used to evaluate the quality of cooperative task assignment of multiple UAVs. Secondly, the learning rate and neighborhood function of nonlinear change are designed to ensure the stability and accelerate its convergence. With the increase of the number of iterations, the radius of the neighborhood gradually decreases, and the renewal range of the superior neighborhood is determined by the nonlinear function. Finally, in order to solve the problem of new tasks of UAV and UAV failure, the attention mechanism based on the ISOM algorithm is introduced. Different attention is allocated by the distance between the UAV and the mission points to ensure the missions can be fully executed. Compared with algorithms PSO-GA, Gurobi and ORTools, the time to complete the task is effectively reduced, respectively. At the same time, the algorithm is verified in a large task environment. When the number of tasks is 200 and the number of UAVs is 8, in the TSPLIB task set, the simulation results demonstrate the high efficiency and flexibility of the proposed algorithm.
Keyword:
Task analysis
Autonomous aerial vehicles
Resource management
Robots
Self-organizing feature maps
Optimization
Vehicle dynamics
Unmanned aerial vehicle (UAV) system
multiple task allocation
Improved Self-OrganizingMapping (ISOM)
emergencies
attention mechanism
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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