Multi-agent task allocation method based on income maximization

Abstract

The invention discloses a multi-agent cooperative task assignment method based on income maximization under the condition of considering the difference of agents. The method takes a set as a model; for a defense penetration task and a detection task, an index value of defense penetration efficiency evaluation is provided, how to effectively realize detection of the area is provided, a task allocation function model is established based on constraint conditions, task selection scheme solving based on a genetic algorithm is performed on a target function under the constraint conditions, and an optimal solution set can be obtained. In a complex battlefield environment, the tasks are quickly allocated according to the defense penetration capability and detection capability of the unmanned aerial vehicle and the task target condition, so that the income is maximized.

Description

technical field [0001] The invention belongs to a multi-task distribution method, in particular to a multi-agent cooperative task distribution method. Background technique [0002] With the growth of robotic agent applications such as drones, heterogeneous networked agent formations are widely used in different types of autonomous missions. Coordination and cooperation among different agents in the formation are crucial to the successful completion of the mission, and the cooperative task assignment and planning method of heterogeneous networked formation autonomy is the basis for realizing this goal. However, with the increase in the number of systems, components, and mission tasks, mission planning for large-scale formations becomes extremely complex. [0003] In the process of UAV penetration, for medium and long-endurance UAVs, they have to fly over various landforms in the process of reaching the target point, and encounter multiple threats from the enemy's air defense...

AI Technical Summary

Problems solved by technology

Pellazar Miles_lo and Ioannis K.Nikolos respectively adopted the evolutionary algorithm based on genetic algorithm to solve the problem of flight path planning, both of which have relatively time-consuming problems

Iris Hong Yang and James Doebble respectively studied the real-time trajectory planning problem of avoiding terrain obstacles, but this method did not consider the influence of other external threats; in the ant colony algorithm, the ant colony only considered fixed threats when searching for the route for the first time , without any other prior information, the route has great randomness, resulting in a small number of ants that can reach the target point, and it takes a long time to complete all searches to form a stable optimal route

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