A multi-robot multi-region collaborative search task assignment method

Abstract

The invention provides a multi-robot multi-area cooperative search task allocation method, which comprises the following steps of when the number of robots is greater than or equal to the number of search areas, determining the priority of each search area according to the income matrix and allocating robots to each search area according to the priority sequence; for the robots which have not beenassigned the search area, determining the priority of each robot according to the income increasing matrix and assigning the priority according to the priority sequence; when the number of robots isless than the number of searching areas, determining the priority of each robot according to the income matrix and assigning the searching areas to each robot according to the priority sequenc; if thenumber Mc (N) of the searching area of the robot is not allocated, allocating the searching area according to the first case, otherwise further determining the priority of each robot according to theincome increase matrix and allocating according to the priority sequence. The method of the invention does not need to execute a large number of iterative and complex algorithms, and is suitable foroccasions requiring high real-time performance.

Description

technical field [0001] The invention belongs to the technical field of multi-robot system task assignment, and more specifically relates to a method for multi-robot multi-region collaborative search task assignment, which is used to realize efficient robot assignment for multi-robot multi-region collaborative search tasks. Background technique [0002] As a branch of distributed artificial intelligence, the problem of task assignment in multi-robot systems has attracted much attention. A multi-robot system is defined as a loosely coupled network composed of robots. Through interaction between robots, problems beyond the capabilities of a single robot can be solved; a multi-robot system has the characteristics of autonomy, distribution, and coordination, as well as self-learning and organizational capabilities. And reasoning ability, can quickly make adaptive adjustments according to the dynamic changes of the environment; robots can replace humans to perform a series of dang...

AI Technical Summary

Problems solved by technology

The above methods have their own advantages and disadvantages. For example, the integer programming method can clearly describe the essence of the problem, but it is difficult to guarantee real-time performance; the swarm intelligence method belonging to the intelligent optimization algorithm has a fast convergence speed in large-scale task allocation problems, but it is easy Trapped in local optimum; the method based on behavioral incentives has strong adaptive ability, but the distribution efficiency is low, and the method based on market mechanism has high distribution efficiency, but has a strong dependence on the communication state, etc.

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