Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm

Abstract

The invention provides a task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm, which belongs to the technical field of task allocation. The method comprises group searching, hunting model building and task allocation strategies. According to the invention, the conception of hunting experience obtained from an intensified learning method is introduced into a task allocation algorithm; the method can make dynamic adjustments to an initial task allocation scheme obtained from an auction algorithm so that the method can better adapt to a dynamic changing hunting environment and reduce the communication quantity and calculation quantity among systems. In the task allocation auction algorithm, the solutions for the cost function for a bidding robot are optimized, and the conception of task intensity is raised. As a result, task allocation efficiency for a multiple robots cooperating system is improved while cost is reduced.

Description

technical field [0001] The invention relates to the field of task allocation, in particular to a multi-robot cooperative capture task allocation method based on dynamic adjustment of task intensity. Background technique [0002] Since the birth of robots, great changes have taken place in the way of life and production in human society. As a very important branch of distributed artificial intelligence, multi-robot system has the characteristics of fault tolerance, strong robustness, distributed coordination and so on. Task allocation is an important basis for effectively utilizing the resources of a multi-robot system to give full play to the advantages of system performance. The prerequisite for maximizing the performance of a multi-robot system is optimal task allocation. However, in practice, the computational complexity of the optimal allocation algorithm increases exponentially with the problem size. When there is significant dynamic uncertainty in task allocation, the...

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Problems solved by technology

The prerequisite for maximizing the performance of a multi-robot system is optimal task allocation. However, in practice, the computational complexity of the optimal allocation algorithm increases exponentially with the problem size. When there is significant dynamic uncertainty in task allocation, the multi-robot It is increasingly difficult for the system to achieve optimal allocation, so the task size to achieve optimal allocation in an acceptable time is limited

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