Multi-intelligent robot task distribution method facing dynamic task

Abstract

The present invention provides a multi-intelligent robot task distribution method facing a dynamic task which mainly solves the multi-task distribution problem of a task state quantity with time-variant characteristics. The method comprises the steps of: obtaining dynamic task feature parameters, combining intelligent robot ability parameters, and establishing a feature equation of a task point state quantity; according to the feature equation, designing an intelligent robot revenue function; according to the revenue function, designing a genetic algorithm fitness function; further designing agenetic algorithm difference selection operator and a local mutation operator, and providing an algorithm repair strategy; and finally, employing the genetic algorithm to generate an intelligent robot task distribution scheme to complete multi-task distribution. The multi-intelligent robot task distribution method takes obtaining of system maximum return as a target to achieve dynamic multi-taskrapid distribution, solve the algorithm chromosome deadlock problem and avoid that search falls into local optimum, and through a multi-stage distribution strategy, the method can fully deploy intelligent robots in the system to participate in task completion so as to improve the whole efficiency of the system.

Description

technical field [0001] The invention relates to the technical field of intelligent robot task allocation algorithms, and more particularly, relates to a dynamic task-oriented multi-intelligent robot task allocation method. Background technique [0002] In recent years, with the deepening of people's understanding of artificial intelligence and complex systems, the research in the field of intelligent multi-robot systems has achieved rich results in both theory and practical systems. Intelligent robots can replace humans to help us in some harsh environments. To complete certain tasks, such as aerospace, deep sea exploration, expedition and disaster relief, etc., these environments are often accompanied by toxic, anaerobic, high temperature and high pressure, strong radiation and other substances harmful to the human body, which exceed the limit that humans can bear. [0003] In the current multi-robot system, the number of tasks is large and the tasks change dynamically. Whe...

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Problems solved by technology

[0003] In the current multi-robot system, the number of tasks faced is large and the tasks change dynamically. When the traditional genetic algorithm solves such dynamic task allocation problems, it will cause deadlocks, easily fall into local optimum, and cannot implement multiple allocations. It is a waste of intelligence. At the same time, it cannot meet the needs of real-time performance, which seriously affects the completion of tasks.

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