Clustering-based multi-robot task distributing method for use in exploiting tasks

Abstract

The invention discloses a clustering-based multi-robot task distributing method for use in exploiting tasks, which comprises a clustering structure generating process and an inter-cluster robot dynamic task distributing process, wherein the tree topology-based clustering structure generating process comprises the steps of: (1) judging a cluster structure, (2) determining conditions for forming a dynamic cluster structure according to an initial task and information acquired by a robot, and (3) setting task dynamic change-oriented cluster updating rules; and the biologically started inter-cluster robot task distribution process comprises the following steps of: (1) establishing the functions of the robot in the clustering cooperation research according to different task types, and (2) establishing an interaction mechanism in a multi-robot clustering mode to realize the inter-cluster task distribution of the robot. The method has the advantages that: the robot has high flexibility in a cooperation process; the design of a single robot is simplified, and the robustness of the system can be improved; and the distribution result can flexibly adapt to the change of environment.

Description

technical field [0001] The invention relates to a multi-robot control method, in particular to a multi-robot task allocation method based on clustering under an exploration task. Background technique [0002] Using a multi-robot system to collaboratively explore an unknown environment has many advantages compared with a single robot: First, the sensors of multiple robots have a wider field of view, and information redundancy is conducive to improving the positioning accuracy of the robot and the quality of map creation; second, the multi-robot system’s Fault tolerance prevents failure of tasks due to failure of a single robot; again, parallel processing capabilities of multi-robot systems allow specific tasks to be completed in less time. Finally, functional complementarity between multiple simple heterogeneous robots can achieve functions that cannot be achieved by a single complex robot. [0003] Experience has shown that in a complex environment, all robots exploring the...

AI Technical Summary

Problems solved by technology

[0007] In most of the current patents at home and abroad, there are few reports on multi-robot task allocation methods inspired by biological cooperative behavior. The only reports are to use group optimization strategies to achieve optimal allocation, and do not consider the locality of individual interactions.

However, most of the other task allocation methods adopt the negotiation method, which has complex design patterns, and the system flexibility and real-time performance are poor.

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