Greedy K-mean self-organizing neural network multi-robot path planning method

Abstract

The invention relates to a greedy K-mean self-organizing neural network multi-robot path planning method, and belongs to the technical field of artificial intelligence and robot system control. According to the method, the task allocation problem and the path planning problem are considered together, a two-stage solving mode is adopted, robot task allocation is completed in the first stage, and path planning is conducted in the second stage according to the task allocation result in the first stage. In a K-mean iteration process, a greedy algorithm is used to estimate path cost required for task execution by each robot, the size of an adjustment factor is guided through the path cost, and a task allocation result in a clustering process is adjusted through the adjustment factor. The problem of unbalanced robot load of a K-means clustering algorithm and a self-organizing neural network algorithm is solved, a task execution path scheme of load balancing is efficiently planned for each robot, and the method has the advantages of being high in universality and robustness.

Description

Technical field [0001] The present invention relates to a robot path planning method, and specifically, a plurality of robotic path planning methods of greed K-average self-organizing neural network, belonging to artificial intelligence and robotic system control technology. Background technique [0002] Since the 20th century, industrial robots have gradually entered the universal period, growing in an amazing speed in a global scale and rapidly updating iteration with high speed, high precision, system integration, and intelligence. [0003] The path planning of multi-independent robots is always one of the important issues in the field of robots. The core objective of the mobile robot system path plan is that each robot within the system has a specific discrete start position, task point location, and end point position, which can plan from the starting position from the starting point, and experience each item. After the task, finally reached the path to the end position. [...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the technical problem that the existing self-organizing neural network cannot solve the multi-robot path planning. The path planning does not consider whether the load of each robot in the multi-robot system is balanced, which eventually leads to the technical problem that the overall operation efficiency of the multi-robot system is low. A greedy K-means self-organizing neural network multi-robot path planning method is creatively proposed. By improving Self-organizing neural networks, enabling them to be used to solve multi-robot path planning problems

At the same time, the greedy K-means self-organizing neural network path planning algorithm is proposed for the first time, which can effectively solve the load imbalance problem of multiple robots

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