Multi-neural network control planning method for robot path in intelligent environment

Abstract

The invention provides a multi-neural network control planning method for a robot path in an intelligent environment. The method comprises the steps that 1 a global map three-dimensional coordinate system is constructed for the carrying area of a carrier robot to acquire a walkable area coordinate in the global map three-dimensional coordinate system; 2 a training sample set is acquired; 3 the global static path planning model of the carrier robot is constructed; and 4 starting and ending coordinates in a transportation task are input into the global static path planning model based on a fuzzy neural network to acquire the corresponding optimal planning path for the carrier robot. According to the invention, the global static path planning model and a local dynamic obstacle avoidance planning model are separately established; the nonlinear fitting property of the neural network is used to find the global optimal solution quickly; and the problem of falling into a local optimum in common path planning is avoided.

Description

technical field [0001] The invention belongs to the field of robot path planning, in particular to a multi-neural network control planning method for robot paths in an intelligent environment. Background technique [0002] With the in-depth development of Made in China 2025, there are more and more applications of robots in smart environments such as smart medical care, smart factories, and smart laboratories. The degree of intelligence of robots determines the automation level of production and scientific research activities and the development level of productivity. Among them, the robot path planning problem is the key to improving the robot's carrying efficiency. How to efficiently and optimally solve the robot path planning problem has always been a difficult point in this field. [0003] Predecessors have proposed many excellent path planning methods, including early visualization method, artificial potential field method, grid method, and later ant colony algorithm, g...

AI Technical Summary

Problems solved by technology

For example, Chinese patent CN101604166B discloses a mobile robot path planning method based on particle swarm optimization algorithm. Converge early and fall into local optimum

Another example is Chinese patent CN105116902A, which discloses a method and system for obstacle avoidance navigation of a mobile robot. The A* algorithm used in it is the most effective direct search method in robot path planning. Excellent, the global optimal solution cannot be obtained

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