Robot path planning method based on improved genetic algorithm

Abstract

The invention provides a robot path planning method based on an improved genetic algorithm. The robot path planning method comprises the following steps: when an optimal path exists, a robot executes an inspection task according to the optimal path, or else, the task is executed according to an approximate optimal path. At the same time, the robot path planning method provided by the invention is used for calculating a shortest path between two random inspection points, then taking the shortest path as basic data of the genetic algorithm to calculate the optimal path for the robot to do the inspection task next time, and continuously iterating like this, so that the path planned by the robot is more and more intelligent, and the reliability and the navigation accuracy of the system are maximized; since the path planning adopts the specially optimized genetic algorithm, as time goes on, the number of robot inspection times is increased, the planned path may become more and more excellent.

Description

technical field [0001] The invention belongs to the technical field of robot path planning, and relates to a robot path planning method, in particular to a robot path planning method based on an improved genetic algorithm. Background technique [0002] With the development of industrialization, the application fields of robots are constantly expanding, and robots have been widely used in the power industry to replace manual line inspections. Realizing effective autonomous operation of multiple mobile robots, considering safe and effective application goals, realizing autonomous path planning, cooperative operation, and cooperative task completion have become the development direction of mobile robots. In recent years, many path planning methods have been studied by people. The main path planning methods can be divided into two categories - artificial intelligence methods and artificial potential field methods. The former mainly uses genetic algorithm, fuzzy logic control a...

AI Technical Summary

Problems solved by technology

The artificial potential field method is simple and convenient, the planning path is relatively smooth, and it is convenient for real-time control, but there are limitations in the global path planning, and there will be oscillation and stagnation; the grid method is simple and flexible, but it is not efficient when the environment is complex. High; genetic algorithm and neural network algorithm, the algorithm itself is relatively complex, and the real-time performance is slightly poor, but they are all global algorithms

The usual genetic algorithm cannot completely solve the problem of obtaining the optimal inspection path. When the inspection robot performs the inspection task, the planned inspection path cannot be inspected according to the optimal path, which increases the inspection time and leads to The execution efficiency of the robot is low, and it is necessary to design a new path planning algorithm to fundamentally solve the problem of calculating the optimal inspection path of the inspection robot

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