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Mobile robot path planning method based on improved genetic algorithm

A technology for improving genetic algorithms and mobile robots, applied in the field of mobile robot path planning, can solve problems such as low efficiency, slow convergence speed, easy to fall into local optimal values, etc., to overcome premature phenomenon, search speed, and efficiently solve path planning effect of the problem

Inactive Publication Date: 2019-09-24
GUIZHOU UNIV
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a mobile robot path planning method based on an improved genetic algorithm to solve the problem that the existing algorithm for mobile robot path planning in the prior art needs a large number of iterations to solve the optimal path, so the efficiency is low, The convergence speed is slow, and it is easy to fall into technical problems such as local optimum

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  • Mobile robot path planning method based on improved genetic algorithm
  • Mobile robot path planning method based on improved genetic algorithm
  • Mobile robot path planning method based on improved genetic algorithm

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[0052] Step 1. Environmental modeling: Preprocess the map using the grid method, and divide the environmental map into regular and uniform grids. Each grid has only two states, occupied or free. The grid in the occupied state represents obstacles, and the grid in the free state represents the walking area of ​​the robot.

[0053] Step 2. Set the start point and end point of the robot. The start point and end point must be in the free grid;

[0054] Step 3. Use an improved genetic algorithm to plan the optimal path of the robot. The improved algorithm includes:

[0055] Step 3.1. Set the population size M, the initial temperature parameter T=T 0 , The genetic algebra counter initializes gen=0, the maximum genetic algebra Maxgen=50, and the temperature termination parameter ε=0.1.

[0056] Step 3.2: Generate the initial population P(gen). Between the start point S and the end point G of the robot movement, a series of randomly selected, free, and not necessarily continuous grid numbers...

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Abstract

The invention discloses a mobile robot path planning method based on an improved genetic algorithm. The mobile robot path planning method comprises: step one, carrying out environment modeling; to be specific, preprocessing a map by using a grid method and dividing an environment map into regular and uniform grids; step two, setting a start point and an end point of a robot; to be specific, setting the locations of the start point and the end point into free grids; step three, planning an optimal path of the robot based on an improved genetic algorithm; and step four, smoothening the path, determining a path point in the optimal path generated based on the algorithm as a control point, and carrying out smoothing on the optimal path by using a cubic B-spline curve to generate a final path. Therefore, technical problems of low efficiency, slow convergence speed, and great easiness of trapping in a local optimal value because of lots of iteration for optimal path calculation for the algorithm according to the mobile robot path planning method in the prior art are solved.

Description

Technical field [0001] The invention belongs to the technical field of mobile robot path planning, and in particular relates to a mobile robot path planning method based on an improved genetic algorithm. Background technique [0002] With the continuous development of science and technology, navigation technology has become a research hotspot in the field of robotics, and path planning algorithms are an important guarantee for realizing mobile robot navigation. Path planning means that the robot searches for an optimal path from the starting point to the target point and avoiding obstacles in the environment according to certain criteria. Traditional path planning algorithms include artificial potential field method, simulated annealing algorithm, fuzzy control method, neural network algorithm, etc., but these algorithms are relatively complex and have defects in their application in real scenarios. Intelligent bionics algorithms such as genetic algorithm, ant colony algorithm, ...

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Application Information

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IPC IPC(8): G05D1/02G06N3/12
CPCG05D1/0217G06N3/126
Inventor 刘紫燕张杰白鹤万培佩袁磊
Owner GUIZHOU UNIV
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