Method of path planning based on improved ant colony algorithm

An ant colony algorithm and path planning technology, applied in two-dimensional position/channel control, vehicle position/route/altitude control, non-electric variable control, etc. The problem of slow convergence speed of the swarm algorithm can shorten the convergence time of the algorithm, reduce the calculation time, and improve the operation efficiency.

Active Publication Date: 2017-10-20
SOUTHEAST UNIV
View PDF7 Cites 50 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the classical ant colony algorithm, the pheromone volatilization coefficient is a constant, and the newly searched optimal path follows the same pheromone volatilization criterion as the path searched in the previous round, but this crit

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method of path planning based on improved ant colony algorithm
  • Method of path planning based on improved ant colony algorithm
  • Method of path planning based on improved ant colony algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0030] figure 1 The flow chart of the path planning method of the present invention is shown. In the present invention, we use MATLAB simulation software to simulate and calculate, as figure 1 As shown, the path planning method based on the ant colony algorithm first performs environment modeling and initialization, and then performs search iterations. After each round of iteration is completed, the feasible path information is counted, and different path decision rules are applied to all feasible paths. The algorithm is improved to get the optimal path faster. The specific steps are described in detail below.

[0031] Step 1. Use the grid method to model the map environment, and set the start node, dest target node, obstacle and other grids.

[0032] Step 2. Initialize the basic parameters of the ant colony algorithm, and manually give th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for path planning based on an improved ant colony algorithm. Compared with the classical ant colony algorithm, the method has the following improvements: (1) a constant pheromone evaporation coefficient is adjusted to be an adaptive pheromone evaporation coefficient, and the size of the coefficient is changed adaptively along with increase in number of iterations of an ant colony method; a local optimum path is preferentially selected by adopting a rule that the number of inflection points is small on the basis that different paths are identical in length; (3) a path simplifying rule is adopted for the local optimum path, whether each passing node in the path and the starting node are neighboring nodes or not is judged, and redundant nodes on the path are eliminated; and (4) a pre-sorting rule is adopted when pheromone updating is performed on the path passing by an ant colony before, and only the top 1/3 of paths in path length sorting are updated. According to the above improvements, the method can effectively reduce the algorithm convergence time of the ant colony algorithm and improve the operating efficiency.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a path planning method based on an ant colony algorithm. Background technique [0002] Path planning is one of the current research hotspots. A path refers to a sequence of points or curves connecting the starting point and the end point. The strategy for forming a path is called path planning. The purpose of path planning is to enable mobile subjects (such as smart cars, mobile robots, drones, etc.) Avoid obstacles on the road with many obstacles, so as to choose an optimal path from the starting point to the end point. [0003] The research on path planning mainly focuses on the following three aspects: first, whether the moving subject can reach the end point smoothly from the starting point; second, whether the moving subject can automatically avoid obstacles along the way during driving; third, On the basis of completing the above two indicators, whether the mobile s...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G05D1/02
CPCG05D1/0276
Inventor 黄杰万弃寒卫锦朱仟曹山山闵溪青张云龙
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap