Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In thes...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced robotic systems Vol. 16; no. 4; p. 172988141985908
Main Authors: Chen, Peng, Li, Qing, Zhang, Chao, Cui, Jiarui, Zhou, Hao
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.07.2019
Sage Publications Ltd
SAGE Publishing
Subjects:
Algorithms
Ant colony optimization
Chebyshev approximation
Coal mine wastes
Computer simulation
Deep sea
Hazards
Hybrid systems
Inspection
Iterative methods
Landfill
Landfills
Object oriented programming
Optimization
Parameters
Particle swarm optimization
Path planning
Robots
Strategy
Swarm intelligence
chaos
Landfill
robots
path planning
pheromone
PSO
ACO
iterations
ISSN:1729-8806, 1729-8814, 1729-8814
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1729-8806
1729-8814
1729-8814
DOI:10.1177/1729881419859083