Application of Improved Ant Colony Algorithm Integrating Adaptive Parameter Configuration in Robot Mobile Path Design

Under the background of the continuous progress of Industry 4.0 reform, the market demand for mobile robots in major world economies is gradually increasing. In order to improve the mobile robot's movement path planning quality and obstacle avoidance ability, this research adjusted the node sel...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of advanced computer science & applications Jg. 14; H. 8
1. Verfasser: Han, Jinli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2023
Schlagworte:
Adaptive algorithms
Algorithms
Ant colony optimization
Construction planning
Convergence
Industrial applications
Industry 4.0
Obstacle avoidance
Path planning
Robot dynamics
Robots
Search algorithms
Transition probabilities
ISSN:2158-107X, 2156-5570
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Under the background of the continuous progress of Industry 4.0 reform, the market demand for mobile robots in major world economies is gradually increasing. In order to improve the mobile robot's movement path planning quality and obstacle avoidance ability, this research adjusted the node selection method, pheromone update mechanism, transition probability and volatility coefficient calculation method of the ant colony algorithm, and improved the search direction setting and cost estimation calculation method of the A* algorithm. Thus, a robot movement path planning model can be designed with respect to the improved ant colony algorithm and A* algorithm. The simulation experiment results on grid maps show that the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the Tianniu whisker search algorithm, and the particle swarm algorithm designed in this study converged after 8, 37, 23, and 26 iterations, respectively. The minimum path lengths after convergence were 13.24m, 17.82m, 16.24m, and 17.05m, respectively. When the edge length of the grid map is 100m, the minimum planning length and total moving time of the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the longicorn whisker search algorithm, and the particle swarm algorithm designed in this study are 49m, 104m, 75m, 93m and 49s, 142s, 93s, and 127s, respectively. This indicates that the model designed in this study can effectively shorten the mobile path and training time while completing mobile tasks. The results of this study have a certain reference value for optimizing the robot's movement mode and obstacle avoidance ability.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.0140844