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Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms

by Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 7
Year of Publication: 2024
Authors: Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta
10.5120/ijca2024923402

Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta . Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms. International Journal of Computer Applications. 186, 7 ( Feb 2024), 1-10. DOI=10.5120/ijca2024923402

@article{ 10.5120/ijca2024923402,
author = { Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta },
title = { Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 7 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number7/optimizing-robot-path-planning-in-2d-static-environments-using-ga-pso-and-aco-search-algorithms/ },
doi = { 10.5120/ijca2024923402 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-22T22:17:52+05:30
%A Santosh Shrestha
%A Pranith Varma Appani
%A Praveen Reddy Kota
%A Alaa Sheta
%T Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 7
%P 1-10
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Meta-heuristic search algorithms have shown great success in robot motion planning by designing collision-free paths in static and dynamic environments. Meta-heuristic search algorithms with suitable representation (i.e., encoding) can find the optimal path from a start to endpoint effectiveness via waypoints. The waypoints are random points generated in the search environment. In this research, we investigate the use of several meta-heuristic algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimizations (PSO), and Ant Colony Optimizations (ACO), to solve path planning problems in a two-dimensional static environment. The proposed path planning-based search enjoys a free robot passage in all possible space directions to operate in complex search spaces. Performance benchmarking is also carried out through simulation in various scenarios to determine and analyze the performance of each algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Path Planning Metaheuristic Search Algorithms Genetic Algorithms Particle Swarm Optimizations and Ant Colony Optimizations.

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