Simulation-Based Analysis of A*, RRT*, Genetic Algorithm, and Ant Colony Optimization for Autonomous Robot Path Planning in Obstacle-Dense Maps

Omer Ba Faqas, Necati Aksoy, Oğur Mısır

2025

Abstract

This paper presents a comparative performance evaluation of four distinct path planning algorithms A*, Rapidly-exploring Random Tree Star (RRT*), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) for autonomous navigation in static, grid-based environments. We assessed each algorithm's efficacy based on path optimality, computational efficiency, and success rate across maps with varying obstacle densities. Empirical results show that the A* algorithm provides optimal paths with the lowest computation time in low-to-moderate complexity environments. RRT* demonstrates superior flexibility in more complex topologies, while the metaheuristic GA and ACO approaches can solve highly complex problems but at a significant computational cost and with high sensitivity to parameter tuning. These findings establish an environment-contingent framework for algorithm selection, underscoring the trade-off between path optimality and computational resources.

Paper Citation

in Harvard Style

Faqas O., Aksoy N. and Mısır O. (2025). Simulation-Based Analysis of A*, RRT*, Genetic Algorithm, and Ant Colony Optimization for Autonomous Robot Path Planning in Obstacle-Dense Maps. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 182-191. DOI: 10.5220/0014368000004848

in Bibtex Style

@conference{iceeecs25,
author={Omer Ba Faqas and Necati Aksoy and Oğur Mısır},
title={Simulation-Based Analysis of A*, RRT*, Genetic Algorithm, and Ant Colony Optimization for Autonomous Robot Path Planning in Obstacle-Dense Maps},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={182-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014368000004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Simulation-Based Analysis of A*, RRT*, Genetic Algorithm, and Ant Colony Optimization for Autonomous Robot Path Planning in Obstacle-Dense Maps
SN - 978-989-758-783-2
AU - Faqas O.
AU - Aksoy N.
AU - Mısır O.
PY - 2025
SP - 182
EP - 191
DO - 10.5220/0014368000004848
PB - SciTePress