Design and Development of a Mobile-Based Application Path Planning System for Autonomous Electric Vehicles with Ant Colony Optimization Algorithm

Abstract

The rapid advancement of technology has generated numerous innovations across various domains, including transportation. One notable development is the autonomous vehicle, a driverless system capable of navigating to a designated destination without human intervention. This study emphasizes two critical aspects: navigation and efficient path planning. The objective is to design and develop a mobile application for optimal path planning based on the Ant Colony Optimization (ACO) algorithm.

 The application was developed using Visual Studio Code as the integrated development environment (IDE) and implemented under the waterfall software development model. The ACO algorithm served as the core mechanism for path determination, supported by the Google Maps API to provide spatial data required for processing. Additionally, Firebase was employed for user authentication—such as registration and login—and for storing trip history.

 Testing results indicate that the developed mobile application successfully operates according to its intended functions. In particular, the system demonstrates the capability to determine the shortest path effectively through the implementation of the Ant Colony Optimization algorithm. These findings suggest that the proposed approach can support advancements in autonomous vehicle navigation systems by offering efficient and reliable path planning solution