Artificial Intelligence Based Navigation in Quasi Structured Environment

TL;DR

This paper compares various algorithms for transportation route planning in quasi-structured environments, proposing a combined Floyd-Warshall and ACO approach that outperforms others in efficiency.

Contribution

It introduces a novel combination of Floyd-Warshall and ACO algorithms optimized for quasi-structured environments, demonstrating improved performance.

Findings

Floyd-Warshall and ACO are effective for route planning.

The combined Floyd-Warshall and ACO algorithm shows reduced time complexity.

Proposed method is suitable for real-time transportation applications.

Abstract

The proper planning of different types of public transportation such as metro, highway, waterways, and so on, can increase the efficiency, reduce the congestion and improve the safety of the country. There are certain challenges associated with route planning, such as high cost of implementation, need for adequate resource & infrastructure and resistance to change. The goal of this research is to examine the working, applications, complexity factors, advantages & disadvantages of Floyd- Warshall, Bellman-Ford, Johnson, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), & Grey Wolf Optimizer (GWO), to find the best choice for the above application. In this paper, comparative analysis of above-mentioned algorithms is presented. The Floyd-Warshall method and ACO algorithm are chosen based on the comparisons. Also, a combination of modified Floyd-Warshall with ACO algorithm is proposed. The proposed algorithm showed better results with less time complexity, when applied on randomly structured points within a boundary called quasi-structured points. In addition, this paper also discusses the future works of integrating Floyd-Warshall with ACO to develop a real-time model for overcoming above mentioned-challenges during transportation route planning.