Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach

Area of Science:

• Robotics and Autonomous Systems
• Network Engineering
• Artificial Intelligence

Background:

• Managing networks for firefighting unmanned ground vehicles (UGVs) in crowded areas presents significant challenges.
• Network peculiarities include large coverage areas, frequent connection failures, and dynamic topology changes.

Purpose of the Study:

• To enhance the communication network and navigation capabilities of self-driving firefighting UGVs.
• To determine optimal routing tracks to fire locations, improving response efficiency.

Main Methods:

• Implementation of a novel recommendation system (RS) model.
• Application of the ant colony optimization (ACO) technique, a swarm intelligence method.
• Focus on improving routing algorithms for vehicles ad-hoc networks (VANETs).

Main Results:

• The proposed model significantly enhances the navigation of firefighting UGVs towards fire regions.
• Achieved shortest possible routes, effectively avoiding closed roads and traffic accidents.
• Demonstrated improved performance in dynamic and challenging network conditions.

Conclusions:

• The ant colony optimization-based model offers a robust solution for managing firefighting UGV networks.
• This research contributes to the control and management of ad-hoc vehicle networks, V2X communication, and the Internet of Things (IoT).
• The findings support more effective and safer deployment of autonomous systems in emergency response scenarios.