Journal Article

Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration

Alutaybi, Aarif, Hamrouni, Chafaa, Ouerfelli, Ghofrane

2025

Overview

This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments.

• Publisher: MDPI AG
• Subject: adaptive route optimization / Algorithms / Changing environments / Collaboration / Constraints / Decision making / Electric vehicle charging / Electric vehicles / Energy consumption / Energy efficiency / energy-efficient navigation / Error reduction / Machine learning / Mapping / multi-agent mapping / Multiagent systems / Navigation systems / neural radiance fields (NeRFs) / Obstacle avoidance / Optimization models / Path planning / Real time / real-time obstacle recognition / Sensors / Situational awareness / Tracking errors / Traffic flow / Unknown environments