Off-highway vehicle (OHV) activities in construction, mining, and large-scale agriculture frequently experience ineffective resource allocation, unnecessary movements, and high fuel consumption, resulting in heightened operational costs and environmental consequences. Motivated by natural swarm behavior, this paper suggests an innovative Swarm Intelligence (SI) framework that empowers autonomous OHV fleets to self-organize, dynamically allocate tasks, and optimize operations in real time through multi-agent coordination and AI-based decision-making.
The suggested system employs reinforcement learning algorithms, real-time sensor networks, and vehicle-to-vehicle (V2V) communication to enable OHVs to replicate the collective intelligence observed in ant colonies, bee swarms, and flocks of birds. Rather than adhering to pre-determined, inflexible schedules, vehicles will modify their routes and workloads adaptively, thereby ensuring minimal idle time, less redundant operations, and improved fuel efficiency. This method not only decreases carbon emissions and energy waste but also boosts fleet productivity, equipment durability, and safety in operations.
Key elements of the study include:
AI-Based Task Allocation: Vehicles evaluate worksite conditions and autonomously assign tasks based on real-time load balancing and energy efficiency.
Swarm-Based Path Optimization: OHVs adapt their routes dynamically using AI to circumvent bottlenecks and limit unnecessary movements, thus minimizing fuel consumption.
Collaborative Decision-Making: Vehicles exchange data to jointly establish the most efficient strategy for task execution, reducing idle engine time.
Real-World Implementation and Simulations: Computational modeling and case analyses evaluate fuel savings, CO₂ reduction, and gains in operational efficiency across mining and construction locations.
By incorporating Swarm Intelligence into OHV fleet management, this research offers a scalable, AI-driven approach to achieving sustainable, high-efficiency operations while reducing the carbon footprint of heavy industries. The results carry significant implications for the future development of autonomous industrial fleets, intelligent mining, and environmentally friendly construction efforts.