Multi-Agentic AI Framework For Autonomous Transmission Line Corridor Planning Using Lidar, Remote Sensing and Multi-Objective Optimization

Abstract

The continuous expansion of high-voltage power grids necessitates the development of transmission line corridors that perfectly balance engineering feasibility, economic viability, and ecological preservation. Traditional routing methodologies heavily depend on manual geographic information system operations and subjective expert judgments, which frequently result in suboptimal paths over extensive distances. This research investigates whether specialized artificial intelligence agents can collaborate to autonomously generate safer, more cost-effective, and environmentally sustainable transmission corridors. A novel Multi-Agentic AI Framework was developed and executed on a complex spatial dataset, deploying six specialized agents tasked with terrain intelligence, land-cover classification, environmental risk assessment, engineering feasibility, multi-objective optimization, and critical route evaluation. High-density Light Detection and Ranging data and high-resolution multispectral imagery were processed by these agents to identify optimal paths autonomously. The land-cover intelligence agent utilized a recurrent residual U-Net architecture to precisely classify spatial features, while the critic agent iteratively challenged the optimization outputs to refine the routes based on micro-level hazard detection. The implemented framework was evaluated against conventional least-cost path analysis and single-agent optimization techniques. The result analysis indicates significant improvements across multiple parameters, demonstrating a substantial reduction in capital expenditure and environmental impact while maximizing route safety. The autonomous collaboration of specialized agents provides a robust, scalable solution for modern infrastructure planning, entirely replacing hypothetical estimations with precise, data-driven spatial intelligence.