Multi-UAV Tasking and Coordination for Monitoring and Coverage of Agricultural Farmland

With global population growth accelerating and food demand rising, the need for efficient farmland surveying to maximize crop yields through precision agriculture has never been more critical. Unmanned Aerial Vehicles (UAVs) have emerged as capable tools due to their advanced sensor capabilities, high mobility, and ability to collect data with minimal risk to both farmers and crops. While single UAV platforms offer significant potential, their effectiveness remains limited by factors such as restricted battery life, limited flight duration, and high operational costs, among other aspects. This article addresses these limitations by introducing a sensing- and constraint-aware, cooperative coverage framework designed for agricultural surveying using a heterogeneous team of UAVs. Our approach accounts for both the physical constraints of UAVs and the user-defined data collection needs, while minimizing the total mission time and energy consumption. The framework begins by defining the Region of Interest (ROI) as a convex polygon, which is then decomposed into subregions based on the relative sensing and flight capabilities of each UAV. Each partition is assigned to a specific UAV based on its relative capability, and coverage paths are generated using an efficient back-and-forth sweeping strategy. A novel receding horizon Mixed Integer Linear Programming (MILP) technique is then applied to produce collision-free trajectories tailored to each UAV’s constraints. We validated the framework within the CoppeliaSim physics-based simulator, where results demonstrate the effective trajectory coordination and full ROI coverage, confirming the system’s adaptability and performance under realistic conditions. The derived solutions will provide an opportunity for farmers to regularly and efficiently survey agricultural fields to maximize crop yields.