Energy-Constrained UAV-UGV Cooperative Systems: A Bilevel Framework for Routing in Multi-Agent Teams

The integration of Unmanned Aerial Vehicles (UAVs) has revolutionized autonomous systems by enabling rapid, flexible, and high-resolution operations across domains such as logistics, disaster response, infrastructure inspection, and urban mobility. However, UAV deployment remains constrained by limited energy capacity and payload restrictions, hindering their effectiveness in long-duration or large-scale missions. This thesis addresses these limitations through a cooperative framework that integrates UAVs with Unmanned Ground Vehicles (UGVs), leveraging the complementary strengths of both platforms. We propose a bi-level optimization framework for energy-aware UAV-UGV cooperative routing. At the upper level, UAV recharging stops are determined using a minimum set cover formulation, guiding the UGV’s road-constrained routing, modeled as a Traveling Salesman Problem. At the lower level, UAV task allocation is solved using an Energy-Constrained Vehicle Routing Problem with Time Windows (E-VRPTW). The framework ensures efficient coordination between heterogeneous agents while minimizing mission time and energy consumption. Evaluation across multiple scenarios demonstrates a 10–30% reduction in mission time and a 15–50% reduction in energy usage compared to UGV-only baselines. To validate the framework, we developed a hardware testbed comprising a DJI Tello UAV and a Raspberry Pi-based UGV in a lab-scale environment. Using motion capture for localization and synchronized wireless communication, the UAV successfully landed on a moving UGV for recharging, completing its tasks under strict energy constraints. These results confirm the practical feasibility of UAV-UGV collaboration for persistent surveillance and emergency response missions.