Date of Award


Document Type


Degree Name

Master of Science (MS)


Automotive Engineering

Committee Chair/Advisor

Professor Beshah Ayalew

Committee Member

Professor Bing Li

Committee Member

Professor Jiangfeng Zhang


The growing demand for unmanned aerial vehicles (UAVs) is driven by their operational convenience, cost-effectiveness, availability, and adaptability to various scenarios. In energy-constrained environments, optimizing energy consumption and ensuring a continuous power supply for UAVs is crucial for mission success. The objective of this thesis is to address this issue by integrating energy-aware motion planning and charge scheduling for UAVs by utilizing a charger hosted on an unmanned ground vehicle (UGV), whose rendezvous locations and routes are jointly computed to minimize overall energy consumption.

This thesis proposes a hierarchical trajectory and control framework comprising local and global planners for each UAV in static and dynamic environments. The global planner considers a point mass model of UAV to generate a global path from any initial point to the goal point. It utilizes Dijkstra's algorithm based on a cost that combines energy and time. The local planner uses the global path information and a detailed model of the UAV that captures the UAV motion dynamics and handles static and dynamic obstacles.

Furthermore, charge scheduling for UAVs involves addressing the energy demand of UAVs through charging options. Traditional charging stations with fixed locations lack flexibility and optimal performance. To overcome this, an automatic charging scheduling system is introduced, utilizing UAVs-UGV coordination wherein the UGV serves as a mobile charging station. When UAVs enter a critical condition and require charging, a rendezvous location where UGV and UAVs meet will be determined automatically. Charging is achieved by docking the UAVs onto the UGV and swapping depleted batteries with charged ones. The rendezvous location and precedence of UAVs’ charging demands are determined through mixed integer programming. The UGV plans its routing between charging locations using a model predictive control (MPC)-based local motion planner to minimize time and control effort. Overall, the automatically determined charging locations provide time and energy-optimal UGV routing compared to baselines with predetermined charging locations.

Author ORCID Identifier

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