Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR.

Area of Science:

• Robotics and Autonomous Systems
• Aerospace Engineering
• Military Operations Research

Background:

• Unmanned Aerial Vehicles (UAVs) are crucial for modern military reconnaissance, utilizing sensors like Electro-Optical/Infrared (EO/IR) and Synthetic Aperture Radars (SARs).
• Effective reconnaissance requires precise UAV trajectory planning to maximize data collection from ground targets while ensuring mission success and safety.

Purpose of the Study:

• To develop and present an algorithm for optimizing the flight trajectory of fixed-wing tactical UAVs for ground object recognition.
• To integrate threat assessment and sensor-specific requirements into the trajectory planning process for enhanced reconnaissance missions.

Main Methods:

• Decomposition of the trajectory planning problem into subproblems: reconnaissance flight method, initial threat-aware trajectory, and detailed maneuver planning.
• Formulation of the trajectory determination as a Mixed Integer Linear Problem (MILP) model.
• Application of optimal control algorithms and Dubin curves for trajectory refinement and real-world flight segment generation.

Main Results:

• A comprehensive algorithm for planning optimal UAV reconnaissance trajectories, considering sensor types, required data, and potential threats.
• Successful integration of mission planning components, including time constraints and maneuverability parameters.
• Demonstrated correction of determined trajectories using Dubin curves for practical flight path execution.

Conclusions:

• The proposed MILP-based approach effectively plans optimal UAV trajectories for reconnaissance missions.
• The algorithm enhances tactical UAV mission effectiveness by maximizing information acquisition and accounting for operational constraints and threats.
• This work contributes to the advancement of autonomous reconnaissance capabilities in military applications.