Incorporating feedback predictions for optimized UAV attack mission planning

This study investigates Unmanned Aerial Vehicle (UAV) motion planning for ground attack missions involving enemy defenses. The UAV dynamics are modelled as a unicycle, linearized using dynamic extension and extended over a finite horizon as a piece-wise affine function. This is then formulated as a constrained, convex optimization problem in the form of Model Predictive Control (MPC) using closed-loop feedback predictions. Avoidance of enemy defenses is achieved using linear inequality constraints. The design is tested in a simulated ground attack scenario involving a layered enemy defense system using MATLAB. Preliminary results demonstrate the feasibility of using MPC to guide a UAV in ground attack missions involving complex enemy defenses.