UAV Trajectory Optimization

UAV trajectory optimization chooses where and how an unmanned aerial vehicle flies while accounting for vehicle dynamics, obstacles, and mission goals such as coverage, low energy use, or reliable communication. Common approaches include optimal control and nonlinear model predictive control, differential-flatness and polynomial planning, graph and sampling-based search, and reinforcement learning for complex or multi-UAV settings. Many methods combine planning with sensing, battery, wind, or communication models, and use real-time replanning to respond to changing conditions. A central challenge is producing plans that are both computationally fast and reliably executable despite uncertain perception, disturbances, and differences between models and real flight.