Trade-Offs in Robust Trajectory Optimization Based on Sensitivity Minimization

Rapid developments in aerospace technologies demand reliable procedures to plan robust missions with high safety. To increase safety under uncertainties in model parameters or environmental conditions, multi-objective robust optimization methods via sensitivity minimization can be used. An acceptable trade-off between a nominal operational cost (e.g., time, energy) and robustness is searched for to plan missions that are less prone to disturbances. The presented analysis considers open-loop and closed-loop sensitivity minimization approaches and utilizes multi-objective optimization to assess the performance and the limitations of both approaches. To solve the multi-objective optimization problems, scalarization techniques are employed using weighted sums and cost bounds. By varying weights and cost bounds, multiple optima can be calculated, resulting in an approximate Pareto front and giving rise to an overview of the trade-off between optimality and robustness of the solutions. The analysis is performed for robust unmanned aerial vehicle (UAV) trajectory optimization minimizing positional sensitivities.