Trajectory Optimization for NASA Aerospace

In aerospace engineering, optimality is not defined by elegance on paper, but by whether a trajectory can be executed safely when reality intervenes.

Trajectory planning in aerospace is not merely an exercise in mathematics. Each and every path must comply with the laws of physics and remain feasible even when the real world fails to meet model assumptions. A path that cannot be followed is not optimal; it is useless.

The premise of my research work on trajectory optimization is based on this understanding. The research focuses on creating trajectories that are informed by constraints, dynamic, and feasible for practical aerospace-related decisions, such as those related to NASA-quality requirements. This is an effort that does not rely on creating optimal trajectories that are corrected at a later stage. Instead, feasibility and executability are embedded directly into the optimization process.

Such a strategy is essential in the context of aerospace, as uncertainties cannot be eliminated. Uncertainty is due to the following factors, all considered simultaneously: atmospheric variation, modeling errors, actuator constraints, and mission-level constraints. The trajectory-planning algorithms I have designed can operate under such conditions and are therefore safe and predictable. These algorithms have been validated through realistic simulations demonstrating robustness under combined uncertainties.

The characteristic feature of this type of work is the integration of planning and control. The trajectories are planned while accounting for the control actions, resulting in fewer aggressive control actions. The system-level approach aligns better with the aerospace design community’s views on reliability and understandability. This integration represents a shift from post-correction strategies to control-aware trajectory generation.

However, the relevance of this work is not limited only to mission scenarios. Robust trajectory optimization is key to achieving higher levels of autonomy, enabling the system to make intelligent decisions within physical and operational constraints.

Though these techniques are clearly applicable to autonomous vehicles and robotics, their rigor is grounded in aerospace practices. This effort stems from a fundamental conviction: trajectory optimization is more than just finding a route; it is about making these systems behave responsibly in the real world.

In aerospace, intelligence is not measured by the cleverness of a plan on paper, but by the reliability of the plan to fly. The novelty and success of this work lie in translating optimization theory into trajectories that are operationally dependable by design.