Solution of the Probabilistic Lambert's Problem: Optimal Transport Approach
<|reference_start|>Solution of the Probabilistic Lambert's Problem: Optimal Transport Approach: The deterministic variant of the Lambert's problem was posed by Lambert in the 18th century and its solution for conic trajectory has been derived by many, including Euler, Lambert, Lagrange, Laplace, Gauss and Legendre. The solution amounts to designing velocity control for steering a spacecraft from a given initial to a given terminal position subject to gravitational potential and flight time constraints. In recent years, a probabilistic variant of the Lambert's problem has received attention in the aerospace community where the endpoint position constraints are softened to endpoint joint probability distributions over the respective positions. Such probabilistic specifications account for the estimation errors, modeling uncertainties, etc. Building on a deterministic optimal control reformulation via analytical mechanics, we show that the probabilistic Lambert's problem is a generalized dynamic optimal mass transport problem where the gravitational potential plays the role of an additive state cost. This allows us to rigorously prove the existence-uniqueness of the solution for the probabilistic Lambert problem both with and without process noise. In the latter case, the problem and its solution correspond to a generalized Schr\"odinger bridge, much like how classical Schrodinger bridge can be seen as stochastic regularization of the optimal mass transport. We deduce the large deviation principle enjoyed by the Lambertian Schr\"odinger bridge. Leveraging these newfound connections, we design a computational algorithm to illustrate the nonparametric numerical solution of the probabilistic Lambert's problem.<|reference_end|>
@article{teter2024solution,
    title={Solution of the Probabilistic Lambert's Problem: Optimal Transport
  Approach},
    author={Alexis M.H. Teter, Iman Nodozi, Abhishek Halder},
    journal={arXiv preprint arXiv:2402.01209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01209},
    primaryClass={math.OC cs.SY eess.SY}
}