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Completed TRL 2 (started at 2, targeting 3)
Rendezvous and docking two spacecraft is a nontrivial matter that can have hugely expensive consequences when something goes wrong. The current approach in rendezvous missions is to assign the two spacecraft very specific roles, a non-maneuvering target and a maneuvering chaser. Having only the chaser vehicle maneuvering makes a great deal of sense when docking with a spacecraft in its final orbit, such as with the ISS or a satellite to be serviced. However situations exist, where expanding the solution space to allow both chaser and target to maneuver allows for added fuel efficiency and robustness. A relative navigation algorithm will be investigated to run on both spacecraft during rendezvous. The algorithm will be identical on both vehicles and the host spacecraft will treat itself as a chaser vehicle and the other as a target, estimating both its own state and the relative state of its target. Additionally, this algorithm will not require communication between spacecraft to perform its estimates. This navigation algorithm will be tested in a simulation for a variety of different mission configurations to ensure robustness. Ultimately, this algorithm will provide more options for rendezvous and docking and therefore increase the likelihood of success in rendezvous missions while simultaneously reducing the number of scenarios where a ground controller is needed to intervene to account for unforeseen situations. The key challenge tackled by the proposed work is the detection and estimation of maneuvers being performed by the other vehicle. Existing works treat the unmodeled acceleration from the unknown maneuvers as either purely deterministic (typically solving an optimization problem) or purely stochastic (as either white noise or a first order Gauss-Markov process). A deterministic maneuver approach is typically more accurate in estimating the actual maneuver direction and magnitude, while a stochastic approach is typically more robust as it only attempts to bound the uncertainty of the unknown dynamics. This approach will include both deterministic and stochastic components in a maneuver detection algorithm. The deterministic component of the maneuver is estimated via Maximum Likelihood adaptation, while the stochastic component via process noise estimation with correlation methods. The combination of two adaptation methods is, to the best of this researcher's knowledge, an unexplored research direction that could exploit the strengths of the two methods while removing their weaknesses. This proposal is applicable to a number of different objectives laid out in NASA's Road Map. This research aims to culminate in a reliable autonomous relative navigation algorithm for the purpose of autonomous rendezvous and docking, and thus supports TA 4.6.2 Guidance, Navigation, and Control (GNC) Algorithms. This proposal also addresses TA: 4.1.4: Object, Event, and Activity Recognition. Detection of off-nominal trajectories in both spacecraft is vitally important to the navigation algorithm proposed, whether the deviation is due to a maneuver or an unmodeled perturbation. TA 4.5.2 Activity Planning, Scheduling, and Execution is applicable to this research as the spacecraft will estimate each other's states and react accordingly to allow for a smooth rendezvous. TA: 5.4.2 Onboard Auto Navigation and Maneuver, seeks to reduce the dependence on ground-based involvement in state estimation (trajectory and attitude determination), maneuver planning, and support functions. Allowing both spacecraft involved in a rendezvous and docking scenario maneuver provides more options for the end goal of docking. Thus the work done here will provide a larger safety net in the event of an unforeseen perturbation, and increase the probability of success.
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