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Missions concerning small-body celestial objects are of growing interest due to the resources and information they can provide. Such missions require detailed information about the surface of the body for interactions, such as landing on the surface, as well as predicting the gravity field of the object. This work provides a means of optimizing the mission elements of trajectory and imaging target schedules so that the level of knowledge of the surface can be increased. The information required to increase one's knowledge of the surface is described as a set of conditions placed on the collection of images taken of each facet of the surface; these requirements constitute the concept of "coverage" and were provided by NASA. Currently, no comparable optimization capability exists. The trajectory optimization task is done using an adapted form of the Non-dominated Sorting Genetic Algorithm-2 (NSGA-2) in which the genetic mutation and recombination operators are replaced with operators inspired by a different Evolutionary Algorithm, Differential Evolution. Since small-body objects have irregular distributions of mass, this optimization accounts for this by using a higher fidelity gravity model; the expense of the calculation causing a significant increase in fitness evaluation time. The trajectory optimization uses the maximization of possible coverage (the coverage achieved is every surface element were targeted for imaging at every opportunity) and minimization of a time quantity that typifies covering less but doing so quickly as the primary optimization objectives with an additional ancillary objective which rewards the fulfillment of the individual aspects of coverage so as to better condition improvement in the first objective. The optimization of imaging schedules is handled using a less adapted version of NSGA-2 in which the base operations were only tailored slightly. This differs from the previous task in that limitation are placed on the imaging process; namely that the camera may only target a single surface element at each opportunity and is thus only able to observe the faces caught within the narrow field-of-view. This optimization trades the minimization of time objective and the ancillary objective for the minimization of the required rotational effort of the imaging spacecraft. Both works result in sets of solutions to their respective problems that capture the trade-space between the considered objectives. The last work detailed here examines the consequences of how velocity domains are phrased in space trajectory optimization problems. Multiple means of framing the optimization domain are examined and the results detail the complications encountered by the more common formulations for a set of test problems. |
