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Relative Navigation of Multiple Spacecraft during Proximity Maneuvers

This thesis presents a novel spacecraft relative and autonomous navigation scheme, based on an adaptive tracking technique. In particular, the Modified Input Estimation (MIE) logic is here employed as robust filtering methodology, allowing each spacecraft in the system to estimate relative state vectors of others and their maneuvers. The need of robust estimation can be justified by the employment of low frequency update sensors, such as cameras, requiring image processing on small micro-computers, typically found on nano-satellites. Furthermore, signal loss and/or darkening of the sensors, frequently occur in space. Preliminary theoretical developments for the three-degrees-of-freedom planar case are presented in this work, and laboratory experimentation shows the capability of the adaptive technique to cope with low frequency measurements updates. In the second part an orbit application will be described with really good simulation results, demonstrating the generality of the estimation scheme. The proposed approach is validated via hardware-in-the-loop experimentation, using four spacecraft simulators at the Spacecraft Robotics Laboratory, forcing a low frequency update of relative measurements and very frequent signal loss and/or darkening of the sensors, where the information of the other vehicles maneuvers is not available. In particular, a four spacecraft simulator assembly maneuver is used as baseline, where reliable relative estimation is needed. Three experimental runs are presented: one showing how classical Kalman Filter based navigation algorithm, fails, and the second and third one successfully completing the two and four simulator assembly, thanks to the modified input estimation approach. Development and evaluation of the on-orbit navigation algorithm, was supported by the realization of a SIMULINK navigation and control algorithm (Appendix).

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3 I. INTRODUCTION A number of d ifferent a pproaches to t he m aneuvering t arget p roblem have appeared in recent l iterature [ 5].[8],[12],[14],[17],[18],[19],[20] a nd [21]. Modeling a system a ccurately, w hen u sing a K alman f ilter f or t arget t racking, i s on e of the m ost important pr oblems. If the s ystem model is not accurate, track loss may occur quickly. The d evelopment of a n accurate s ystem m odel r equires m aneuvers detection a nd estimation of the magnitude of the maneuvers [5], [8], [17] and [20]. In space applications, particularly in spacecraft relative navigation for spacecraft autonomous r endezvous a nd assembly, each spacecraft i s t he t arget of a nother one . In other words, there is a complete symmetry, where each vehicle needs to track the others. Here a n a dditional c hallenge i s e ncountered: the f requent loss of communication and, when the application involves more than one spacecraft, the contact for the data exchange could not be always available. If data communication is not the way spacecraft perform relative n avigation, a vision b ased s ystem may be used. These types of sensors require image processing and may result in low frequency measurement upda tes, especially for small spacecraft w ith l imited computation capabilities. Such sensors suffer of p roblems such a s l imitations on t he f ield of view a nd/or ot her s pacecraft obs tructing t he view. Furthermore, in spacecraft relative maneuvering, each vehicle does not usually know the other vehicles inputs, i.e. it does not possess the information about the maneuvers decided by its fellow spacecraft. This missing information needs to be reconstructed somehow in the Kalman filter, which otherwise would diverge quickly. Many schemes addressing these types of problems have been developed, based on different a ssumptions about t he t arget’s dynamics. For e xample, some r esearchers developed algorithms based on constant velocity o r c onstant a cceleration assumptions, other methods need to use small s ampling times between two measurements to operate accurately (Munu e t a l., 1992 ; B lair e t a l., 1991; Mook and Shyu, 1992; Rokhsaz and Steck, 1991). The above listed limitations are the motivation for the present study.

Tesi di Master

Autore: Veronica Pellegrini Contatta »

Composta da 200 pagine.

 

Questa tesi ha raggiunto 95 click dal 16/04/2010.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.