Accumulating space debris edges the space domain ever closer to cascading Kessler syndrome, a chain reaction of debris generation that could dramatically inhibit the practical use of space. Meanwhile, a growing number of retired satellites, particularly in higher orbits like geostationary orbit, remain nearly functional except for minor but critical malfunctions or fuel depletion. Servicing these ailing satellites and cleaning up “high-value” space debris remains a formidable challenge, but active interception of these targets with autonomous repair and deorbit spacecraft is inching closer toward reality as shown through a variety of rendezvous demonstration missions. However, some practical challenges are still unsolved and undemonstrated. Devoid of station-keeping ability, space debris and fuel-depleted satellites often enter uncontrolled tumbles on-orbit. In order to perform on-orbit servicing or active debris removal, docking spacecraft (the “Chaser”) must account for the tumbling motion of these targets (the “Target”), which is oftentimes not known a priori. Accounting for the tumbling dynamics of the Target, the Chaser spacecraft must have an algorithmic approach to identifying the state of the Target’s tumble, then use this information to produce useful motion planning and control. Furthermore, careful consideration of the inherent uncertainty of any maneuvers must be accounted for in order to provide guarantees on system performance. This study proposes the complete pipeline of rendezvous with such a Target, starting from a standoff estimation point to a mating point fixed in the rotating Target’s body frame. A novel visual estimation algorithm is applied using a 3D time-of-flight camera to perform remote standoff estimation of the Target’s rotational state and its principal axes of rotation. A novel motion planning algorithm is employed, making use of offline simulation of potential Target tumble types to produce a look-up table that is parsed on-orbit using the estimation data. This nonlinear programming-based algorithm accounts for known Target geometry and important practical constraints such as field of view requirements, producing a motion plan in the Target’s rotating body frame. Meanwhile, an uncertainty characterization method is demonstrated which propagates uncertainty in the Target’s tumble uncertainty to provide disturbance bounds on the motion plan’s reference trajectory in the inertial frame. Finally, this uncertainty bound is provided to a robust tube model predictive controller, which provides tube-based robustness guarantees on the system’s ability to follow the reference trajectory translationally. The combination and interfaces of these methods are shown, and some of the practical implications of their use on a planned demonstration on NASA’s Astrobee free-flyer are additionally discussed. Simulation results of each of the components individually and in a complete case study example of the full pipeline are presented as the study prepares to move toward demonstration on the International Space Station.

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Seit 2012 gelten in der Europäischen Union neue Regeln für die Behandlung von Derivaten, besonders von Over-the-Counter (OTC)-Derivaten. Mit dem Inkrafttreten der European Market Infrastructure Regulation (EMIR) innerhalb der EU und verschiedenen anderen Regelungen in Drittstaaten werden die Anforderungen der G20 umgesetzt. Das Handbuch EMIR richtet sich in erster Linie an die Rechts- und Compliance-Abteilungen der Kredit-, Versicherungs- und Finanzdienstleistungswirtschaft sowie an Kapitalverwaltungsgesellschaften, Depotbanken, Clearingstellen und Aufsichtsbehörden. Es bietet Ihnen eine ausgewogene Mischung aus Praxis und Wissenschaft, wobei die rechtliche Entwicklung bei den Derivaten unter Berücksichtigung von wichtigen Schreiben der Aufsichtsbehörden dargestellt wird.

As the concentration of large space debris increases, how rendezvous maneuvers involving these typically non-cooperative, freely tumbling bodies are planned and executed is evolving. The rendezvous must be carefully planned, employing up-to-date in situ data to identify the inertial and motion parameters of the target body, and executed in a manner that accounts for the remaining uncertainty in these parameters. This paper presents an extension of the Tumbling Rendezvous via Autonomous Characterization and Execution (TRACE) pipeline used in the ROAM/TumbleDock Astrobee experiment campaign, which sequences the target state estimation, motion planning, controller design, and maneuver execution tasks while additionally providing logical loop-back avenues to previous tasks, increasing the chances of a successful maneuver. The pipeline’s performance is analyzed in simulation, utilizing target state estimates generated in a previous activity on a dedicated on-ground test bed; online motion planning, based on nonlinear programming and warm-started using a trajectory library generated offline with a novel graphics-processing-unit-based method; and tube-based model predictive control to robustly track the planned trajectory. Tube-based model predictive control is an actively evolving subject, distributed over multiple publications and various research interests. The necessary theory and considerations for practical implementation of the method are consolidated; its use, features, and limitations in the proposed task are demonstrated.