No abstract

A policy for six-degree-of-freedom docking maneuvers is developed through reinforcement learning and implemented as a feedback control law. Reinforcement learning provides a potential framework for robust, autonomous maneuversin uncertain environments with low on-board computational cost. Specifically, proximal policy optimization is used to produce a docking policy that is valid over a portion of the six-degree-of-freedom state-space while striving to minimize performance and control costs. Experiments using the simulated Apollo transposition and docking maneuver exhibit the policy's capabilities and provide a comparison with standard optimal control techniques. Furthermore, specific challenges and work-arounds, as well as a discussion on the benefits and disadvantages of reinforcement learning for docking policies, are discussed to facilitate future research. As such, this work will serve as a foundation for further investigation of learningbased control laws for spacecraft proximity operations in uncertain environments.

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.

Autonomous spacecraft critically depend on on-orbit inspection (i.e., relative navigation and inertial properties estimation) to intercept tumbling debris objects or defunct satellites. This work presents a practical method for onorbit inspection and demonstrates its performance in simulation using NASA's Astrobee robotic free-flyers. The problem is formulated as a simultaneous localization and mapping task, utilizing IMU data from an observing "chaser" spacecraft and point clouds of the observed "target" spacecraft obtained via a 3D time-of-flight camera. The relative navigation between the chaser and target is solved via a factor graph-based approach. The target's principal axes of inertia are then estimated via a conic fit optimization procedure using a polhode analysis. Simulation results indicate the accuracy of the proposed method in preparation for hardware experiments on the International Space Station.