A Flexible Image Processing Framework for Vision-based Navigation Using Monocular Image Sensors

On-Orbit Servicing (OOS) encompasses all operations related to servicing satellites and performing other work on-orbit, such as reduction of space debris. Servicing satellites includes repairs, refueling, attitude control and other tasks, which may be needed to put a failed satellite back into working condition. A servicing satellite requires accurate position and orientation (pose) information about the target spacecraft. A large quantity of different sensor families is available to accommodate this need. However, when it comes to minimizing mass, space and power required for a sensor system, mostly monocular imaging sensors perform very well. A disadvantage is- when comparing to LIDAR sensors- that costly computations are needed to process the data of the sensor. The method presented in this paper is addressing these problems by aiming to implement three different design principles; First: keep the computational burden as low as possible. Second: utilize different algorithms and choose among them, depending on the situation, to retrieve the most stable results. Third: Stay modular and flexible. The software is designed primarily for utilization in On-Orbit Servicing tasks, where- for example- a servicer spacecraft approaches an uncooperative client spacecraft, which can not aid in the process in any way as it is assumed to be completely passive. Image processing is used for navigating to the client spacecraft. In this specific scenario, it is vital to obtain accurate distance and bearing information until, in the last few meters, all six degrees of freedom are needed to be known. The smaller the distance between the spacecraft, the more accurate pose estimates are required. The algorithms used here are tested and optimized on a sophisticated Rendezvous and Docking Simulation facility (European Proximity Operations Simulator- EPOS 2.0) in its second-generation form located at the German Space Operations Center (GSOC) in Weßling, Germany. This particular simulation environment is real-time capable and provides an interface to test sensor system hardware in closed loop configuration. The results from these tests are summarized in the paper as well. Finally, an outlook on future work is given, with the intention of providing some long-term goals as the paper is presenting a snapshot of ongoing, by far not yet completed work. Moreover, it serves as an overview of additions which can improve the presented method further

A Methodology to Repair or Deorbit LEO Satellite Constellations

In this thesis, mitigation of space debris is addressed by examining an approach for repair or de-orbit of a specific population of non-functional Low Earth Orbit (LEO) satellites. Basic orbital mechanics propagation of the orbits was used as the process for computing a solution to the time and intercept position for the targeted satellites. Optimal orbital maneuvers to reach the target satellites from a pre-established orbit were also considered. In this way minimum ΔV budget, rendezvous time and mass budgets were managed. The Clohessy-Wiltshire Equations and two-impulsive rendezvous maneuvers were used to determine the orbital path of a chase satellite between two position vectors, along with the time of flight. A monopropellant propulsion system was assumed in order to estimate propellant mass requirements. This methodology can be applied to a variety of satellite constellations, as implemented using MatLab and Analytical Graphics, Inc. STK software. Several cases were investigated in the study. Simulations showed that the methodology can provide guidance for the rendezvous process, facilitating a minimum ΔV budget and minimum rendezvous time

Cognitive vision for autonomous satellite rendezvous and docking

We present a cognitively-controlled vision system that combines low-level object recognition and tracking with high-level symbolic reasoning for the purpose of solving difficult space robotics problems—satellite rendezvous and docking. The reasoning module, which encodes a model of the environment, performs deliberation to 1) guide the vision system in a taskdirected manner, 2) activate vision modules depending on the progress of the task, 3) validate the performance of the vision system, and 4) suggest corrections to the vision system when the latter is performing poorly. Reasoning and related elements, among them intention, context, and memory, contribute to improve the performance (i.e., robustness, reliability, and usability). We demonstrate the vision system controlling a robotic arm that autonomously captures a free-flying satellite. Currently such operations are performed either manually or by constructing detailed control scripts. The manual approach is costly and exposes astronauts to danger, while the scripted approach is tedious and error-prone. Therefore, there is substantial interest in performing these operations autonomously, and the work presented here is a step in this direction. To our knowledge, this is the only satellite-capturing system that relies exclusively on vision to estimate the pose of the satellite and can deal with an uncooperative satellite.