Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Abstract

This paper presents a novel failure-tolerant architecture for future robotic spacecraft. It is based on the Time and Space Partitioning (TSP) principle as well as a combination of Artificial Intelligence (AI) and traditional concepts for system failure detection, isolation and recovery (FDIR). Contrary to classic payload that is separated from the platform, robotic devices attached onto a satellite become an integral part of the spacecraft itself. Hence, the robot needs to be integrated into the overall satellite FDIR concept in order to prevent fatal damage upon hardware or software failure. In addition, complex dexterous manipulators as required for on orbit servicing (OOS) tasks may reach unexpected failure states, where classic FDIR methods reach the edge of their capabilities with respect to successfully detecting and resolving them. Combining, and partly replacing traditional methods with flexible AI approaches aims to yield a control environment that features increased robustness, safety and reliability for space robots. The developed architecture is based on a modular on-board operational framework that features deterministic partition scheduling, an OS abstraction layer and a middleware for standardized inter-component and external communication. The supervisor (SUV) concept is utilized for exception and health management as well as deterministic system control and error management. In addition, a Kohonen self-organizing map (SOM) approach was implemented yielding a real-time robot sensor confidence analysis and failure detection. The SOM features non supervized training given a typical set of defined world states. By compiling a set of reviewable three-dimensional maps, alternative strategies in case of a failure can be found, increasing operational robustness. As demonstrator, a satellite simulator was set up featuring a client satellite that is to be captured by a servicing satellite with a 7-DoF dexterous manipulator. The avionics and robot control were integrated on an embedded, space-qualified Airbus e.Cube on-board computer. The experiments showed that the integration of SOM for robot failure detection positively complemented the capabilities of traditional FDIR methods.

Applications and Challenges of Artificial Intelligence in Space Missions

“…For instance, by introducing dexterous manipulators to traditional satellite platforms the spacecraft design becomes increasingly complex. Stemming from the high level of interconnectedness between the manipulator and its floating base, the manipulator becomes an intrinsic part of the overall spacecraft design [182]. Fundamentally, the whole satellite turns in into a 'space robot.'…”

“…In contrast to a classic payload, which is usually detached from the platform, a robotic device attached to a satellite becomes an integral part of the spacecraft itself. By introducing dexterous manipulators to traditional satellite platforms, Jaekel et al [182] argue that the spacecraft design becomes increasingly sophisticated and complex. The high interdependencies between the manipulator and its floating base automatically turn the whole satellite into a 'space robot.'…”

A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operations

“…An example of an ML-based solution to the issue of model brittleness can be found in a paper by Jaekel et al (Jaekel and Scholz, 2015). This work uses Self-Organizing Maps (SOM), an unsupervised variant of Artificial Neural Networks (ANNs), for the detection of failures in dexterous manipulators for On-Orbit Servicing (OOS).…”

“…In FDIR, recovery entails reconfiguring the problematic element and/or the entire spacecraft to restore normal system behaviour (Jaekel and Scholz, 2015).…”

“…Due to these uncertainties, the system and its environment cannot be completely observed by traditional FDIR concepts that pose limitations to autonomous isolation and recovery (Meß, 2019). For example, Mars Express lost six months of operational hours due to a non-resolvable memory problem that forced it into safe mode repeatedly (Jaekel and Scholz, 2015).…”