System engineering of autonomous space vehicles

Abstract

Human exploration of the solar system requires fully autonomous systems when travelling more than 5 light minutes from Earth. This autonomy is necessary to manage a large, complex spacecraft with limited crew members and skills available. The communication latency requires the vehicle to deal with events with only limited crew interaction in most cases. The engineering of these systems requires an extensive knowledge of the spacecraft systems, information theory, and autonomous algorithm characteristics. The characteristics of the spacecraft systems must be matched with the autonomous algorithm characteristics to reliably monitor and control the system. This presents a large system engineering problem. Recent work on product-focused, elegant system engineering will be applied to this application, looking at the full autonomy stack, the matching of autonomous systems to spacecraft systems, and the integration of different types of algorithms. Each of these areas will be outlined and a general approach defined for system engineering to provide the optimal solution to the given application context.

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA’s Space Launch System

“…This is already possible using MAVERIC, the GNC Flight Mechanics tool, which is configurable for different flight vehicles. 7 For deep-space human exploration requiring more autonomy, the M&FM algorithms will need to be more robust, reliable, and possibly able to accommodate predictive technologies [11]. The LV/spacecraft subsystem models (e.g., nuclear based) will exhibit more complexity with expected physics-based elements addressing off nominal conditions such as effects from environmental disturbances, vehicle system upsets, radiation and solar effects, including communication delay models [6,11].…”

“…Furthermore, the supporting hardware and software platforms hosting the advanced algorithms, including any that are model based, will likely need to utilize advanced computing concepts such as those from the artificial intelligence realm (e.g., neural, fuzzy, or Bayesian methods) [11]. Advanced computing algorithmic concepts may also employ nonlinear mathematical constructs (e.g., inference engine and probabilistic methods), rapid detection systems using advanced fusion and reasoning algorithms for sensor data, information processing, and reliable decision theory [11]. Software related to these systems and their integration will require advanced testbeds with commensurate V&V methods particularly for certifying any adaptive diagnostic, prognostic, and control algorithms operating under off-nominal conditions.…”

“…The viewpoint for the M&FM methods for such deep space LV/spacecraft are not unlike those for the SLS, but may be compounded with the need for more autonomy and less man-in-the-loop interactions. The ongoing work from Watson et al and others across NASA, academia, and industry are projecting these types of directions and the principal proposition is that VMET will be poised to provide the M&FM focus for such endeavors, especially with technology trending towards more integrated and so called intelligent systems [11].…”

“…Former Chief, promoted to Vehicle Management Discipline Lead Engineer in April 20159 Former Deputy Chief, promoted to Chief in May 201510 Former Chief, now serving in the Chief Engineer's Office for Systems Engineering11 Such as Crew Office, Ground ops, SPIO, Avionics, GNC, KSC, JSC, GRC, ARC, Booster, Liquid Engines, and S&MA 12 VMET, analysis, algorithm developers, schedule leads, IDT leads, prototype developers, and all administrative and project support personnel…”