Model-Based Autonomy for the Next Generation of Robotic Spacecraft

Details Model-Based Autonomy for the Next Generation of Robotic Spacecraft Model-Based Autonomy for the Next Generation of Robotic Spacecraft

Jan 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002iaf..confe.918f&link_type=abstract

IAF abstracts, 34th COSPAR Scientific Assembly, The Second World Space Congress, held 10-19 October, 2002 in Houston, TX, USA.,

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NASA' vision of an end-to-end autonomously operated space flight system is inspiring the development of enabling technologies for highly robust spacecraft. Over the past few years, a new approach to the design of reactive embedded software systems, called model-based autonomy, has generated significant interest in the space systems community. The goal of the model-based approach is to automate onboard sequence execution, by tightly integrating goal- driven commanding with fault detection, diagnosis, and recovery capabilities. Model-based autonomy has been deployed in various aerospace applications, including the Deep Space 1 (DS- 1) mission, and ground testbeds for the Space Interferometry Mission, the X-34 and X-37 rocket planes, and an in-situ propellant production system. This paper describes a system-level autonomy framework that significantly expands upon previous model-based approaches to configuration management, such as the Remote Agent mode identification and reconfiguration system (Livingstone), which was flown onboard the DS-1 spacecraft. This autonomy framework, named Titan, is a model-based executive that is capable of estimating current spacecraft modes, detecting and repairing failures, and executing commands, all within a fast sense-decide-act loop. Titan is composed of a model-based control sequencer and a deductive controller. The sequencer takes in system-level goals from theSystem-level ground or an onboard planner. Each system goal invokes a control program, which prescribesControl Sequencer complex spacecraft behaviors and issuesConfiguration appropriate configuration goals to the deductiveestimatesgoals controller, based on the system' estimated state trajectory. The deductive controller uses a common-sense plant model of the hardware and software components in the system to trackObservationsCommands state, diagnose faults and generate controlRT Control Layer actions that achieve the configuration goals. This paper first provides a description of the model-based software technologies implemented within the Titan framework. It describes a case study of the deployment of Titan to a representative space mission, including an overview of the various component models assembled, as well as the scenarios generated to verify proper execution of the software. Finally, it provides results from validation tests of the Titan implementation on these scenarios and models.

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