Multi-Levels Planning for Spacecraft Autonomy

2002

The New Millennium Remote Agent (NMRA) vSll be the first AI system to control an actual spacecraft. The spacecraft domain raises a number of challenges for planning and execution, ranging from extended agency and long-term planning to dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a v-ide variety of possible failures. We believe NMRA is the first system to integrate closed-loop planning and e.xecution of concurrent temporal plans, and the first autonomous system that v-ill be able to achieve a sustained multi-stage multiyear mission v-ithout communication or guidance from earth.

1996

NASA has recently announced the New Millennium Program (NMP) to develop \faster, better, cheaper" spacecraft in order to establish a \virtual presence" in space. A crucial element in achieving this vision is onboard spacecraft autonomy, requiring us to automate functions which have traditionally been achieved on ground by humans. These include planning activities, sequencing spacecraft actions, tracking spacecraft state, ensuring correct functioning, recovering in cases of failure and recon guring hardware. In response to these challenging requirements, we analyzed the spacecraft domain to determine its unique properties and developed an architecture which provided the required functionality. This architecture integrates traditional real-time monitoring and control with constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and recon guration. In a ve month e ort we successfully demonstrated this implemented architecture in the context of an autonomous insertion of a simulated spacecraft into orbit around Saturn, trading o science and engineering goals, and achieving the mission goals in the face of any single point of hardware failure. This scenario turned out to be among the most complex handled by each of the component technologies. As a result of this success, the integrated architecture has been selected to control the rst NMP ight, Deep Space One, in 1998. It will be the rst AI system to autonomously control an actual spacecraft.

NASA STI/Recon Technical Report N, 2003

In addition to traditional fault detection and recovery, the algorithms must be robust to fleet-level faults, loss of shared information, communication latency, relative control and estimation, and collisions. • The computation, information management and communication must be well distributed. • Robust distributed autonomous GN&C algorithms are required. The flight software must be flexible and easy to adapt, such as allowing the software blocks to be dynamically loaded.

The Deep Space One (DS1) mission, scheduled to fly in 1998, will be the first spacecraft to feature an on-board planner. The planner is part of an artificial intelligence based control architecture that comprises a planner/scheduler, a plan execution engine, and a model-based fault diagnosis and reconfiguration engine. This autonomy architecture reduces mission costs and increases mission quality by enabling highlevel commanding, robust fault responses, and opportunistic responses to serendipitous events. This paper describes the on-board planning and scheduling component of the DS1 autonomy architecture.

2009

1997

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. This architecture integrates traditional real-time monitoring and control with constraintbased planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconguration. We implemented a prototype autonomous spacecraft agent within the architecture and demonstrated the prototype in the context of a challenging autonomous mission scenario on a simulated spacecraft. As a result of this success, the integrated architecture has been selected to control Deep Space One (DS-1), the rst ight of NASA's New Millennium Program (NMP), which will launch in 1998. It will be the rst AI system to autonomously control an actual spacecraft.

IEEE Aerospace and Electronic Systems Magazine, 2009

An autonomous spacecraft must balance longterm and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. In terms of high-level, goal-oriented activity, spacecraft plans must often be modified in the event of fortuitous events such as observations completing early and setbacks such as failure to acquire a guidestar for a science observation. This paper describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of changing operating context.

ArXiv, 2021

Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of operating missions with such onboard autonomous capabilities has not been studied to a level of detail sufficient for consideration in mission concepts. These autonomy capabilities will require changes to current operations processes, practices, and tools. We have developed a case study to assess the changes needed to enable operators and scientists to operate an autonomous spacecraft by facilitating a common model between the ground personnel and the onboard algorithms. We assess the new operations tools and workflows necessary to enable operators and scientists to convey their desired intent to the spacecraft, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft. Mock-ups of these tools were used in a user study to underst...

IEEE Intelligent Systems, 1998

Space Sciencecraft Control and Tracking in the New Millennium, 1996

NASA has recently announced the New Millennium Program NMP to develop faster, better, cheaper" spacecraft in order to establish a virtual presence" in space. A crucial element i n a c hieving this vision is onboard spacecraft autonomy, requiring us to automate functions which h a v e traditionally been achieved on ground by humans. These include planning activities, sequencing spacecraft actions, tracking spacecraft state, ensuring correct functioning, recovering in cases of failure and recon guring hardware. In response to these challenging requirements, we analyzed the spacecraft domain to determine its unique properties and developed an architecture which provided the required functionality. This architecture integrates traditional real-time monitoring and control with constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and recon guration. In a ve month e ort we successfully demonstrated this implemented architecture in the context of an autonomous insertion of a simulated spacecraft into orbit around Saturn, trading o science and engineering goals, and achieving the mission goals in the face of any single point of hardware failure. This scenario turned out to be among the most complex handled by each of the component technologies. As a result of this success, the integrated architecture has been selected to control the rst NMP ight, Deep Space One, in 1998. It will be the rst AI system to autonomously control an actual spacecraft.