Renewed motives for space exploration have inspired NASA to work toward the goal of establishing a virtual presence in space, through heterogeneous effets of robotic explorers. Information technology, and Artificial Intelligence in particular, will play a central role in this endeavor by endowing these explorers with a form of computational intelligence that we call remote agents. In this paper we describe the Remote Agent, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future. This architecture addresses the unique characteristics of the spacecraft domain that require highly reliable autonomous operations over long periods of time with tight deadlines, resource constraints, and concurrent activity among tightly coupled subsystems. The Remote Agent integrates constraint-based temporal planning and scheduling, robust multi-threaded execution, and model-based mode identification and reconfiguration. The demonstration of the integrated system as an on-board controller for Deep Space One, NASA's rst New Millennium mission, is scheduled for a period of a week in late 1998. The development of the Remote Agent also provided the opportunity to reassess some of AI's conventional wisdom about the challenges of implementing embedded systems, tractable reasoning, and knowledge representation. We discuss these issues, and our often contrary experiences, throughout the paper.

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 recon#guration.

The Remote Agent (RA) integrates a broad spectrum of robotic activities, including planning, scheduling, execution, monitoring, failure detection, diagnosis, and recovery. The RA Executive (EXEC) can be viewed as the core of the agent. EXEC enables software developers to think about the robot at a higher level; it also supports the reuse of knowledge and code across multiple robot applications. EXEC's capabilities include a high-level procedural action-definition language, services for resource management and configuration management, support for executing flexible closed-loop plans and command sequences, support for replanning, and a framework for specifying fault responses including safe modes and responses to plan failures. We believe that these capabilities are required for most autonomous agents, and that executives and execution capabilities will become more essential as we attempt to develop autonomous agents of increasing capability. Moreover, we deem modular...

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 reconfiguring 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 reconfiguration. In a five month effort we successfully demonstrated this implemented architecture in the context...

The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DS1), the first deep space mission of the NASA's New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which RA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, su...

Autonomous rovers are a critical element for the success of human exploration of Mars. The robotic tasks required for human presence on Mars are beyond the ability of current rovers; these tasks include landing-site scouting and mining, as well as emplacement and maintenance of a habitat, fuel production facility, and power generator. These tasks are required before and also during human presence; the ability of rovers to offload work from the human explorers will enable the humans to accomplish their mission. Performing these tasks will require significant advances in rover autonomy and will require improvements in robustness, resource utilization, and failure recovery. The Pathfinder mission demonstrated the potential for robotic Mars exploration, but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. Advances in rover autonomy offer increased...

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 reconfiguring 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 reconfiguration. In a five month effort we successfully demonstrated this implemented architecture in the context...

. Deep Space Oue will the first spacecraft to he controlled by an autonomous closed loop potentially capable of carrying out a complete mission with minimal from Earth. A major component of the autonomous flight software is an onboard planner/scheduler. generative planning and temporal ing technologies, the planner/scheduler transforms abstract goals into detailed tasks to executed within resource and time limits. This paper discusses the knowledge acquisition issues involved in trausitioning this novel technology into spacecraft flight software, developing the planner in the context a software project completing the work under a compressed development schedule. Our experience shows that the planning framework used is adequate to address the challenges of and future autonomous spacecraft systems, and it points to a series of open technological challenges in developing and tools for acquisition and validation. 1 Introduction The future of the space for ambitious missions of exploration...

With the rapidly increasing performance of information technologies, a new capability is being developed that holds the clear promise of greatly increased exploration possibilities, along with dramatically reduced design, development, and operating costs. In addition, specific technologies such as neural nets will provide a degree of machine intelligence and associated autonomy which has previously been unavailable to the mission and spacecraft designer and the system operator. One of the most promising applications of these new information technologies is to the area of in-situ resource utilization. Useful resources such as oxygen, carbon dioxide, methane, and water can be extracted and/or generated from planetary atmospheres, to be used for propulsion and life-support needs. This can provide significant savings in

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, halfhour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, ISRU and power systems. A...