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An autonomous spacecraft agent prototype

An Autonomous Spacecraft Agent Prototype
Barney Pell

Douglas E. Bernard


Steve A. Chien


Erann Gat


Nicola Muscettola P. Pandurang Nayak Michael D. Wagner Brian C. Williams


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. 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.


The future of space exploration calls for establishing a \virtual presence" in space. This will be reached with a large number of smart, cheap spacecraft conducting missions as ambitious as robotic rovers, balloons for extended atmospheric explorations and robotic submarines. Several new technologies need to be demonstrated to reach this goal, and one of the most crucial is on-board spacecraft autonomy. In the traditional approach to spacecraft operations humans carry out on the ground a large number of funcz Recom Technologies, NASA Ames Research Center, MS 269/2, Mo ett Field, CA 94035. y Caelum Research, NASA Ames Research Center, MS 269/2, Mo ett Field, CA 94035. x Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109. zz Fourth Planet, 155A Mo ett Park Drive, Suite 104, Sunnyvale, CA 94089.


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tions including planning activities, sequencing spacecraft actions, tracking the spacecraft's internal hardware state, ensuring correct functioning, recovering in cases of failure, and subsequently working around faulty subsystems. This approach will not be viable anymore in the future due to (a) round trip light time communication delays which make joysticking a deep space mission impossible and (b) a desire to limit the operations team and deep-space communications costs. In the new model of operations, the scientists will communicate high-level science goals directly to the spacecraft. The spacecraft will then perform its own science planning and scheduling, translate those schedules into sequences, verify that they will not damage the spacecraft, and ultimately execute them without routine human intervention. In the case of error recovery, the spacecraft will have to understand the impact of the error on its previously planned sequence and then reschedule in light of the new information and potentially degraded capabilities. To bridge the gap between the old operations model and the new one, we conducted a rapid-prototyping effort in which we demonstrated complete autonomous operations in a very challenging context: simulated insertion of a realistic spacecraft into orbit around Saturn. The mission scenario included trading o science and engineering goals and achieving the mission in the face of any single point of hardware failure. This Saturn Orbit Insertion (SOI) scenario, although simpli ed, still contained the most important constraints and sources of complexities of a real mission, making it the most dif cult challenge in the context of the most complicated mission phase of the most advanced spacecraft to date (Pell et al. 1996a). The unique requirements of this domain led us to the New Millennium Remote Agent (NMRA) architecture. The architecture integrates traditional real-time monitoring and control with (a) constraint-based planning and scheduling, to ensure achievement of long-term mission objectives and e ectively manage allocation of scarce system resources; (b) robust multi-threaded execution, to reliably execute planned sequences under conditions of uncertainty, to rapidly respond to unexpected events such as component failures, and to manage concurrent real-time activities; and (c) model-based diagnosis, to con rm successful plan execution and to infer

the health of all system components based on inherently limited sensor information. The New Millennium Remote Agent (NMRA) architecture was successfully demonstrated on the simulated SOI scenario in October 1995. This success resulted in the inclusion of NMRA in the ight software of the rst NMP mission, Deep Space 1 (DS-1), which is scheduled to launch in mid-1998. This will be the rst AI system to autonomously control an actual spacecraft. The spacecraft domain places a number of requirements on the software architecture that di erentiates it from domains considered by other researchers. There are three major properties of the domain that drove the architecture design. First, a spacecraft must be able to carry on autonomous operations for long periods of time with no human interaction. This requirement stems from limitations of deep-space communication and the desire to cut operating expenses. The requirement for autonomous operations over long periods is further complicated by two additional features of the domain{tight resource constraints and hard deadlines . A spacecraft uses various resources, including obvious ones like fuel and electrical power, and less obvious ones like the number of times a battery can be reliably discharged and recharged. Some of these resources are renewable but most of them are not. Hence, autonomous operations requires signi cant emphasis on the careful utilization of non-renewable resources and on planning for the replacement of renewable resources before they run dangerously low. Spacecraft operations are also characterized by the presence of hard deadlines, e.g., the eciency of orbit change maneuvers is a strong function of the location of the spacecraft in its orbit, so that the time at which SOI must be achieved is constrained to lie within a two hour window. Sophisticated planning and scheduling systems are needed to meet this requirement. The second central requirement of spacecraft operation is high reliability . Since a spacecraft is very expensive and often unique, it is essential that it achieve its mission with a very high level of reliability. Part of this high reliability is achieved through the use of very reliable hardware. However, the harsh environment of space or the inability to test in all ight conditions can still cause unexpected hardware failures, so that the software architecture is required to compensate for such contingencies. This requirement dictates the use of an executive and elaborate system-level fault protection that can rapidly react to contingencies by retrying failed actions, recon guring spacecraft subsystems, or sa ng the spacecraft to prevent further, potentially irretrievable, damage. Of equal danger are catastrophic software bugs, often introduced through a mismatch of spacecraft models in the heads of di erent software engineers. This requirement dictates the need to maximize the use of a consistent model shared between the di erent executive functions.


The requirement of high reliability is further complicated by the fact that there is limited observability into the spacecraft's state due to the availability of a limited number of sensors. The addition of sensors implies added mass1, power, cabling, and up front engineering time and e ort. Each sensor must add clear value to the mission to be justi ed for inclusion. Furthermore, sensors are typically no more reliable than the associated spacecraft hardware, making it that much more dicult to deduce the true state of the spacecraft hardware. These requirements dictate the use of sophisticated model-based diagnosis methods for identifying the true state of the spacecraft hardware. These methods predict unobservable state variables using a spacecraft model, and can e ectively handle sensor failures. In addition these diagnostic methods must be augmented with sophisticated model-based control methods that help the executive to recon gure hardware in view of failure knowledge and to predict the consequences of these actions. The third central requirement of spacecraft operation is that of concurrent activity . The spacecraft has a number of di erent subsystems, all of which operate concurrently. Hence, reasoning about the spacecraft needs to re ect its concurrent nature. In particular, the planner/scheduler needs to be able to schedule concurrent activities in di erent parts of the spacecraft, including constraints between concurrent activities. The executive needs to have concurrent threads active to handle concurrent commands to di erent parts of the spacecraft. The model-based diagnosis and recon guration system needs to handle concurrent changes in the spacecraft state, either due to scheduled events or due to failures. In the architecture autonomous operations is achieved through the cooperation of 5 distinct components (Figure 1). Continuous autonomous operation is achieved by the repetition of the following cycle. 1. Retrieve high level goals from the mission's goals database. In the actual mission, goals can be known at the beginning of the mission, put into the database by communication from ground mission control or can originate from the operations of spacecraft subsystems (e.g., \take more pictures of star elds to estimate position and velocity of the spacecraft"). 2. Ask the planner/scheduler to generate a schedule. The planner receives the goals, the scheduling horizon, i.e., the time interval that the schedule needs to cover, and an initial state, i.e., the state of all relevant spacecraft subsystems at the beginning of the scheduling horizon. The resulting schedule is represented as a set of tokens placed on various state variable time lines , with temporal constraints between tokens.
In a spacecraft, mass directly translates to the cost of launch and the cost of carrying extra fuel to achieve all mission maneuvers.


Planning & Scheduling


Ground software

Model-based Mode Ident. and Recovery


Real-Time Control system

Hardware/ Dynamics Simulator

Figure 1: NMRA architecture 3. Send the new schedule generated by the planner to the executive . The executive will continue executing its current schedule and start executing the new schedule when the clock reaches the beginning of the new scheduling horizon. The executive translates the abstract tokens contained in the schedule into a sequence of lower level spacecraft commands that correctly implement the tokens and the constraints between tokens. It then executes these commands, making sure that the commands succeed and either retries failed commands or generates an alternate low level command sequence that achieves the token. Hard command execution failures may require the modi cation of the schedule in which case the executive will coordinate the actions needed to keep the spacecraft in a \safe state" and request the generation of a new schedule from the planner. 4. Repeat the cycle from step 1 when one of the following conditions apply: (a) Execution (real) time has reached the end of the scheduling horizon minus the estimated time needed for the planner to generate a schedule for the following scheduling horizon; (b) The executive has requested a new schedule as a result of a hard failure. Schedule execution is achieved through the cooperation of the the executive, a mode-identi cation system, and a lower-layer of software responsible for real-time monitoring and control. The executive reasons about spacecraft state in terms of a set of component modes. The mode identi cation (MI) component is responsible for providing this level of abstraction to the executive. MI takes as input the executive command sequence and observations from sensors to identify the current mode (nominal or failed) of each spacecraft component. The monitoring layer takes the raw sensor data stream, and discretizes it to the abstract level required by MI. Finally, the control and real-time system layer takes commands from the executive and provides the actual control of the low level state of the spacecraft. It is responsible for providing the low level sensor data stream to the monitors. The planner/scheduler is the only component that is activated as a \batch process" and dies after a new schedule has been generated. The rest of the software is always active and in concurrent execution. This ensures the high reliability required by the domain. Monitoring and control follow traditional approaches to spacecraft software and will not be discussed here. In the following we will concentrate on the other modules.

3.1 Planner

The goal of the planner/scheduler is to generate a set of synchronized high-level commands that once executed will achieve mission goals. Particularly in the spacecraft domain planning and scheduling aspects of the problem need to be tightly integrated. Clearly the planner needs to recursively select and schedule appropriate activities to achieve mission goals and any other subgoals generated by these activities. It also needs to synchronize activities and allocate global resources over time (e.g., power and data storage capacity). In this domain (but this is also true in general) subgoals may also be generated due to limited availability of resources over time. For example, in a mission it would be preferable to keep scienti c instruments on as long as possible (to maximize the amount of science gathered). However limited power availability may force a temporary instrument shut-down when other more mission critical subsystems need to be functioning. In this case the allocation of power to critical subsystems (the main result of a scheduling step) generates the subgoal \instrument must be o " (which requires the application of a planning step). Considering simultaneously the consequences of planning and scheduling steps enables a planning algorithm to exert more control on the order in which decisions are made and to therefore keep search complexity under control. Besides activities, the planner must also \schedule" the occurrence of states and conditions that need to be monitored to ensure that high level spacecraft conditions are correct for goals (such as spacecraft pointing states, spacecraft acceleration and stability require-

ments, etc.). These states can also consume resources and have nite durations. The planner used in the NMRA architecture consists of a heuristic search engine operating on a temporal database. The search engine posts constraints on the basis of external goals or constraint templates stored in a model of the spacecraft. Using an iterative sampling approach, the planner tries to heuristically improve on certain aspects of schedule quality, although it does not guarantee even local optimality along this metric. The temporal database and the facilities for de ning and accessing model information during search are provided by the HSTS system (Muscettola 1994). The domain model contains an explicit declaration of the spacecraft subsystems on which an activity or a state will occur. In the temporal database each subsystem has an associated timeline on which the planner inserts activities and states and resolves resource allocation con icts. The model also contains the declaration of duration constraints and of templates of temporal constraints between activities and states. Such constraints have to be satis ed by any schedule stored in the temporal database for it to be consistent with the physics of the domain. Temporal constraint templates serve the role of generalized planning operators and are de ned for any activity or state in the domain. The temporal database also provides constraint propagation services to verify the global consistency of the constraints posted so far. The constraint template in Figure 2 describes the conditions needed for an engine burn to initiate correctly (activity Engine Ignition scheduled on the (Engine Op State) timeline). Constraint 5 represents a request for power that increases the level of Power Used on the timeline (Power Mgmt Power) of an amount returned by the Lisp function call (compute-power 'Engine Ignition). Explicit invocation of external function calls provides the means for the planner to invoke \expert" modules to provide narrow but deep levels of expertise in the computation of various parameters such as durations or temperature and power levels. Access to such external knowledge is a key requirement for realworld applications of planning systems (Muscettola et al. 1995).

(Define Compatibility ((Engine Op State) (Engine Ignition)) (AND ;; 1. Ignition requires good engine pressure (contained by ((Engine Tanks Pressure) Good))) ;; 2. Engine must have finished burn preparation (met by ((Engine Op State) (Burn Prep))) ;; 3. Engine goes into sustained burn state next (meets ((Engine Op State) (Engine Burn))) ;; 4. Injector temperature must be good throughout (contained by ((Engine Injector Temp) Good)) ;; 5. Formula to determine Power consumption (equal ((Power Mgmt Power) ( + (Lisp (compute-power 'Engine Ignition)) Power Used))))) (Define Duration Spec ((Engine Op State) (Engine Ignition)) ;; minimum duration (Lisp (compute-duration 'Engine Ignition :minimum)) ;; maximum duration (Lisp (compute-duration 'Engine Ignition :maximum)))

Figure 2: Constraints on the Engine Burn Ignition activity standby mode in the abstract language understood by the planner. It is important to note that establishing standby modes following plan failure is a costly activity, as it causes us to interrupt the ongoing planned activities and lose important opportunities. Such concerns motivate a strong desire for plan robustness, in which the plans contain enough exibility, and the executive has the capability, to continue execution of the plan under a wide variety of execution outcomes (Pell et al. 1996b). Our executive can be viewed as a hybrid system that shares execution responsibilities between a classical reactive execution system, RAPS (Firby 1978) and a novel model-based recon guration system, called Livingstone (Williams & Nayak 1996). RAPS provides a specialized representation language for describing context-dependent contingent response procedures, with an event-driven execution semantics. The language ensures reactivity, is natural for decomposing tasks and corresponding methods, and makes it easy to express monitoring and contingent action schemas. Its runtime system then manages the reactive exploration of a space of alternative actions by searching through a space of task decompositions. The basic runtime loop of the executive is illustrated in Figure 3. The system maintains an agenda on which all tasks are stored. Tasks are either active or sleeping. On each pass through the loop, the executive checks the external world to see if any new events have occured. Examples of events include model updates

3.2 Hybrid executive

The executive is responsible for performing runtime management of all system activities. The executive's functions include process synchronization, process dependency management, hardware recon guration and runtime resource management, and the execution of fault recovery procedures. The executive invokes the planner and mode identi cation components to help it perform these functions. The executive also controls the low-level control software by setting its modes and supplying parameters and by responding to monitored events. In the event of plan failure, the executive knows how to enter a stable state (called a standby mode) prior to invoking the planner, and it knows how to express that

Executive Input


Fetch New Events


6 6

Process Events Expand De nition


Update Memory Update Agenda

Update Agenda Executive Output

? ?

6 De ned 

Execute Command

Process Best Task

Evaluate Ready Tasks

Figure 3: Executive Task Expansion Flowchart from the mode inference system, announcements of commanded activity completion from external software, and requests from external users. The executive responds to these events by updating its internal model of the world, changing the status of a ected tasks, and installing new tasks onto the agenda. It then selects some active task (based on heuristics) and performs a small amount of processing on the task. Processing a high-level task involves breaking it up into subtasks, possibly choosing among multiple methods, whereas processing a primitive task involves sending messages to external software systems. At this point, the agenda is updated, and the basic reactive loop repeats. RAPS encourages a close adherence to a reactive programming principle of limiting deductions within the sense-act loop to that of constructing task decompositions using a limited form of matching. This ensures quick response time, which is essential to the survival of the spacecraft. Nevertheless it places a burden on the programmer of deducing a priori the consequences of failures and contingencies. This is exacerbated by subtle hardware interactions, multiple and unmodeled failures, the mixture of interactions between computation, electronics and hydraulic subsystems, and limited observability due to sensor costs. The model-based recon guration system, Livingstone, complements these reactive capabilities by providing a set of deductive capabilities along the senseact loop that operate on a single, compositional model. These models permit signi cant on the y deduction of system wide interactions, used to process new sensor information or to evaluate the e ects of alternate recovery actions. Yet Livingstone respects the intent of reactive systems, using propositional deductive capabilities coupled to anytime algorithms that have proven exceptionally ecient in the model-based diagnosis of causal systems. Hence Livingstone is able to reason reactively from knowledge of failure, through the models, to optimal actions that reestablish the planner's primitive goals while obviating the failures' e ects. Nevertheless, the assurance of fast inference is achieved through strong restrictions on the representation used for possible recovery actions and even more severe limitations on the way in which these actions are combined. If reactivity is to be preserved, then the only alternative is for a programmer or deductive system to script these complex actions before the fact. Hence RAPS provides a natural complement to Livingstone's deductive capabilities. For example, with respect to recovery, Livingstone provides a service for selecting, composing together and deducing the e ects of basic actions, in light of failure knowledge. Meanwhile RAPS provides powerful capabilities for elaborating and interleaving these basic actions into more complex sequences, which in turn may be further evaluated through Livingstone's deductive capabilities.

3.3 Mode identi cation

The mode identi cation (MI) component of the NMRA architecture is responsible for identifying the current operating or failure mode of each component in the spacecraft. MI is the sensing component of Livingstone's model-based recon guration capability, and provides a layer of abstraction to the executive: it allows the executive to reason about the state of the spacecraft in terms of component modes, rather than in terms of low level sensor values. (Williams & Nayak 1996) provides a detailed technical description of Livingstone. MI provides a variety of functions within the overall architecture. These include:  Mode con rmation: Provide con rmation to the executive that a particular spacecraft command has completed successfully.  Anomaly detection: Identify observed spacecraft behavior that is inconsistent with its expected behavior.  Fault isolation and diagnosis: Identify components whose failures explain detected anomalies. In cases where models of component failure exist, identify the particular failure modes of components that explain anomalies.

Conflict-directed best first search engine


Conflict database

Behavior prediction engine


Figure 4: Architecture of Livingstone's mode identi cation capability.

Token tracking: Monitor the state of planner tokens, allowing the executive to monitor plan execution. MI uses algorithms adapted from model-based diagnosis (de Kleer & Williams 1987; 1989) to provide the above functions (see Figure 4). The key idea underlying model-based diagnosis is that the current state of the spacecraft can be described by a combination of component modes only if the set of models associated with these modes is consistent with the observed sensor values. Following de Kleer & Williams (1989), MI uses a con ict directed best- rst search to nd the most likely combination of component modes consistent with the observations. Note that this methodology is independent of the actual set of available sensors. Furthermore, it does not require that all aspects of the spacecraft state are directly observable, providing an elegant solution to the problem of limited observability discussed in Section 2. The use of model-based diagnosis algorithms immediately provides MI with a number of additional features. First, the search algorithms are sound and complete, providing a guarantee of coverage with respect to the models used. Second, the model building methodology is modular, which simpli es model construction and maintenance, and supports reuse. Third, the algorithms extend smoothly to handling multiple faults. Fourth, while the algorithms do not require explicit fault models for each component, they can easily exploit available fault models to nd likely failures. MI extends the basic ideas of model-based diagnosis by modeling each component as a nite state machine, and the whole spacecraft as a set of concurrent, synchronous state machines. Modeling components as nite state machines allows MI to e ectively track state changes resulting from executive commands. Modeling the spacecraft as a concurrent machine allows MI to effectively track concurrent state changes caused either by executive commands or component failures. Another important feature of MI is that it models the behavior of each component mode using abstract, or qualitative, models (Weld & de Kleer 1990; de Kleer & Williams 1991). These abstract models are encoded as a set of propositional clauses, allowing the use of ecient unit propagation for behavior prediction. In addition to supporting ecient behavior prediction, abstract models are much easier to acquire than detailed quantitative engineering models, and yield more robust

predictions since small changes in the underlying parameters do not a ect the abstract behavior of the spacecraft. Spacecraft modes are a symbolic abstraction of non-discrete sensor values and are synthesized by the monitoring module. Finally, Livingstone uses a single model to perform all of MI's functions, also used for the executive functions of model-based recovery and recon guration. It also uses the kernel algorithm, generalized from diagnosis, to perform all of these MI and executive functions. The combination of a small kernel with a single model, and the process of exercising these through multiple uses, contributes signi cantly to the robustness of the complete system. The implemented NMRA architecture successfully demonstrated planning of a nominal scenario, concurrent execution and monitoring, fault isolation, recovery and re-planning on a simulation of the simpli ed Cassini SOI scenario. The planner modeled the domain with 22 parallel timelines and 52 distinct temporal constraint templates. Each template included an average of 3 temporal constraints of which an average of 1.4 constraints synchronized di erent timelines. The resulting schedule for the nominal scenario included 200 distinct time intervals; a schedule generated after re-planning due to engine burn interruption included 123 time intervals. The planner generated these schedules exploring less than 500 search states in an elapsed time of less than 15 minutes on a SPARC-10. Considering the computational resources available in the DS-1 mission and the background nature of the planning process, this speed is acceptable with respect to the performance needed for DS-1. The executive contained 100 raps with an average of 2.7 steps per rap. The nominal schedule translated into a task net with 465 steps, making it the biggest RAP to date. The executive interacted with the underlying control loops which operated at a cycle frequency of 4 Hz. This performance level is higher than that needed to meet the requirements of the DS-1 mission. The SOI model for the mode identi cation and recovery system included 80 spacecraft components with an average of 3.5 modes per component. The structure and dynamics of the domain was captured by 3424 propositions and 11101 clauses. In spite of the very large size of the model, the con ict-centered algorithms permitted fast fault isolation and determination of recovery actions. Fault isolation took between 4 and 16 search steps (1.1 to 5.5 seconds on a SPARC-5) with an average of 7 steps (2.2 seconds). Recovery took between 4 and 20 steps (1.6 to 6.1 seconds) with an average of 9.3 steps (3.1 seconds). Many important aspects of our architecture follow from our use of a heterogeneous architecture and from significant di erences between the spacecraft domain and the mobile robot domain.



5.1 Heterogeneous knowledge representation

The research approach to an architecture for autonomy is usually to seek a uni ed system based on a uniform representational and computational framework. While this is a very important goal, often the complexity of a real-world domain forces researchers to compromise on complete autonomy or to address simpler domains and applications. In our case the challenge was to achieve complete autonomy for a very complex domain in a limited amount of time. Therefore we chose from the outset to use state-of-the-art, general-purpose components that had been applied to solving isolated problems in the domain. The main architectural challenge was therefore to integrate these components. The main source of dif culty here was that our computational engines all require di erent representations. This heterogeneity has both bene ts and diculties. One bene t of having each engine look at the spacecraft from a di erent perspective is that the heterogeneous knowledge acquisition process aids in attaining coverage and completeness. Each new perspective on a subsystem potentially increases the understanding, and hence improves the modeling, for each of the other components which also represent knowledge of that subsystem. Another bene t is redundancy, where overlapping models enable one component to compensate for restrictions in the representation of another component. This is particularly true for overlapping responsibility in the hybrid executive. A third bene t is task specialization, in which each component is optimized for solving certain kinds of tasks. This means that we can use each component to solve problems for which it is well suited, rather than require one component to solve all problems (a similar point is made by Bonasso et al. (1996)). An important example of representational di erences that we found was between the planner/scheduler and the hybrid execution system. In NMRA the planner is concerned with activities at a high-level of abstraction which encapsulates a detailed sequence of executivelevel commands. A fundamental objective for the planner is to allocate resources to the high-level activities so as to provide a time and resource envelope that will ensure correctness of execution for each executive-level detailed sequence. An interval based representation is suitable for this purpose. From this perspective the planner does not really need to know if a time interval pertains to an activity or a state. However, this knowledge is crucial to ensure correct execution. The executive is interested in the occurrence of events, i.e., the transition between time intervals in the planner's perspective. To generate the appropriate commands and set up the appropriate sensor monitors, the executive needs to know if an event is controllable (the executive needs to send a command), observable (the executive expects sensory information) or neither (the executive can deduce information on the state on the basis of the domain model). Our approach localizes such distinctions to the executive's knowledge representation. This frees the planner to reason eciently about intervals, and

enables us to move responsibility exibly between other architectural components (for example, let the control tasks handle an activity which was formerly decomposed by the executive, or vice-versa) without having to modify the planner's models. While heterogeneous representations have a number of bene ts, they also raise some diculties. Most signi cant of these are the possibility for models to diverge rather than converge, and the need to duplicate knowledge representation e orts. We have made some progress on this front by heading toward a more uni ed representation of some modeled properties. First, the uni ed modeling for MI/MR in Livingstone (see Section 3.3) has proven to be extremely useful. Second, we use code generation techniques to translate some modeled properties, such as device power requirements, into the di erent representations used for each computational engine. Ideally, we would like to head toward a single representation of the spacecraft (the one true model, a holy grail of AI), but we intend to do so always generalizing from powerful models capable of handling the complexities of our real-world domain.

5.2 Di erences with the mobot domain

Many of the AI autonomy architectures have been developed with respect to mobile robots (mobots). Two di erences in particular are the role of perception and failure handling in the two domains. Many of the problems of perception common in mobile robot architectures were not signi cant in our domain. NMRA is focused on the spacecraft's state, and sensing the state of a synthetic artifact is much easier than sensing and understanding a complex natural environment. Furthermore, only limited aspects of the relationship of the spacecraft to its environment were sensed using sophisticated sensors, e.g., spacecraft acceleration, spacecraft angular velocity, sun position. Results from such sensors are easy to understand and incorporate into the model of the spacecraft's state. Second, there are important di erences in the structure of unexpected contingencies between the spacecraft domain and the mobile robot domain. The major difference is that there are almost no serendipitous contingencies on spacecraft, because spacecraft are carefully designed to perform a narrow, speci c mission, and any deviation is considered a failure. By contrast, multiple outcomes of actions and unexpected contingencies for mobots are often dicult to dichotomize into success and failure; mobots can sometimes achieve their goals by performing random actions. This distinction is manifested in the design of the RAP language, which recognizes failure of a plan step, but does not provide a mechanism for failure recovery per se. Instead, failure recovery procedures must be written like any other method, to be triggered on the result and context of the failure rather than the failure itself. Moreover, mobots are typically concerned with failures in the interaction between robot and environment. These failures are typically intermittent. In the case of spacecraft, a permanent hardware failure will not go

away even if the system recovers this time. Having now limited capabilities, the agent must plan and execute behavior with new constraints in mind, and make future inferences relative to the new system state. This raises a need for a system-level approach to fault protection, which ultimately resulted in the important role of Livingstone and in several architectural requirements to support replanning in the case of failures. The New Millennium Remote Agent (NMRA) architecture is closely related to the 3T (three-tier) architecture described in (Bonasso et al. 1996). The 3T architecture consists of a deliberative component and a realtime control component connected by a reactive conditional sequencer. We and Bonasso both use RAPS (Firby 1978) as our sequencer, although we are developing a new sequencer which is more closely tailored to the demands of the spacecraft environment (Gat 1996).2 Our deliberator is a traditional AI planner based on the HSTS temporal database (Muscettola 1994), and our control component is a traditional spacecraft attitude control system (Hackney, Bernard, & Rasmussen 1993). We also add an architectural component explicitly dedicated to world modeling (the mode identi er), and distinguish between control and monitoring. In contrast to the system described by Bonasso, the prime mover in our system is the RAP sequencer, not the planner. The planner is viewed as a service invoked and controlled by the sequencer. This is necessary because computation is a limited resource (due to the hard time constraints) and so the relatively expensive operation of the planner must be carefully controlled. In this respect, our architecture follows the design of the ATLANTIS architecture (Gat 1992). The current state of the art in spacecraft autonomy is represented by the attitude and articulation control subsystem (AACS) on the Cassini spacecraft (Brown, Bernard, & Rasmussen 1995; Hackney, Bernard, & Rasmussen 1993) (which supplied the SOI scenario used in our prototype). The autonomy capabilities of Cassini include context-dependent command handling, resource management and fault protection. Planning is a ground (rather than on-board) function and on-board replanning is limited to a couple of prede ned contingencies. An extensive set of fault monitors is used to lter measurements and warn the system of both unacceptable and o -nominal behavior. Fault diagnosis and recovery are rule-based. That is, for every possible fault or set of faults, the monitor states leading to a particular diagnosis are explicitly encoded into rules. Likewise, the fault responses for each diagnosis are explicitly encoded by hand. Robustness is achieved in dicult-todiagnose situations by setting the system to a simple, known state from which capabilities are added incrementally until full capability is achieved or the fault is unambiguously identi ed. The NMRA architecture uses a model-based fault diagnosis system, adds an on-board
2 The esl system (Gat 1996) has now replaced RAPS as the core engine for the DS-1 Executive.


planner, and greatly enhances the capabilities of the onboard sequencer, resulting in a dramatic leap ahead in autonomy capability. Ahmed, Aljabri, & Eldred (1994) have also worked on architecture for autonomous spacecraft. Their architecture integrates planning and execution, using TCA (Simmons 1990) as a sequencing mechanism. However, they focused only on a subset of the problem, that of autonomous maneuver planning, which will be incorporated into our work as part of the DS-1 mission. Among the many general-purpose autonomy architectures is Guardian (Hayes-Roth 1995), a two-layer architecture which has been used for medical monitoring of intensive care patients. Like the spacecraft domain, intensive care has hard real-time deadlines imposed by the environment and operational criticality. One notable feature of the Guardian architecture is its ability to dynamically change the amount of computational resources being devoted to its various components. The NMRA architecture also has this ability, but the approaches are quite di erent. Guardian manages computational resources by changing the rates at which messages are sent to the various parts of the system. The NMRA architecture manages computational resources by giving the executive control over deliberative processes, which are managed according to the knowledge encoded in the RAPs. SOAR (Laird, Newell, & Rosenbloom 1987) is an architecture based on a general-purpose search mechanism and a learning mechanism that compiles the results of past searches for fast response in the future. SOAR has been used to control ight simulators, a domain which also has hard real-time constraints and operational criticality (Tambe et al. 1995). CIRCA (Musliner, Durfee, & Shin 1993) is an architecture that uses a slow AI component to provide guidance to a real-time scheduler that guarantees hard real-time response when possible. Noreils & Chatila (1995) describes a mobile robot control architecture that combines planning, execution, monitoring, and contingency recovery. Cypress is an architecture which combines a planning and an execution system (SIPE-II and PRS (George & Lansky 1987)) using a common representation called ACTS (Wilkins & Myers 1995). The main di erence between Cypress and our system is our use of an interval-based rather than an operator-based planner. This paper has described NMRA, an implemented architecture for autonomous spacecraft. The architecture was driven by a careful analysis of the spacecraft domain, and integrates traditional real-time monitoring and control with constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and recon guration. The implemented architecture was successfully demonstrated on an extremely challenging simulated spacecraft autonomy scenario. As a result, the architecture will control the rst ight of NASA's New Millennium Program (NMP). The space-


craft, NMP Deep Space One (DS-1), will launch in 1998 and will autonomously cruise to and y-by an asteroid and a comet. This will be the rst AI system to autonomously control an actual spacecraft. Our immediate work for DS-1 consists mainly in acquiring and validating models of the DS-1 spacecraft and in eliciting and addressing mission requirements. To make this possible, we are working on developing better tools for sharing models across the di erent heterogeneous architectural components, and for model veri cation and validation. Longer term, we see three major areas of research. First, our architecture could bene t from an increased use of simulation. Currently we use a simulator for development and testing the software. This could be extended to facilitate interactive knowledge acquisition and re nement, to improve projection in the planner, or to provide a tighter integration between planning and execution (Drummond, Bresina, & Swanson 1994; Levinson 1994). Second, our architecture leaves open issues of machine learning, which could be used to tune parameters in the control system, for optimizing search control in planning, or for modifying method selection priorities during execution. Third, we see substantial bene ts in having a single representation of the spacecraft, supporting multiple uses by processes of abstraction and translation. We believe that progress toward this goal is best made by generalizing from powerful, focused models capable of representing the complexities of a real-world domain. The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA and at the NASA Ames Research Center. We would like to acknowledge the invaluable contributions of Guy K. Man and Robert D. Rasmussen for their work in de ning a vision model for spacecraft autonomy that evolved into this e ort. In addition to the authors, the NMRA autonomy prototype was accomplished through the efforts of Charles Fry, Dennis DeCoste, Rob Sherwood, Kim Gostelow, Asif Ahmed, Hans Thomas, Illah Nourbakhsh, and Robert Kanefsky.
Ahmed, A.; Aljabri, A. S.; and Eldred, D. 1994. Demonstration of on-board maneuver planning using autonomous s/w architecture. In 8th Annual AIAA/USU Conference on Small Satellites. Bonasso, R. P.; Kortenkamp, D.; Miller, D.; and Slack, M. 1996. Experiences with an architecture for intelligent, reactive agents. JETAI. to appear. Brown, G.; Bernard, D.; and Rasmussen, R. 1995. Attitude and articulation control for the cassini spacecraft: A fault tolerance overview. In 14th AIAA/IEEE Digital Avionics Systems Conference. de Kleer, J., and Williams, B. C. 1987. Diagnosing multiple faults. Arti cial Intelligence 32(1):97{130. Reprinted in (Hamscher, Console, & de Kleer 1992).



de Kleer, J., and Williams, B. C. 1989. Diagnosis with behavioral modes. In Proceedings of IJCAI-89, 1324{1330. Reprinted in (Hamscher, Console, & de Kleer 1992). de Kleer, J., and Williams, B. C., eds. 1991. Arti cial Intelligence, volume 51. Elsevier. Drummond, M.; Bresina, J.; and Swanson, K. 1994. Justin-case scheduling. In Procs. of AAAI-94, 1098{1104. Cambridge, Mass.: AAAI. Firby, R. J. 1978. Adaptive execution in complex dynamic worlds. Ph.D. Dissertation, Yale University. Gat, E. 1992. Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling realworld mobile robots. In Procs. of AAAI-92. Cambridge, Mass.: AAAI. Gat, E. 1996. ESL: A language for supporting robust plan execution in embedded autonomous agents. In Pryor (1996). George , M. P., and Lansky, A. L. 1987. Procedural knowledge. Technical Report 411, Arti cial Intelligence Center, SRI International. Hackney, J.; Bernard, D.; and Rasmussen, R. 1993. The cassini spacecraft: Object oriented ight control software. In 1993 Guidance and Control Conference. Hamscher, W.; Console, L.; and de Kleer, J. 1992. Readings in Model-Based Diagnosis. San Mateo, CA: Morgan Kaufmann. Hayes-Roth, B. 1995. An architecture for adaptive intelligent systems. Arti cial Intelligence 72. Laird, J. E.; Newell, A.; and Rosenbloom, P. S. 1987. Soar: An architecture for general intelligence. Arti cial Intelligence 33(1). Levinson, R. 1994. A general programming language for uni ed planning and control. Arti cial Intelligence 76. Muscettola, N.; Pell, B.; Hansson, O.; and Mohan, S. 1995. Automating mission scheduling for space-based observatories. In Henry, G., and Eaton, J., eds., Robotic Telescopes: Current Capabilities, Present Developments, and Future Prospects for Automated Astronomy, number 79 in ASP Conf. Series. Provo, UT: Astronomical Society of the Paci c. Muscettola, N. 1994. HSTS: Integrating planning and scheduling. In Fox, M., and Zweben, M., eds., Intelligent Scheduling. Morgan Kaufmann. Musliner, D.; Durfee, E.; and Shin, K. 1993. Circa: A cooperative, intelligent, real-time control architecture. IEEE Transactions on Systems, Man, and Cybernetics 23(6). Noreils, F., and Chatila, R. 1995. Plan execution monitoring and control architecture for mobile robots. IEEE Transactions on Robotics and Automation. Pell, B.; Bernard, D. E.; Chien, S. A.; Gat, E.; Muscettola, N.; Nayak, P. P.; Wagner, M. D.; and Williams, B. C. 1996a. A remote agent prototype for spacecraft autonomy. In Proceedings of the SPIE Conference on Optical Science, Engineering, and Instrumentation. Pell, B.; Gat, E.; Keesing, R.; Muscettola, N.; and Smith, B. 1996b. Plan execution for autonomous spacecraft. In Pryor (1996). Pryor, L., ed. 1996. Proceedings of the AAAI Fall Symposium on Plan Execution. AAAI Press. Simmons, R. 1990. An architecture for coordinating planning, sensing, and action. In Procs. DARPA Workshop on

Innovative Approaches to Planning, Scheduling and Control, 292{297. San Mateo, CA: Morgan Kaufmann. Tambe, M.; Johnson, W. L.; Jones, R. M.; Koss, F.; Laird, J. E.; Rosenbloom, P. S.; and Schwamb, K. 1995. Intelligent agents for interactive simulation environments. AI Magazine 16(1):15{39. Weld, D. S., and de Kleer, J., eds. 1990. Readings in Qualitative Reasoning About Physical Systems. San Mateo, California: Morgan Kaufmann Publishers, Inc. Wilkins, D. E., and Myers, K. L. 1995. A common knowledge representation for plan generation and reactive execution. Journal of Logic and Computation. Williams, B. C., and Nayak, P. P. 1996. A model-based approach to reactive self-con guring systems. In Proceedings of AAAI-96, 971{978.



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