A system which generates plans for the dynamic environment of space operations is discussed. This system synthesizes plans by combining known operations under a set of physical, functional, and temperal constraints from various plan entities, which are modeled independently but combine in a flexible manner to suit dynamic planning needs. This independence allows the generation of a single plan source which can be compiled and applied to a variety of agents. The architecture blends elements of temperal logic, nonlinear planning, and object oriented constraint modeling to achieve its flexibility. This system was applied to the domain of the Intravehicular Activity (IVA) maintenance and repair aboard Space Station Freedom testbed

Davis, William S.

English

NASA Technical Reports Server

N91-20674
ARCHITECTURE FOR SPACECRAFT
OPERATIONS PLANNING
William S. Davis
Boeing Computer Services
Artificial Intelligence Center
PO Box 240002, MS JA-74
Huntsville, AL 35824
ABSTRACT
The dynamic environment of working in space
presents several challenges to planning space operations.
One challenge is the heterogeneous nature of the
operations to be performed, ranging from life science
experimentation to vehicle assembly. Generating plans
for such diverse tasks requires a system that exhibits
many domain-independent characteristics. A second
major challenge is that the performers of these operations
(plan agents) are also heterogeneous, possessing varying
skills and physical capabilities. In this regard, planning
must be possible both separately from, and in
consideration of, each agent's capabilities, whether
crewmember or robot. Finally, operating in space
encompasses unanticipated events. By definition, no pre-
established (or "canned") plan can accommodate.such
situations; hence a system is required which can
dynamically plan and replan according to evolving
knowledge.
We present such a system which generates plans
for the dynamic environment of space operations. This
system synthesizes plans by combining known
operations under a set of physical, functional, and
temporal constraints from various plan entities (agents,
objects, tasks), which are modeled independently but
combined in a flexible manner to suit dynamic planning
needs. This independence allows the generation of a
single plan source which can be compiled and applied to
a variety of agents. Another result of this modular
planning concept is the ability to generate different plans
from the same instructions, according to the objects used
in the plan. The architecture blends aspects of temporal
logic, nonlinear planning, and object-oriented constraint
modeling to achieve its flexibility. In our operations
testbed, we have applied this system to the domain of
IVA maintenance and repair aboard Space Station
Freedom, planning operations for (1) a crewmember,
generating English statements and interacting using
© 1990 The Boeing Company, all rights reserved
speech synthesis and voice recognition systems; (2) a
one-armed robot, generating robotic programming
instructions for sequential actions; and (3) a graphical
simulator, generating software commands for a
NASA/Vanderbilt simulator modeling one- and two-
armed robots.
I. Introduction
The domain of mission operations, as applied to
spacecraft such as Space Station Freedom (SSF), is
characterized by both long life cycles and a broad scope
of activities. For example, Space Station Freedom is
expected to perform orbital operations for approximately
thirty years, during which it will serve a variety of both
scientific and space exploration needs. Scientific
activities vary in types of experimentation, including life
sciences, materials processing, astronomy, automation
and robotics, and other disciplines applying primarily to
spacecraft operations. Space exploration includes the
orbit-based activities of assembling, testing, servicing,
launching, and recovering spacecraft (Lunar and Mars
Transfer Vehicles) and the surface-based activities of
navigation, experimentation, and outpost establishment
[ll. In addition, routine activities (housekeeping,
maintenance, repair) must be performed for each craft to
maintain proper operating status of its components.
2. Planning Issues Associated with Spacecraft
Operations
A recent NASA/OAST study has indicated a
long-term research focus on intelligent agents to support
automation for space exploration [2]. Planning for these
intelligent agents, however, faces challenges created by
properties inherent to the domain. First, the tasks
associated with spacecraft operations are heterogeneous.
As mentioned above, activities include experimental
research, spacecraft assembly and checkout, maintenance,
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repair, and safety monitoring Each of these areas is a
domain unto itself, thereby having its own set of
methods and beuristics to reason about actions. Hence,
it is clear that automated planning systems developed for
spacecraft operations must possess a significant amount
of domain-independence, and yet allow a modular
combination of sophisticated consWaints and heuristics in
applying specific problem-solving strategies. In
addition, with heterogeneous agents performing a
heterogeneous set of tasks, a proper balance must be
maintained between distributed and global reasoning.
Second, the agents involved in spacecraft
operations are also heterogeneous. Each agent possesses
a unique combination of physical and functional
capabilities, emphasizing distinct skills. Crewmembers,
for example, possess differing specialties (e.g., a flight
officer, a telerobotic expert, a life-sciences payload
specialist) while being similar in physical capabilities
and general functionality. On the other hand, on-orbit
robots differ greatly in physical capabilities (e.g., Flight
Telerobotic Servicer, Special Purpose Dextrous
Manipulator, spacecraft assembly cranes, IVA
maintenance robots), and by nature address different
functional needs.
Third, this environment is naturally dynamic.
For example, Space Station Freedom's payload
operations will be scheduled into contiguous 90-day
increments. Every increment will see new objectives and
priorities, while possibly maintaining continuing
activities from previous increments. Additionally, there
will always be the potential for immediate-term
adjustment of priorities to service needs such as
emergencies or windows of unexpected opportunity.
Such changes in activity schedules call for a strong
emphasis on both replanning and monitoring of plan
execution. Another dynamic aspect of the domain is
evolution. The environment about which the reasoning
occurs may evolve as systems are upgraded or
reconfignred, thus changing their associated operations
tasks. Crew agents will likely evolve as they expand and
improve their skill sets through increased space
habitation. Robotic agents will similarly evolve as
changes in technology produce enhanced capabilities.
These continuous changes in agents, their environment,
and their activities suggest a requirement for automated
planners that are highly flexible and extensible.
3. Strategies Addressing Planning Issues
This section discusses aspects of the architecture
of on-going investigations at Beeing's Defense and Space
Group, Huntsville Division, which address some of the
issues highlighted above. Figure 1 depicts an abstraction
of the architecture from which this discussion draws.
The intent of this research is to provide a foundation
which supports multiple aspects of the automated
planning problem, including representation, reasoning
schemes, and verification.
The issue of heterogeneous agents performing
heterogeneous tasks is primarily being addressed with a
methodology known as "agent-independent planning" [3].
Agent-independent planning generates activity plans
using only the constraints associated with the tasks to be
performed and the objects in the environment. Once a
plan is generated, an agent is selected for whom the plan
is validated, and instructions particular to that agent are
translated from the original plan representation (see
figure 2). This separation of constraints supports a
planning system that is robust in allowing the evolution
of tasks independently from the evolution of agents. The
primary representation for activity is based on the
temporal-interval logic established by James Allen [4].
Networks of temporal intervals form a hierarchically
abstracted plan space to describe complex tasks. The
system's constraints, which are arranged in a class
structure and are similar to the basic planning constraints
in SIPE [5], are dynamically grouped as combinations of
tasks, objects, and agents are constructed to satis[y
particular planning goals. The mechanism for plan
generation is a combination of nonlinear and temporal
planning techniques. While the very difficult problem of
reasoning about multiple interacting agents is not
currently addressed by this methodology, it does provide
mechanisms to reason about agents with varying skills,
and to determine validity among combinations of plans
and agents.
The temporal logic employed by the planning
system affords a rich scheme for representing activity
within the complex domain. However, maintaining
logical consistency among the temporal networks is
computationally expensive. Of particular difficulty is
dealing with the dynamic nature of spacecraft operations,
where domain information is sometimes incomplete.
This issue is being addressed by developing
nonmonotonic capabilities into the temporal logic used
by the planner. This provides the ability to retract
temporal assertions made by previous inferences without
disrupting previous propagations of constraints which
remain valid. This capability allows more extensive use
of temporal planning techniques, and in particular gives
rise to replanning algorithms which also exploit this
representation.
For automated planners to be successfully
adopted into the potentially-hazardous domain of space,
their reasoning methods must be verifiable to ensure safe
and consistent operation. This includes not only
verifying planner rationale, but also proper execution of
the plans by the intelligent agents. Validation of plans
during execution is being addressed by using techniques
of model-based diagnostics to monitor and detect faults.
Just as fault sensors in a physical system can be used to
signal failures and characterize symptoms, an agent's
sensors provide feedback which can be combined with
plan causality structures and models of the environment
276
to determine if and where a plan has failed. Fault
detection and isolation algorithms provide a focus for
replanning efforts to correct the plan error. I re-execution
plan validation is being addressed by investigating an
explanation system which describes the rationale used in
generating plans. This rationale is constructed from the
plan's causal links, temporal relationships, and models of
the domain environment (physical, functional, spatial,
etc.). Explanation will be a key factor in integrating
human agents with automated planning systems.
4. Directions for Research
In addition to the emphasis on intelligent agents,
a recent OAST workshop identified both multi-agent
reasoning and verification of automated functions as areas
needing technology development [6]. While these areas
encompass a broad spectrum of technology within
automated planning, this section underscores a few that
particularlyaddress spacecraft operations issues.
Multi-agent reasoning, in this context, concerns
not only the interaction and cooperation of agents but
also the mere notion of multiple, heterogeneous agents
available for a large variety of tasks. A critical factor to
successful reasoning, then, is how well the domain is
modeled. Planning for a complex envkonment requires
better representations to deal with the complexity.
Current logic and frame representations have afforded
continuity in developing planning technology, but tools
such as these should be extended to provide more
encompassing representations. For example, the
temporal interval logic has allowed significant advances
in plan reasoning, but must be enhanced to incorporate
complex domain issues such as nonmonotonicity.
Spacecraft operations should be modeled in a manner that
allows reasoning that can determine appropriate amounts
of interaction among agents. Such models should
address questions of how and when activity can be
distributed, where should reasoning be performed, and
how accurately must plans be developed.
Rich representational capability is needed to
model not only plan behavior, but also the agents and
the physical environment. Characteristics of agents
should be modeled to facilitate applying agents to meet
dynamic needs. The physical and functional models of
agents should provide planning constraints not only with
agent capabilities, but also with more abstract knowledge
such as skill proficiencies, task preferences, and
endurance. In terms of cooperating agents, the complex
issue of an agent's knowledge or belief of other agents is
compounded when certain assumptions can possess
hazardous consequences. Representations of the physical
environment should follow along the lines of model-
based reasoning. Insight into physical component
functionality will allow planners to match agent
capabilities with object behavior to achieve complex
goals.
Verification of automated functions, while
difficult, is intensified when applied to the spacecraft
operations domain, where there is usually little margin
for error. It is critical not only for reliable performance
of operations, but also for acceptability of automation
among spacecraft personnel. The rationale used in
deriving plans must be shown to be satisfactory in order
for crewmembers to rely on automated planning.
Verification of planning rationale should address multiple
levels of resolution, depending on the accuracy desired
and time needed to perform the verification. Knowledge-
based, model-based, and explanation-based methods are
viable emphasis areas for performing and reporting plan
validation. Ensuring reliable operation also extends into
plan execution and monitoring. Hence, replanning,
reactive planning, plan diagnosis, etc. are all valid areas
of attention. What will make them useful for spacecraft
operations is their integration with complex
representations, as described above, to perform more
comprehensive reasoning about all aspects of the
environment.
Ill
[2]
[3]
[4]
[51
[61
References
Report of the 90-Day Study on lluman Exploration
of the Moon and Mars, Aaron Cohen, Study Director,
NASA, November 1989.
Review of Human Exploration Program Technology
Requirements, NASA OAST presentation,
September 6, 1989.
Agent-lndependent Task Planning, W.S. Davis,
Proceedings of Fifth Conference on Artificial
Intelligence for Space Applications, Huntsville,
Alabama, May 1990.
Maintaining Knowledge About Temporal Intervals",
J.F. Allen, Communications of the ACM, vol. 26,
pp. 832-843, 1983.
Practical Planning: Extending the Classical AI
Planning Paradigm, D.E. Wilkins, Morgan
Kaufmann Publishers, San Mateo, California, 1988.
Technology for Space Station Evolution,
NASA/OAST Workshop, Dallas, Texas, January
1990.
277
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i :, agemplani teraction i _ plan execution
[ KnowloageBased: tJ
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Figure l: High-level Architectorc for Operations Planning
TASK
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• Plan
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Figure 2: Agent-Independent Planning Process
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Architecture for spacecraft operations planning

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