Decomposability and scalability in space-based observatory scheduling

In this paper, we discuss issues of problem and model decomposition within the HSTS scheduling framework. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) scheduling problem, motivated by the limitations of the current solution and, more generally, the insufficiency of classical planning and scheduling approaches in this problem context. We first summarize the salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research. Then, we describe some key problem decomposition techniques supported by HSTS and underlying our integrated planning and scheduling approach, and we discuss the leverage they provide in solving space-based observatory scheduling problems

State-based scheduling: An architecture for telescope observation scheduling

The applicability of constraint-based scheduling, a methodology previously developed and validated in the domain of factory scheduling, is extended to problem domains that require attendance to a wider range of state-dependent constraints. The problem of constructing and maintaining a short-term observation schedule for the Hubble Space Telescope (HST), which typifies this type of domain is the focus of interest. The nature of the constraints encountered in the HST domain is examined, system requirements are discussed with respect to utilization of a constraint-based scheduling methodology in such domains, and a general framework for state-based scheduling is presented

Constraint-based integration of planning and scheduling for space-based observatory management

Progress toward the development of effective, practical solutions to space-based observatory scheduling problems within the HSTS scheduling framework is reported. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) short-term observation scheduling problem. The work was motivated by the limitations of the current solution and, more generally, by the insufficiency of classical planning and scheduling approaches in this problem context. HSTS has subsequently been used to develop improved heuristic solution techniques in related scheduling domains and is currently being applied to develop a scheduling tool for the upcoming Submillimeter Wave Astronomy Satellite (SWAS) mission. The salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research are summarized. Then, some key problem decomposition techniques underlying the integrated planning and scheduling approach to the HST problem are described; research results indicate that these techniques provide leverage in solving space-based observatory scheduling problems. Finally, more recently developed constraint-posting scheduling procedures and the current SWAS application focus are summarized

Investigations into Generalization of Constraint-Based Scheduling Theories with Applications to Space Telescope Observation Scheduling

This final report summarizes research performed under NASA contract NCC 2-531 toward generalization of constraint-based scheduling theories and techniques for application to space telescope observation scheduling problems. Our work into theories and techniques for solution of this class of problems has led to the development of the Heuristic Scheduling Testbed System (HSTS), a software system for integrated planning and scheduling. Within HSTS, planning and scheduling are treated as two complementary aspects of the more general process of constructing a feasible set of behaviors of a target system. We have validated the HSTS approach by applying it to the generation of observation schedules for the Hubble Space Telescope. This report summarizes the HSTS framework and its application to the Hubble Space Telescope domain. First, the HSTS software architecture is described, indicating (1) how the structure and dynamics of a system is modeled in HSTS, (2) how schedules are represented at multiple levels of abstraction, and (3) the problem solving machinery that is provided. Next, the specific scheduler developed within this software architecture for detailed management of Hubble Space Telescope operations is presented. Finally, experimental performance results are given that confirm the utility and practicality of the approach

Automating Mission Scheduling for Space-Based Observatories

In this paper we describe the use of our planning and scheduling framework, HSTS, to reduce the complexity of science mission planning. This work is part of an overall project to enable a small team of scientists to control the operations of a spacecraft. The present process is highly labor intensive. Users (scientists and operators) rely on a non-codified understanding of the different spacecraft subsystems and of their operating constraints. They use a variety of software tools to support their decision making process. This paper considers the types of decision making that need to be supported/automated, the nature of the domain constraints and the capabilities needed to address them successfully, and the nature of external software systems with which the core planning/scheduling engine needs to interact. HSTS has been applied to science scheduling for EUVE and Cassini and is being adapted to support autonomous spacecraft operations in the New Millennium initiative

An operations and command systems for the extreme ultraviolet explorer

About 40% of the budget of a scientific spacecraft mission is usually consumed by Mission Operations & Data Analysis (MO&DA) with MO driving these costs. In the current practice, MO is separated from spacecraft design and comes in focus relatively late in the mission life cycle. As a result, spacecraft may be designed that are very difficult to operate. NASA centers have extensive MO expertise but often lessons learned in one mission are not exploited for other parallel or future missions. A significant reduction of MO costs is essential to ensure a continuing and growing access to space for the scientific community. We are addressing some of these issues with a highly automated payload operations and command system for an existing mission, the Extreme Ultraviolet Explorer (EUVE). EUVE is currently operated jointly by the Goddard Space Flight Center (GSFC), responsible for spacecraft operations, and the Center for Extreme Ultraviolet Astrophysics (CEA) of the University of California, Berkeley, which controls the telescopes and scientific instruments aboard the satellite. The new automated system is being developed by a team including personnel from the NASA Ames Research Center (ARC), the Jet Propulsion Laboratory (JPL) and the Center for EUV Astrophysics (CEA). An important goal of the project is to provide AI-based technology that can be easily operated by nonspecialists in AI. Another important goal is the reusability of the techniques for other missions. Models of the EUVE spacecraft need to be built both for planning/scheduling and for monitoring. In both cases, our modeling tools allow the assembly of a spacecraft model from separate sub-models of the various spacecraft subsystems. These sub-models are reusable; therefore, building mission operations systems for another small satellite mission will require choosing pre-existing modules, reparametrizing them with respect to the actual satellite telemetry information, and reassembling them in a new model. We briefly describe the EUVE mission and indicate why it is particularly suitable for the task. Then we briefly outline our current work in mission planning/scheduling and spacecraft and instrument health monitoring

Planning the Behavior of Dynamical Systems

Two important factors hinder our ability to address large pIanning problems. On one hand, our understanding of planning is not independent from specific planning frameworks. On the other hand, current planning fraxneworks lack modularity, a key factor for "divide and conquer" approaches to large problems. This paper addresses the formal definition of planning, points out some limit&tions of the current planning frameworks, and describes a new planning framework that overcomes these limitations