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This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbitin 1 (EO-1) missions. The planning system, ASPEN, is used to autonomously schedule the daily activites of the satellite.
The MAPGEN [2] (Mixed-initiative Activity Plan GENerator) planning system is a great example of a hybrid procedural/declarative system where the advantages of each are leveraged to produce an effective planner/scheduler for Mars Exploration Rover tactical planning. We explore the adaptation of the same domain to an entirely declarative planning system (ASPEN [4] Activity Scheduling and Planning ENvironment), and demonstrate that, with some translation, much of the procedural knowledge encoding is amenable to a declarative knowledge encoding.
This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbiting 1 (EO-1) mission. The planning system, ASPEN, is used to autonomously schedule the daily activites of the satellite. The satellite and operations constraints are encoded within a software model used by the planner.
In an effort to demonstrate the efficacy of automated planning and scheduling techniques for large missions, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] and CLASP (Compressed Large-scale Activity Scheduling and Planning) [2] to the domain of scheduling high-level science goals into conflict-free operations plans for Titan encounters by the Cassini spacecraft.
In an effort to demonstrate the efficacy of automated planning and scheduling techniques for large missions, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] and CLASP (Compressed Large-scale Activity Scheduling and Planning) [2] to the domain of scheduling high-level science goals into conflict-free operations plans for Titan encounters by the Cassini spacecraft.
By studying the Orbital Express mission, modeling the spacecraft and scenarios, and testing the system, a technique has been developed that uses recursive decomposition to represent procedural actions declaratively, schema-level uncertainty reasoning to make uncertainty reasoning tractable, and lightweight, natural language processing to automatically parse procedures to produce declarative models. Schema-level uncertainty reasoning has, at its core, the basic assumption that certain variables are uncertain, but not independent. Once any are known, then the others become known. This is important where a variable is uncertain for an action and many actions of the same type exist in the plan. For example, if the number of retries to purge pump lines was unknown (but bounded), and each attempt required a sub-plan, then, once the correct number of attempts required for a purge was known, it would likely be the same for all subsequent purges. This greatly reduces the space of plans that needs to be searched to ensure that all executions are feasible. To accommodate changing scenario procedures, each is ingested into a tabular format in temporal order, and a simple natural-language parser is used to read each step and to derive the impact of that step on memory, power, and communications. Then an ASPEN (Activity Scheduling and Planning Environment) model is produced based on this analysis. The model is tested and further changed by hand, if necessary, to reflect the actual procedure. This results in a great savings of time used for modeling procedures. Many processes that need to be modeled in ASPEN (a declarative system) are, in fact, procedural. ASPEN includes the ability to model activities in a hierarchical fashion, but this representation breaks down if there is a practically unbounded number of sub-activities and decomposition topologies. However, if recursive decomposition is allowed, HTN-like encodings are enabled to represent most procedural phenomena. For example, if a switch requires a variable (but known at the time of the attempt) number of attempts to switch on, one can recurse on the number of remaining switch attempts and decompose into either the same switching activity with one less required attempt, or not decompose at all (or decompose into a dummy task), resulting in the end of the decomposition. In fact, any bounded procedural behavior can be modeled using recursive decompositions assuming that the variables impinging the disjunctive decomposition decision are computable at the time that the decision is made. This enables one to represent tasks that are controlled outside of the scheduler, but that the scheduler must accommodate, without requiring one to give a declarative model of the procedural behavior.
Leveraging previous work on scheduling personnel for space mission operations, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] to the domain of scheduling personnel for operations of the Mars Science Laboratory. Automated scheduling of personnel is not new. We compare our representations to a sampling of employee scheduling systems available with respect to desired features. We described the constraints required by MSL personnel schedulers and how each is handled by the scheduling algorithm.
The Automated Scheduling and Planning Environment (ASPEN) computer program has been updated to version 3.0. ASPEN is a modular, reconfigurable, application software framework for solving batch problems that involve reasoning about time, activities, states, and resources. Applications of ASPEN can include planning spacecraft missions, scheduling of personnel, and managing supply chains, inventories, and production lines. ASPEN 3.0 can be customized for a wide range of applications and for a variety of computing environments that include various central processing units and random access memories.
The Orbital Express satellite servicing demonstrator program is a DARPA program aimed at developing "a safe and cost-effective approach to autonomously service satellites in orbit". The system consists of: a) the Autonomous Space Transport Robotic Operations (ASTRO) vehicle, under development by Boeing Integrated Defense Systems, and b) a prototype modular next-generation serviceable satellite, NEXTSat, being developed by Ball Aerospace. Flexibility of ASPEN: a) Accommodate changes to procedures; b) Accommodate changes to daily losses and gains; c) Responsive re-planning; and d) Critical to success of mission planning Auto-Generation of activity models: a) Created plans quickly; b) Repetition/Re-use of models each day; and c) Guarantees the AML syntax. One SRP per day vs. Tactical team
This software translates MAPGEN (Europa and APGEN) domains to ASPEN, and the resulting domain can be used to perform planning for the Mars Exploration Rover (MER). In other words, this is a conversion of two distinct planning languages (both declarative and procedural) to a third (declarative) planning language in order to solve the problem of faithful translation from mixed-domain representations into the ASPEN Modeling Language. The MAPGEN planning system is an example of a hybrid procedural/declarative system where the advantages of each are leveraged to produce an effective planner/scheduler for MER tactical planning. The adaptation of the planning system (ASPEN) was investigated, and, with some translation, much of the procedural knowledge encoding is amenable to declarative knowledge encoding. The approach was to compose translators from the core languages used for adapting MAGPEN, which consists of Europa and APGEN. Europa is a constraint- based planner/scheduler where domains are encoded using a declarative model. APGEN is also constraint-based, in that it tracks constraints on resources and states and other variables. Domains are encoded in both constraints and code snippets that execute according to a forward sweep through the plan. Europa and APGEN communicate to each other using proxy activities in APGEN that represent constraints and/or tokens in Europa. The composition of a translator from Europa to ASPEN was fairly straightforward, as ASPEN is also a declarative planning system, and the specific uses of Europa for the MER domain matched ASPEN s native encoding fairly closely. On the other hand, translating from APGEN to ASPEN was considerably more involved. On the surface, the types of activities and resources one encodes in APGEN appear to match oneto- one to the activities, state variables, and resources in ASPEN. But, when looking into the definitions of how resources are profiled and activities are expanded, one sees code snippets that access various information available during planning for the moment in time being planned to decide at the time what the appropriate profile or expansion is. APGEN is actually a forward (in time) sweeping discrete event simulator, where the model is composed of code snippets that are artfully interleaved by the engine to produce a plan/schedule. To solve this problem, representative code is simulated as a declarative series of task expansions. Predominantly, three types of procedural models were translated: loops, if statements, and code blocks. Loops and if statements were handled using controlled task expansion, and code blocks were handled using constraint networks that maintained the generation of results based on what the order of execution would be for a procedural representation. One advantage with respect to performance for MAPGEN is the use of APGEN s GUI. This GUI is written in C++ and Motif, and performs very well for large plans.
The Automated Scheduling and Planning Environment (ASPEN) computer program has been updated to version 3.0. ASPEN as a whole (up to version 2.0) has been summarized, and selected aspects of ASPEN have been discussed in several previous NASA Tech Briefs articles. Restated briefly, ASPEN is a modular, reconfigurable, application software framework for solving batch problems that involve reasoning about time, activities, states, and resources. Applications of ASPEN can include planning spacecraft missions, scheduling of personnel, and managing supply chains, inventories, and production lines. ASPEN 3.0 can be customized for a wide range of applications and for a variety of computing environments that include various central processing units and randomaccess memories. Domain-specific reasoning modules (e.g., modules for determining orbits for spacecraft) can easily be plugged into ASPEN 3.0. Improvements over other, similar software that have been incorporated into ASPEN 3.0 include a provision for more expressive time-line values, new parsing capabilities afforded by an ASPEN language based on Extensible Markup Language, improved search capabilities, and improved interfaces to other, utility-type software (notably including MATLAB).
This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from highlevel science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules.
A computer program for scheduling the activities of multiple agents that share limited resources has been incorporated into the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles. In the original intended application, the agents would be multiple spacecraft and/or robotic vehicles engaged in scientific exploration of distant planets. The program could also be used on Earth in such diverse settings as production lines and military maneuvers. This program includes a planning/scheduling subprogram of the iterative repair type that reasons about the activities of multiple agents at abstract levels in order to greatly improve the scheduling of their use of shared resources. The program summarizes the information about the constraints on, and resource requirements of, abstract activities on the basis of the constraints and requirements that pertain to their potential refinements (decomposition into less-abstract and ultimately to primitive activities). The advantage of reasoning about summary information is that time needed to find consistent schedules is exponentially smaller than the time that would be needed for reasoning about the same tasks at the primitive level.
The Orbital Express space mission was a Defense Advanced Research Projects Agency (DARPA) lead demonstration of on-orbit satellite servicing scenarios, autonomous rendezvous, fluid transfers of hydrazine propellant, and robotic arm transfers of Orbital Replacement Unit (ORU) components. Boeing's Autonomous Space Transport Robotic Operations (ASTRO) vehicle provided the servicing to the Ball Aerospace's Next Generation Serviceable Satellite (NextSat) client. For communication opportunities, operations used the high-bandwidth ground-based Air Force Satellite Control Network (AFSCN) along with the relatively low-bandwidth GEO-Synchronous space-borne Tracking and Data Relay Satellite System (TDRSS) network. Mission operations were conducted out of the RDT&E Support Complex (RSC) at the Kirtland Air Force Base in New Mexico. All mission objectives were met successfully: The first of several autonomous rendezvous was demonstrated on May 5, 2007; autonomous free-flyer capture was demonstrated on June 22, 2007; the fluid and ORU transfers throughout the mission were successful. Planning operations for the mission were conducted by a team of personnel including Flight Directors, who were responsible for verifying the steps and contacts within the procedures, the Rendezvous Planners who would compute the locations and visibilities of the spacecraft, the Scenario Resource Planners (SRPs), who were concerned with assignment of communications windows, monitoring of resources, and sending commands to the ASTRO spacecraft, and the Mission planners who would interface with the real-time operations environment, process planning products and coordinate activities with the SRP. The SRP position was staffed by JPL personnel who used the Automated Scheduling and Planning ENvironment (ASPEN) to model and enforce mission and satellite constraints. The lifecycle of a plan began three weeks outside its execution on-board. During the planning timeframe, many aspects could change the plan, causing the need for re-planning. These variable factors, ranging from shifting contact times to ground-station closures and required maintenance times, are discussed along with the flexibility of the ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission. This paper will present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work. A description of ground operations after the SRP deliveries were made is included, and we briefly discuss lessons learned from the planning perspective and future work.
A computer program enables construction and optimization of plans for activities that are directed toward achievement of goals that are interdependent. Goal interdependence is defined as the achievement of one or more goals affecting the desirability or priority of achieving one or more other goals. This program is overlaid on the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been described in a number of prior NASA Tech Briefs articles. Unlike other known or related planning programs, this program considers interdependences among goals that can change between problems and provides a language for easily specifying such dependences. Specifications of the interdependences can be formulated dynamically and provided to the associated planning software as part of the goal input. Then an optimization algorithm provided by this program enables the planning software to reason about the interdependences and incorporate them into an overall objective function that it uses to rate the quality of a plan under construction and to direct its optimization search. In tests on a series of problems of planning geological experiments by a team of instrumented robotic vehicles (rovers) on new terrain, this program was found to enhance plan quality.
Topics include: Cloud Absorption Radiometer Autonomous Navigation System - CANS, Software Method for Computed Tomography Cylinder Data Unwrapping, Re-slicing, and Analysis, Discrete Data Qualification System and Method Comprising Noise Series Fault Detection, Simple Laser Communications Terminal for Downlink from Earth Orbit at Rates Exceeding 10 Gb/s, Application Program Interface for the Orion Aerodynamics Database, Hyperspectral Imager-Tracker, Web Application Software for Ground Operations Planning Database (GOPDb) Management, Software Defined Radio with Parallelized Software Architecture, Compact Radar Transceiver with Included Calibration, Software Defined Radio with Parallelized Software Architecture, Phase Change Material Thermal Power Generator, The Thermal Hogan - A Means of Surviving the Lunar Night, Micromachined Active Magnetic Regenerator for Low-Temperature Magnetic Coolers, Nano-Ceramic Coated Plastics, Preparation of a Bimetal Using Mechanical Alloying for Environmental or Industrial Use, Phase Change Material for Temperature Control of Imager or Sounder on GOES Type Satellites in GEO, Dual-Compartment Inflatable Suitlock, Modular Connector Keying Concept, Genesis Ultrapure Water Megasonic Wafer Spin Cleaner, Piezoelectrically Initiated Pyrotechnic Igniter, Folding Elastic Thermal Surface - FETS, Multi-Pass Quadrupole Mass Analyzer, Lunar Sulfur Capture System, Environmental Qualification of a Single-Crystal Silicon Mirror for Spaceflight Use, Planar Superconducting Millimeter-Wave/Terahertz Channelizing Filter, Qualification of UHF Antenna for Extreme Martian Thermal Environments, Ensemble Eclipse: A Process for Prefab Development Environment for the Ensemble Project, ISS Live!, Space Operations Learning Center (SOLC) iPhone/iPad Application, Software to Compare NPP HDF5 Data Files, Planetary Data Systems (PDS) Imaging Node Atlas II, Automatic Calibration of an Airborne Imaging System to an Inertial Navigation Unit, Translating MAPGEN to ASPEN for MER, Support Routines for In Situ Image Processing, and Semi-Supervised Eigenbasis Novelty Detection.