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Knight, Russell

Publications and source records attributed to Knight, Russell.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Planning Coverage Campaigns for Mission Design and Analysis: Clasp for the Proposed DESDynI Mission

Mission design and analysis present challenges in that almost all variables are in constant flux, yet the goal is to achieve an acceptable level of performance against a concept of operations, which might also be in flux. To increase responsiveness, our approach is to use automated planning tools that allow for the continual modification of spacecraft, ground system, staffing, and concept of operations while returning metrics that are important to mission evaluation, such as area covered, peak memory usage, and peak data throughput. We have applied this approach to DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) mission design concept using the CLASP (Compressed Large-scale Activity Scheduler/Planner) planning system [7], but since this adaptation many techniques have changed under the hood for CLASP and the DESDynI mission concept has undergone drastic changes, including that it has been renamed the Earth Radar Mission. Over the past two years, we have run more than fifty simulations with the CLASP-DESDynI adaptation, simulating different mission scenarios with changing parameters including targets, swaths, instrument modes, and data and downlink rates. We describe the evolution of simulations through the DESDynI MCR (Mission Concept Review) and afterwards.

DESDynI (Deformation, Ecosystem Structure, and Dyn↗
NASA NTRS2012

A Comparison of Declarative and Hybrid Declarative-Procedural Models for Rover Operations

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.

declarative domain representations↗
NASA NTRS2011

Scheduling Results for the THEMIS Observation Scheduling Tool

We describe a scheduling system intended to assist in the development of instrument data acquisitions for the THEMIS instrument, onboard the Mars Odyssey spacecraft, and compare results from multiple scheduling algorithms. This tool creates observations of both (a) targeted geographical regions of interest and (b) general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool therefore must address both geometric and state/timing/resource constraints. We describe a tool that maps geometric polygon overlap constraints to set covering constraints using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm incorporating operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potential thousands of observations. This tool is currently under evaluation by the THEMIS observation planning team at Arizona State University.

Thermal Emission Imaging System (THEMIS)↗
NASA NTRS2010

Adapting ASPEN for Orbital Express

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.

Chouinard, Caroline↗
NASA NTRS2010

Coordinating Multiple Spacecraft Assets for Joint Science Campaigns

This paper describes technology to support a new paradigm of space science campaigns. These campaigns enable opportunistic science observations to be autonomously coordinated between multiple spacecraft. Coordinated spacecraft can consist of multiple orbiters, landers, rovers, or other in-situ vehicles (such as an aerobot). In this paradigm, opportunistic science detections can be cued by any of these assets where additional spacecraft are requested to take further observations characterizing the identified event or surface feature. Such coordination will enable a number of science campaigns not possible with present spacecraft technology. Examples from Mars include enabling rapid data collection from multiple craft on dynamic events such as new Mars dark slope streaks, dust-devils or trace gases. Technology to support the identification of opportunistic science events and/or the re-tasking of a spacecraft to take new measurements of the event is already in place on several individual missions such as the Mars Exploration Rover (MER) Mission and the Earth Observing One (EO1) Mission. This technology includes onboard data analysis techniques as well as capabilities for planning and scheduling. This paper describes how these techniques can be cue and coordinate multiple spacecraft in observing the same science event from their different vantage points.

automated commanding↗
NASA NTRS2009

Planning Complex Sequences Using Compressed Representations

A method that notably includes the use of compressed representations interleaved with non-compressed (time-line) representations of a general scheduling problem has been conceived as a means of increasing, by orders of magnitude, the speeds of computations needed for scheduling complex sequences of activities that include cycles wherein subsets of the activities and/or sequences are repeated. The method was originally intended to be used in scheduling large campaigns of scientific observations by instruments aboard a spacecraft. A typical such campaign could include observations of millions of targets, many observations to be made during long repeated passes. The method would also be useful on Earth for scheduling complex sequences of activities that include cycles.

Chien, Steve↗
NASA NTRS2008

Automated and Adaptive Mission Planning for Orbital Express

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.

scheduling↗
NASA NTRS2008

Orbital Express Mission Operations Planning and Resource Management using ASPEN

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

automated planning scheduling↗
NASA NTRS2008

Orbital Express Mission Operations Planning and Resource Management using ASPEN

As satellite equipment and mission operations become more costly, the drive to keep working equipment running with less man-power rises.Demonstrating the feasibility of autonomous satellite servicing was the main goal behind the Orbital Express (OE) mission. Planning the satellite mission operations for OE required the ability to create a plan which could be executed autonomously over variable conditions. The Automated-Scheduling and Planning Environment (ASPEN)tool, developed at the Jet Propulsion Laboratory, was used to create the schedule of events in each daily plan for the two satellites of the OE mission. This paper presents an introduction to the ASPEN tool, the constraints of the OE domain, the variable conditions that were presented within the mission, and the solution to operations that ASPEN provided. ASPEN has been used in several other domains, including research rovers, Deep Space Network scheduling research, and in flight operations for the ASE project's EO1 satellite. Related work is discussed, as are the future of ASPEN and the future of autonomous satellite servicing.

Automated Scheduling and Planning Environment (ASP↗
NASA NTRS2008

Automated Planning and Scheduling for Orbital Express (151)

The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Because the mission was a technology demonstration, pertinent planning information was learned during actual mission execution. This information led to amendments to procedures, which led to changes in the mission plan. In general, we used the ASPEN planner scheduler to generate and validate the mission plans. We enhanced ASPEN to enable it to reason about uncertainty. We also developed a model generator that would read the text of a procedure and translate it into an ASPEN model. These technologies had a significant impact on the success of the Orbital Express mission.

autonomy↗
NASA NTRS2007

CASPER Version 2.0

CASPER is designed to perform automated planning of interdependent activities within a system subject to requirements, constraints, and limitations on resources. In contradistinction to the traditional concept of batch planning followed by execution, CASPER implements a concept of continuous planning and replanning in response to unanticipated changes (including failures), integrated with execution. Improvements over other, similar software that have been incorporated into CASPER version 2.0 include an enhanced executable interface to facilitate integration with a wide range of execution software systems and supporting software libraries; features to support execution while reasoning about urgency, importance, and impending deadlines; features that enable accommodation to a wide range of computing environments that include various central processing units and random- access-memory capacities; and improved generic time-server and time-control features.

Chien, Steve↗
NASA NTRS2006

ASPEN Version 3.0

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.

Rabideau, Gregg↗
NASA NTRS2006

Solving the Swath Segment Selection Problem

Several artificial-intelligence search techniques have been tested as means of solving the swath segment selection problem (SSSP) -- a real-world problem that is not only of interest in its own right, but is also useful as a test bed for search techniques in general. In simplest terms, the SSSP is the problem of scheduling the observation times of an airborne or spaceborne synthetic-aperture radar (SAR) system to effect the maximum coverage of a specified area (denoted the target), given a schedule of downlinks (opportunities for radio transmission of SAR scan data to a ground station), given the limit on the quantity of SAR scan data that can be stored in an onboard memory between downlink opportunities, and given the limit on the achievable downlink data rate. The SSSP is NP complete (short for "nondeterministic polynomial time complete" -- characteristic of a class of intractable problems that can be solved only by use of computers capable of making guesses and then checking the guesses in polynomial time).

Knight, Russell↗
NASA NTRS2005

Optimizing observing sequence design for periodic and non-periodic phenomena : a Bayesian approach

In this paper we report on our progress on addressing these issues. We have developed an approximate expression for the uniformity of phase coverage that can be used when scheduling to assess candidate sample times. We describe the results obtained using this estimator, and compare them with detailed simulations. We describe our progress and plans for integrating optimizing criteria for both periodic and non-periodic observations into a single observation sequence.

Bayesian↗
NASA NTRS2005

Software for Automation of Real-Time Agents, Version 2

Version 2 of Closed Loop Execution and Recovery (CLEaR) has been developed. CLEaR is an artificial intelligence computer program for use in planning and execution of actions of autonomous agents, including, for example, Deep Space Network (DSN) antenna ground stations, robotic exploratory ground vehicles (rovers), robotic aircraft (UAVs), and robotic spacecraft. CLEaR automates the generation and execution of command sequences, monitoring the sequence execution, and modifying the command sequence in response to execution deviations and failures as well as new goals for the agent to achieve. The development of CLEaR has focused on the unification of planning and execution to increase the ability of the autonomous agent to perform under tight resource and time constraints coupled with uncertainty in how much of resources and time will be required to perform a task. This unification is realized by extending the traditional three-tier robotic control architecture by increasing the interaction between the software components that perform deliberation and reactive functions. The increase in interaction reduces the need to replan, enables earlier detection of the need to replan, and enables replanning to occur before an agent enters a state of failure.

Fisher, Forest↗
NASA NTRS2005

Computationally Lightweight Air-Traffic-Control Simulation

An algorithm for computationally lightweight simulation of automated air traffic control (ATC) at a busy airport has been derived. The algorithm is expected to serve as the basis for development of software that would be incorporated into flight-simulator software, the ATC component of which is not yet capable of handling realistic airport loads. Software based on this algorithm could also be incorporated into other computer programs that simulate a variety of scenarios for purposes of training or amusement.

Knight, Russell↗