SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
If requests for scientific observations, rather than specific plans, are uplinked to an autonomous execution system on the vehicle, it would be able to adjust its execution based upon actual performance. Such a science-based executive control system had been developed and demonstrated for the Rocky7 research rover.
In this article we explore the design of an executive for an autonomous spacecraft. The executive is responsible for translating high-level commands, whether they come from the ground or from an on-board planner, into the low-level commands understood directly by the spacecraft hardware.
Autonomy is needed for future spacecraft to solve the problems of human operator overload and transmission delay. This paper describes the autonomous spacecraft executive for rendezvous and docking. It is an onboard expert system and has decision making capability for mission planning of nominal and contingency cases. The executive has been developed and verified using a hardware motion based simulator.
This paper describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of changing operating context.
Now that major assembly of the International Space Station (ISS) is complete, NASA's focus has turned to using this high fidelity in-space research testbed to not only advance fundamental science research, but also demonstrate and mature technologies and develop operational concepts that will enable future human exploration missions beyond low Earth orbit. The ISS as a Testbed for Analog Research (ISTAR) project was established to reduce risks for manned missions to exploration destinations by utilizing ISS as a high fidelity micro-g laboratory to demonstrate technologies, operations concepts, and techniques associated with crew autonomous operations. One of these focus areas is the development and execution of ISS Testbed for Analog Research (ISTAR) autonomous flight crew procedures intended to increase crew autonomy that will be required for long duration human exploration missions. Due to increasing communications delays and reduced logistics resupply, autonomous procedures are expected to help reduce crew reliance on the ground flight control team, increase crew performance, and enable the crew to become more subject-matter experts on both the exploration space vehicle systems and the scientific investigation operations that will be conducted on a long duration human space exploration mission. These tests make use of previous or ongoing projects tested in ground analogs such as Research and Technology Studies (RATS) and NASA Extreme Environment Mission Operations (NEEMO). Since the latter half of 2012, selected non-critical ISS systems crew procedures have been used to develop techniques for building ISTAR autonomous procedures, and ISS flight crews have successfully executed them without flight controller involvement. Although the main focus has been preparing for exploration, the ISS has been a beneficiary of this synergistic effort and is considering modifying additional standard ISS procedures that may increase crew efficiency, reduce operational costs, and raise the amount of crew time available for scientific research. The next phase of autonomous procedure development is expected to include payload science and human research investigations. Additionally, ISS International Partners have expressed interest in participating in this effort. The recently approved one-year crew expedition starting in 2015, consisting of one Russian and one U.S. Operating Segment (USOS) crewmember, will be used not only for long duration human research investigations but also for the testing of exploration operations concepts, including crew autonomy.
With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase raises not only the opportunities for science data collection, but also amplifies the amount of environment and rover state uncertainty that must be handled in rover operations. This paper describes how planning, scheduling and execution techniques can be used onboard a rover to autonomously generate and execute rover activities and in particular to handle new science opportunities that have been identified dynamically. We also discuss some of the particular challenges we face in supporting autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.
The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft. The spacecraft domain places a strong premium on autonomy and requires dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a wide variety of possible failures. To achieve this level of execution robustness, we have integrated a procedural executive based on generic procedures with a deductive model-based executive. A procedural executive provides sophisticated control constructs such as loops, parallel activity, locks, and synchronization which are used for robust schedule execution, hierarchical task decomposition, and routine configuration management. A deductive executive provides algorithms for sophisticated state inference and optimal failure recover), planning. The integrated executive enables designers to code knowledge via a combination of procedures and declarative models, yielding a rich modeling capability suitable to the challenges of real spacecraft control. The interface between the two executives ensures both that recovery sequences are smoothly merged into high-level schedule execution and that a high degree of reactivity is retained to effectively handle additional failures during recovery.
The Task Control Architecture (TCA) provides communication and coordination facilities to construct distributed, concurrent robotic systems. The use of TCA in a system that walks a legged robot through rugged terrain is described. The walking system, as originally implemented, had a sequential sense-plan-act control cycle. Utilizing TCA features for task sequencing and monitoring, the system was modified to concurrently plan and execute steps. Walking speed improved by over 30 percent, with only a relatively modest conversion effort.
The space station automation study (SSAS) was to develop informed technical guidance for NASA personnel in the use of autonomy and autonomous systems to implement space station functions. The initial step taken by NASA in organizing the SSAS was to form and convene a panel of recognized expert technologists in automation, space sciences and aerospace engineering to produce a space station automation plan.
The purpose of the Space Station Automation Study (SSAS) was to develop informed technical guidance for NASA personnel in the use of autonomy and autonomous systems to implement space station functions.
This viewgraph slide tutorial reviews methods for planning and scheduling events. The presentation reviews several methods and uses several examples of scheduling events for the successful and timely completion of the overall plan. Using constraint based models the presentation reviews planning with time, time representations in problem solving and resource reasoning.
Explore the source record for details and available documents.
The Aerial Onboard Autonomous Science Investigation System (AerOASIS) system provides autonomous planning and execution capabilities for aerial vehicles (see figure). The system is capable of generating high-quality operations plans that integrate observation requests from ground planning teams, as well as opportunistic science events detected onboard the vehicle while respecting mission and resource constraints. AerOASIS allows an airborne planetary exploration vehicle to summarize and prioritize the most scientifically relevant data; identify and select high-value science sites for additional investigation; and dynamically plan, schedule, and monitor the various science activities being performed, even during extended communications blackout periods with Earth.
The Planning Execution Monitoring (PEM) architecture is a design concept for developing autonomous cockpit command and control software. The PEM architecture is designed to reduce the operations costs in the space transportation system through the use of automation while improving safety and operability of the system. Specifically, the PEM autonomous framework enables automatic performance of many vehicle operations that would typically be performed by a human. Also, this framework supports varying levels of autonomous control, ranging from fully automatic to fully manual control. The PEM autonomous framework interfaces with the core flight software to perform flight procedures. It can either assist human operators in performing procedures or autonomously execute routine cockpit procedures based on the operational context. Most importantly, the PEM autonomous framework promotes and simplifies the capture, verification, and validation of the flight operations knowledge. Through a hierarchical decomposition of the domain knowledge, the vehicle command and control capabilities are divided into manageable functional "chunks" that can be captured and verified separately. These functional units, each of which has the responsibility to manage part of the vehicle command and control, are modular, re-usable, and extensible. Also, the functional units are self-contained and have the ability to plan and execute the necessary steps for accomplishing a task based upon the current mission state and available resources. The PEM architecture has potential for application outside the realm of spaceflight, including management of complex industrial processes, nuclear control, and control of complex vehicles such as submarines or unmanned air vehicles.
A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.
A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.
The Intelligent Distributed Execution Architecture (IDEA) is a real-time architecture that exploits artificial intelligence planning as the core reasoning engine for interacting autonomous agents. Rather than enforcing separate deliberation and execution layers, IDEA unifies them under a single planning technology. Deliberative and reactive planners reason about and act according to a single representation of the past, present and future domain state. The domain state behaves the rules dictated by a declarative model of the subsystem to be controlled, internal processes of the IDEA controller, and interactions with other agents. We present IDEA concepts - modeling, the IDEA core architecture, the unification of deliberation and reaction under planning - and illustrate its use in a simple example. Finally, we present several real-world applications of IDEA, and compare IDEA to other high-level control approaches.
NASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions.