Results for “Remote Supervision” — Page 3

NASA NTRS1991

JPL space station telerobotic engineering prototype development: Advanced telerobotics system technology

The objective of the Advanced Telerobotics System Technology Task is to develop/prototype advanced telerobotics supervisory and shared control to enhance Intra-Vehicular Activity (IVA) teleoperation in the Space Station. The technology provides enhanced telerobotics capabilities while operating within the expected constraints of computation limitations, time delay, and bus bandwidth. A local site operator interface has also been developed for specifying teleoperation and shared control modes as well as supervised autonomous macros for execution at the remote site. The primary objective of the task is to transfer the advanced technology to appropriate flight centers to enhance the baseline Station capabilities.

Backes, Paul G.↗

NASA NTRS1993

The probabilistic neural network architecture for high speed classification of remotely sensed imagery

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

Chettri, Samir R.↗

NASA NTRSDate not supplied

iMETRO (Integrated Mobile Evaluation Testbed for Robotics Operations) Facility

Exploration crew time in space is precious – every hour could yield immense scientific discovery. However, overhead tasks such as logistics, maintenance, and assembly greatly limit crew time available for science and exploration. Robotic remote operations capabilities offer a solution, but operating mobile dexterous robots in human centered environments presents many unknowns and challenges, therefore testing is needed. iMETRO is a NASA JSC robotics test facility for terrestrial robotic technology adaptation for space exploration use cases, including logistics, maintenance, and science utilization. iMETRO focuses on Intra-Vehicular task environments, such as surface habitats, pressurized rover cabins, and space station modules (both Gateway & LEO). Its goal is to advance the Technology Readiness Levels (TRL) of remote space robot operations systems with Earth supervision.

Robotics↗

NASA NTRS1993

Telerobot local-remote control architecture for space flight program applications

The JPL Supervisory Telerobotics (STELER) Laboratory has developed and demonstrated a unique local-remote robot control architecture which enables management of intermittent communication bus latencies and delays such as those expected for ground-remote operation of Space Station robotic systems via the Tracking and Data Relay Satellite System (TDRSS) communication platform. The current work at JPL in this area has focused on enhancing the technologies and transferring the control architecture to hardware and software environments which are more compatible with projected ground and space operational environments. At the local site, the operator updates the remote worksite model using stereo video and a model overlay/fitting algorithm which outputs the location and orientation of the object in free space. That information is relayed to the robot User Macro Interface (UMI) to enable programming of the robot control macros. This capability runs on a single Silicon Graphics Inc. machine. The operator can employ either manual teleoperation, shared control, or supervised autonomous control to manipulate the intended object. The remote site controller, called the Modular Telerobot Task Execution System (MOTES), runs in a multi-processor VME environment and performs the task sequencing, task execution, trajectory generation, closed loop force/torque control, task parameter monitoring, and reflex action. This paper describes the new STELER architecture implementation, and also documents the results of the recent autonomous docking task execution using the local site and MOTES.

Zimmerman, Wayne↗

NASA NTRS1973

Unsupervised spatial clustering with spectral discrimination

The development of a computer program is reported for extracting features from remotely sensed data presented in digital image form. This computer program requires no human supervision or prejudgment and operates unassisted on the raw digital data. A condensed general background is included on remote sensing of earth features and a short synopsis on some of the most commonly used types of feature extraction techniques. Results obtained from the unsupervised feature extraction computer program along with a description and listing of the computer program are presented.

Jayroe, R. R., Jr.↗

NASA NTRS1998

Support Vector Machines for Hyperspectral Remote Sensing Classification

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Gualtieri, J. Anthony↗

NASA NTRSDate not supplied

Human Autonomy Teaming - m:N Operations

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust).

multi-vehicle control↗

NASA NTRSDate not supplied

NASA HAT Lab Activities

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust).

multi-vehicle control↗

NASA NTRSDate not supplied

m:N Working Group

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the background and progress of the m:N working group.

multi-vehicle control↗

NASA NTRSDate not supplied

Future Operations

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the future of GCS control stations and need for Human Systems Integration.

multi-vehicle control↗

NASA NTRSDate not supplied

m:N Operations and Future

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N).This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT).This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the barriers and requirements for future m:N operations.

multi-vehicle control↗

NASA NTRS1982

A means for utilizing ancillary information in multispectral classification

Attention is given to a method which makes it possible to incorporate information from ancillary data sources into the results of an existing classification of remotely sensed data. The method is based upon probabilistic label relaxation procedures which are used for imbedding spatial context data in image-labeling problems. The source of ancillary information is utilized in the form of a set of probabilities. These are introduced into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. A description is given of results of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the outlined manner.

Richards, J. A.↗

NASA NTRS1993

System architecture for asynchronous multi-processor robotic control system

The architecture for the Modular Telerobot Task Execution System (MOTES) as implemented in the Supervisory Telerobotics (STELER) Laboratory is described. MOTES is the software component of the remote site of a local-remote telerobotic system which is being developed for NASA for space applications, in particular Space Station Freedom applications. The system is being developed to provide control and supervised autonomous control to support both space based operation and ground-remote control with time delay. The local-remote architecture places task planning responsibilities at the local site and task execution responsibilities at the remote site. This separation allows the remote site to be designed to optimize task execution capability within a limited computational environment such as is expected in flight systems. The local site task planning system could be placed on the ground where few computational limitations are expected. MOTES is written in the Ada programming language for a multiprocessor environment.

Steele, Robert D.↗

NASA NTRS1997

Supervised Classification Techniques for Hyperspectral Data

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many mm-e spectral intervals than previous possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data provides a challenge to the current techniques for analyzing such data. Human experience in three dimensional space tends to mislead one's intuition of geometrical and statistical properties in high dimensional space, properties which must guide our choices in the data analysis process. In this paper high dimensional space properties are mentioned with their implication for high dimensional data analysis in order to illuminate the next steps that need to be taken for the next generation of hyperspectral data classifiers.

Jimenez, Luis O.↗

DOE OSTI2020

Human Factors Considerations for Automating Microreactors

The U.S. Nuclear Regulatory Commission (NRC) has interacted with vendors pursuing the commercialization of microreactors ( <10 MW th ). It is envisioned that microreactors could be assembled and fueled in a factory and shipped to a site. Many of the sites are expected to be remote locations requiring off-grid power or in some cases military bases. However, before this new class of nuclear reactor can be fully developed and implemented by designers, an effort must be made to explore the technical issues and provide reasonable assurance to the public regarding health and safety impacts centered on various technical issues. Prior works have examined many of these issues. One issue not yet fully explored is the possible change in role of the operations and support personnel. Due to the passive safety features of microreactors and their low level of nuclear material, the microreactor facilities may automate more functions and rely on inherent safety features more than its predecessor nuclear power plants. In some instances, human operators may not be located onsite and may instead be operating or monitoring the facility from a remote location. Some designs also call for operators to supervise and control multiple microreactors from the control room. This report explores the issues around reduced staffing of microreactors, highlights the historical safety functions associated with human operators, assesses current licensing requirements for appropriateness to varying levels of personnel support, and describes a recommended regulatory approach for reviewing the impact of reduced staff to the operation of microreactors. The report evaluates these issues through an extensive literature survey and Subject Matter Expert interviews. The literature covers research topics related to Human Factors Engineering as well as regulatory guidance given by the Nuclear Regulatory Commission.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1990

Processing Of Multispectral Data For Identification Of Rocks

Linear discriminant analysis and supervised classification evaluated. Report discusses processing of multispectral remote-sensing imagery to identify kinds of sedimentary rocks by spectral signatures in geological and geographical contexts. Raw image data are spectra of picture elements in images of seven sedimentary rock units exposed on margin of Wind River Basin in Wyoming. Data acquired by Landsat Thematic Mapper (TM), Thermal Infrared Multispectral Scanner (TIMS), and NASA/JPL airborne synthetic-aperture radar (SAR).

Evans, Diane L.↗

NASA NTRS2012

Mini AERCam for In-Space Inspection

The NASA Johnson Space Center Engineering Directorate has developed the Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) as a free-flying, robotic inspection vehicle intended for future external inspection and remote viewing of human spacecraft. The Mini AERCam technology demonstration unit has been successfully integrated into the approximate form and function of a nanosatellite flight system by leveraging the success of AERCam Sprint flight system and related free-flyer technology development. The Mini AERCam free flyer can be operated via remote piloting from a control station supporting teleoperation and supervised autonomous commanding, with functions such as automatic stationkeeping, point-to-point maneuvering, and automatic docking. Free-flyer testing has been conducted on an air-bearing table and in a six degree-of-freedom closed-loop orbital simulation, and enhancements have been made to provide additional capabilities for future space-based inspection. This presentation will provide a technical overview of the Mini AERCam development, including strategies for spacecraft integration.

Fredrickson, Steven E.↗

NASA NTRS1992

Ground-remote control for space station telerobotics with time delay

The study proposes a ground-remote telerobot control architecture which could be used for control of Space Station Freedom manipulators. The architecture provides two local-site operator control stations representing potential earth-based and remote Space Station-based operator control stations. A unified control system at the remote site provides autonomous, shared, and teleoperation control for single-and dual-arm task execution. An operational laboratory system which demonstrates the feasibility of various technologies in the proposed architecture, including teleoperation, shared control, and supervised autonomy, is described. Enhancements to the system currently under development, including remote site implementation in Ada, integration and control of a redundant 7-DOF manipulator, and local site advanced operator aids, are also described.

Backes, Paul G.↗