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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.

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At least 181 records · Page 10

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NASA NTRS1992

Automated assembly of a tetrahedral truss structure using machine vision

The Automated Structures Assembly Laboratory is a unique facility at NASA Langley Research Center used to investigate the robotic assembly of truss structures. Two special-purpose end-effectors have been used to assemble 102 truss members and 12 panels into an 8-meter diameter structure. One end-effector is dedicated to truss member insertion, while a second end-effector is used to install panels. Until recently, the robot motions required to construct the structure were developed iteratively using the facility hardware. Recent work at Langley has resulted in a compact machine vision system capable of providing position information relative to targets on the structure. Use of the vision system to guide the robot from an approach point 10 to 18 inches from the structure, offsetting model inaccuracies, permits robot motion based on calculated points as a first step toward use of preplanned paths from an automated path planner. This paper presents recent work at Langley highlighting the application of the machine vision system during truss member insertion.

Doggett, William R.↗

NASA NTRS1989

Design and implementation of a multi-sensor robot system for printed circuit board insertion

The design and implementation of a robot system equipped with multiple sensors are described. The robotic assembly system couples the task sequence of the robot with information from different sensors, enabling the system to handle uncertainties encountered during task execution. The target task involves the insertion of a printed circuit board into a card cage. The sensors used are an overhead three-dimensional vision system, a fingertip cross-fire sensor, and a 6-DOF force/torque sensor. The vision system is used to locate the printed circuit boards and the insertion slots. Information provided by the cross-fire sensor is used to complement the information from the vision system in order to obtain a proper grasping location on the printed circuit board. A fuzzy-logic-based system is used to interpret the forces and torques generated by the force/torque sensor during the insertion process. The repeated success of this assembly task over many experimental trials verifies the modeling and controller assumptions.

Sood, Deepak↗

DOE OSTI2020

Radiation Testing for Nuclear Inspection Systems (Final CRADA Report)

Higher-radiation hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems has developed a radiation-hardened vision system for Nuclear Energy applications. The high-radiation hardness testing of this vision system was conducted using the Argonne Low-Energy Accelerator Facility (LEAF).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1985

Fuzzy vision - Multiple inputs speed image understanding

The fuzzy vision system designed for the interpretation of multiple successive images is described. The system is noise insensitive and can be mapped directly onto parallel processing hardware. The system consists of a region generator and a viewer which access a common semantic net; the components and operation of these subsystems are examined. The advantages and disadvantages of the fuzzy vision system are discussed. Diagrams of the region generator, viewer, and a semantic net are provided. An example depicting the operation of the fuzzy vision system is presented.

Meier, R. J., Jr.↗

NASA NTRS1984

Artificial intelligence, expert systems, computer vision, and natural language processing

An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.

Gevarter, W. B.↗

NASA NTRS1981

Marking parts to aid robot vision

The premarking of parts for subsequent identification by a robot vision system appears to be beneficial as an aid in the automation of certain tasks such as construction in space. A simple, color coded marking system is presented which allows a computer vision system to locate an object, calculate its orientation, and determine its identity. Such a system has the potential to operate accurately, and because the computer shape analysis problem has been simplified, it has the ability to operate in real time.

Bales, J. W.↗

NASA NTRS1994

A smart telerobotic system driven by monocular vision

A robotic system that accepts autonomously generated motion and control commands is described. The system provides images from the monocular vision of a camera mounted on a robot's end effector, eliminating the need for traditional guidance targets that must be predetermined and specifically identified. The telerobotic vision system presents different views of the targeted object relative to the camera, based on a single camera image and knowledge of the target's solid geometry.

Defigueiredo, R. J. P.↗

NASA NTRS1994

Low computation vision-based navigation for a Martian rover

Construction and design details of the Mobot Vision System, a small, self-contained, mobile vision system, are presented. This system uses the view from the top of a small, roving, robotic vehicle to supply data that is processed in real-time to safely navigate the surface of Mars. A simple, low-computation algorithm for constructing a 3-D navigational map of the Martian environment to be used by the rover is discussed.

Gavin, Andrew S.↗

NASA NTRS1994

VAS: A Vision Advisor System combining agents and object-oriented databases

A model-based approach to identifying and finding the orientation of non-overlapping parts on a tray has been developed. The part models contain both exact and fuzzy descriptions of part features, and are stored in an object-oriented database. Full identification of the parts involves several interacting tasks each of which is handled by a distinct agent. Using fuzzy information stored in the model allowed part features that were essentially at the noise level to be extracted and used for identification. This was done by focusing attention on the portion of the part where the feature must be found if the current hypothesis of the part ID is correct. In going from one set of parts to another the only thing that needs to be changed is the database of part models. This work is part of an effort in developing a Vision Advisor System (VAS) that combines agents and objected-oriented databases.

Eilbert, James L.↗

NASA NTRS1992

A computer architecture for intelligent machines

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Lefebvre, D. R.↗

NASA NTRS1991

A computer architecture for intelligent machines

The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Lefebvre, D. R.↗

NASA NTRS1988

Processing Welding Images For Robot Control

Image data from two distinct windows used to locate weld features. Analyzer part of vision system described in companion article, "Image Control in Automatic Welding Vision System" (MFS-26035). Horizontal video lines define windows for viewing unwelded joint and weld pool. Data from picture elements outside windows not processed. Widely-separated local features carry no significance, but closely spaced features indicate welding feature. Image processor assigns confidence level to group of local features according to spacing and pattern.

Richardson, Richard W.↗

NASA NTRS1997

Sensory Interactive Teleoperator Robotic Grasping

As the technological world strives for efficiency, the need for economical equipment that increases operator proficiency in minimal time is fundamental. This system links a CCD camera, a controller and a robotic arm to a computer vision system to provide an alternative method of image analysis. The machine vision system which was employed possesses software tools for acquiring and analyzing images which are received through a CCD camera. After feature extraction on the object in the image was performed, information about the object's location, orientation and distance from the robotic gripper is sent to the robot controller so that the robot can manipulate the object.

Alark, Keli↗

NASA NTRS2004

An Integrated Vision-Based System for Spacecraft Attitude and Topology Determination for Formation Flight Missions

With the space industry's increasing focus upon multi-spacecraft formation flight missions, the ability to precisely determine system topology and the orientation of member spacecraft relative to both inertial space and each other is becoming a critical design requirement. Topology determination in satellite systems has traditionally made use of GPS or ground uplink position data for low Earth orbits, or, alternatively, inter-satellite ranging between all formation pairs. While these techniques work, they are not ideal for extension to interplanetary missions or to large fleets of decentralized, mixed-function spacecraft. The Vision-Based Attitude and Formation Determination System (VBAFDS) represents a novel solution to both the navigation and topology determination problems with an integrated approach that combines a miniature star tracker with a suite of robust processing algorithms. By combining a single range measurement with vision data to resolve complete system topology, the VBAFDS design represents a simple, resource-efficient solution that is not constrained to certain Earth orbits or formation geometries. In this paper, analysis and design of the VBAFDS integrated guidance, navigation and control (GN&C) technology will be discussed, including hardware requirements, algorithm development, and simulation results in the context of potential mission applications.

Rogers, Aaron↗

NASA NTRS1991

Multispectral Image Processing for Plants

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

Miles, Gaines E.↗

NASA NTRS1985

Visual monitoring of autonomous life sciences experimentation

The goal of this research project is the development of a computer vision system to monitor and control life sciences experimentation on board space stations. The vision system is organized as a multiprocessor system with distributed processes selectively analyzing hierarchical imagery in order to monitor and control the appropriate instrumentation.

Blank, G. E.↗

NASA NTRS2004

Latency Requirements for Head-Worn Display S/EVS Applications

NASA s Aviation Safety Program, Synthetic Vision Systems Project is conducting research in advanced flight deck concepts, such as Synthetic/Enhanced Vision Systems (S/EVS), for commercial and business aircraft. An emerging thrust in this activity is the development of spatially-integrated, large field-of-regard information display systems. Head-worn or helmet-mounted display systems are being proposed as one method in which to meet this objective. System delays or latencies inherent to spatially-integrated, head-worn displays critically influence the display utility, usability, and acceptability. Research results from three different, yet similar technical areas flight control, flight simulation, and virtual reality are collectively assembled in this paper to create a global perspective of delay or latency effects in head-worn or helmet-mounted display systems. Consistent definitions and measurement techniques are proposed herein for universal application and latency requirements for Head-Worn Display S/EVS applications are drafted. Future research areas are defined.

Bailey, Randall E.↗

NASA NTRS2021

Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts

Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.

Chen, Allen↗