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NASA NTRS1987

A database/knowledge structure for a robotics vision system

Desirable properties of robotics vision database systems are given, and structures which possess properties appropriate for some aspects of such database systems are examined. Included in the structures discussed is a family of networks in which link membership is determined by measures of proximity between pairs of the entities stored in the database. This type of network is shown to have properties which guarantee that the search for a matching feature vector is monotonic. That is, the database can be searched with no backtracking, if there is a feature vector in the database which matches the feature vector of the external entity which is to be identified. The construction of the database is discussed, and the search procedure is presented. A section on the support provided by the database for description of the decision-making processes and the search path is also included.

Dearholt, D. W.↗

NASA NTRS1993

Prototype Optical Correlator For Robotic Vision System

Known and unknown images fed in electronically at high speed. Optical correlator and associated electronic circuitry developed for vision system of robotic vehicle. System recognizes features of landscape by optical correlation between input image of scene viewed by video camera on robot and stored reference image. Optical configuration is Vander Lugt correlator, in which Fourier transform of scene formed in coherent light and spatially modulated by hologram of reference image to obtain correlation.

Scholl, Marija S.↗

NASA NTRS1980

Design of a dynamic test platform for autonomous robot vision systems

The concept and design of a dynamic test platform for development and evluation of a robot vision system is discussed. The platform is to serve as a diagnostic and developmental tool for future work with the RPI Mars Rover's multi laser/multi detector vision system. The platform allows testing of the vision system while its attitude is varied, statically or periodically. The vision system is mounted on the test platform. It can then be subjected to a wide variety of simulated can thus be examined in a controlled, quantitative fashion. Defining and modeling Rover motions and designing the platform to emulate these motions are also discussed. Individual aspects of the design process are treated separately, as structural, driving linkages, and motors and transmissions.

Rich, G. C.↗

NASA NTRS1990

Space environment robot vision system

A prototype twin-camera stereo vision system for autonomous robots has been developed at Goddard Space Flight Center. Standard charge coupled device (CCD) imagers are interfaced with commercial frame buffers and direct memory access to a computer. The overlapping portions of the images are analyzed using photogrammetric techniques to obtain information about the position and orientation of objects in the scene. The camera head consists of two 510 x 492 x 8-bit CCD cameras mounted on individually adjustable mounts. The 16 mm efl lenses are designed for minimum geometric distortion. The cameras can be rotated in the pitch, roll, and yaw (pan angle) directions with respect to their optical axes. Calibration routines have been developed which automatically determine the lens focal lengths and pan angle between the two cameras. The calibration utilizes observations of a calibration structure with known geometry. Test results show the precision attainable is plus or minus 0.8 mm in range at 2 m distance using a camera separation of 171 mm. To demonstrate a task needed on Space Station Freedom, a target structure with a movable I beam was built. The camera head can autonomously direct actuators to dock the I-beam to another one so that they could be bolted together.

Wood, H. John↗

NASA NTRS1989

Three-Dimensional Robotic Vision System

Stereoscopy and motion provide clues to outlines of objects. Digital image-processing system acts as "intelligent" automatic machine-vision system by processing views from stereoscopic television cameras into three-dimensional coordinates of moving object in view. Epipolar-line technique used to find corresponding points in stereoscopic views. Robotic vision system analyzes views from two television cameras to detect rigid three-dimensional objects and reconstruct numerically in terms of coordinates of corner points. Stereoscopy and effects of motion on two images complement each other in providing image-analyzing subsystem with clues to natures and locations of principal features.

Nguyen, Thinh V.↗

NASA NTRS1988

Automatic inspection of analog and digital meters in a robot vision system

A critical limitation of most of the robots utilized in industrial environments arises due to their inability to utilize sensory feedback. This forces robot operation into totally preprogrammed or teleoperation modes. In order to endow the new generation of robots with higher levels of autonomy techniques for sensing of their work environments and for accurate and efficient analysis of the sensory data must be developed. In this paper detailed development of vision system modules for inspecting various types of meters, both analog and digital, encountered in a robotic inspection and manipulation tasks are described. These modules are tested using industrial robots having multisensory input capability.

Trivedi, Mohan M.↗

NASA NTRS1989

Optical calculation of correlation filters for a robotic vision system

A method is presented for designing optical correlation filters based on measuring three intensity patterns: the Fourier transform of a filter object, a reference wave and the interference pattern produced by the sum of the object transform and the reference. The method can produce a filter that is well matched to both the object, its transforming optical system and the spatial light modulator used in the correlator input plane. A computer simulation was presented to demonstrate the approach for the special case of a conventional binary phase-only filter. The simulation produced a workable filter with a sharp correlation peak.

Knopp, Jerome↗

NASA NTRS2008

Vision System Measures Motions of Robot and External Objects

A prototype of an advanced robotic vision system both (1) measures its own motion with respect to a stationary background and (2) detects other moving objects and estimates their motions, all by use of visual cues. Like some prior robotic and other optoelectronic vision systems, this system is based partly on concepts of optical flow and visual odometry. Whereas prior optoelectronic visual-odometry systems have been limited to frame rates of no more than 1 Hz, a visual-odometry subsystem that is part of this system operates at a frame rate of 60 to 200 Hz, given optical-flow estimates. The overall system operates at an effective frame rate of 12 Hz. Moreover, unlike prior machine-vision systems for detecting motions of external objects, this system need not remain stationary: it can detect such motions while it is moving (even vibrating). The system includes a stereoscopic pair of cameras mounted on a moving robot. The outputs of the cameras are digitized, then processed to extract positions and velocities. The initial image-data-processing functions of this system are the same as those of some prior systems: Stereoscopy is used to compute three-dimensional (3D) positions for all pixels in the camera images. For each pixel of each image, optical flow between successive image frames is used to compute the two-dimensional (2D) apparent relative translational motion of the point transverse to the line of sight of the camera. The challenge in designing this system was to provide for utilization of the 3D information from stereoscopy in conjunction with the 2D information from optical flow to distinguish between motion of the camera pair and motions of external objects, compute the motion of the camera pair in all six degrees of translational and rotational freedom, and robustly estimate the motions of external objects, all in real time. To meet this challenge, the system is designed to perform the following image-data-processing functions: The visual-odometry subsystem (the subsystem that estimates the motion of the camera pair relative to the stationary background) utilizes the 3D information from stereoscopy and the 2D information from optical flow. It computes the relationship between the 3D and 2D motions and uses a least-mean-squares technique to estimate motion parameters. The least-mean-squares technique is suitable for real-time implementation when the number of external-moving-object pixels is smaller than the number of stationary-background pixels.

Talukder, Ashit↗

NASA NTRS1990

Zoom Vision System For Robotic Welding

Rugged zoom lens subsystem proposed for use in along-the-torch vision system of robotic welder. Enables system to adapt, via simple mechanical adjustments, to gas cups of different lengths, electrodes of different protrusions, and/or different distances between end of electrode and workpiece. Unnecessary to change optical components to accommodate changes in geometry. Easy to calibrate with respect to object in view. Provides variable focus and variable magnification.

Gilbert, Jeffrey L.↗

NASA NTRS1993

Stereoscopic Vision System For Robotic Vehicle

Distances estimated from images by cross-correlation. Two-camera stereoscopic vision system with onboard processing of image data developed for use in guiding robotic vehicle semiautonomously. Combination of semiautonomous guidance and teleoperation useful in remote and/or hazardous operations, including clean-up of toxic wastes, exploration of dangerous terrain on Earth and other planets, and delivery of materials in factories where unexpected hazards or obstacles can arise.

Matthies, Larry H.↗

NASA NTRS1991

A color-coded vision scheme for robotics

Most vision systems for robotic applications rely entirely on the extraction of information from gray-level images. Humans, however, regularly depend on color to discriminate between objects. Therefore, the inclusion of color in a robot vision system seems a natural extension of the existing gray-level capabilities. A method for robot object recognition using a color-coding classification scheme is discussed. The scheme is based on an algebraic system in which a two-dimensional color image is represented as a polynomial of two variables. The system is then used to find the color contour of objects. In a controlled environment, such as that of the in-orbit space station, a particular class of objects can thus be quickly recognized by its color.

Johnson, Kelley Tina↗

NASA NTRS1990

Hierarchical modelling of mobile, seeing robots

This paper describes the implementation of a hierarchical robot simulation which supports the design of robots with vision and mobility. A seeing robot applies a classification expert system for visual identification of laboratory objects. The visual data acquisition algorithm used by the robot vision system has been developed to exploit multiple viewing distances and perspectives. Several different simulations have been run testing the visual logic in a laboratory environment. Much work remains to integrate the vision system with the rest of the robot system.

Luh, Cheng-Jye↗

NASA NTRS1998

Vision Algorithms to Determine Shape and Distance for Manipulation of Unmodeled Objects

This paper discusses the development of a robotic system for general use in an unstructured environment. This is illustrated through pick and place of randomly positioned, un-modeled objects. There are many applications for this project, including rock collection for the Mars Surveyor Program. This system is demonstrated with a Puma560 robot, Barrett hand, Cognex vision system, and Cimetrix simulation and control, all running on a PC. The demonstration consists of two processes: vision system and robotics. The vision system determines the size and location of the unknown objects. The robotics part consists of moving the robot to the object, configuring the hand based on the information from the vision system, then performing the pick/place operation. This work enhances and is a part of the Low Cost Virtual Collaborative Environment which provides remote simulation and control of equipment.

Montes, Leticia↗

NASA NTRS1991

Optical correlator techniques applied to robotic vision

Vision processing is one of the most computationally intensive tasks required of an autonomous robot. The data flow from a single typical imaging sensor is roughly 60 Mbits/sec, which can easily overload current on-board processors. Optical correlator-based processing can be used to perform many of the functions required of a general robotic vision system, such as object recognition, tracking, and orientation determination, and can perform these functions fast enough to keep pace with the incoming sensor data. We describe a hybrid digital electronic/analog optical robotic vision processing system developed at Ames Research Center to test concepts and algorithms for autonomous construction, inspection, and maintenance of space-based habitats. We discuss the system architecture design and implementation, its performance characteristics, and our future plans. In particular, we compare the performance of the system to a more conventional all digital electronic system developed concurrently. The hybrid system consistently outperforms the digital electronic one in both speed and robustness.

Hine, Butler P., III↗

NASA NTRS1990

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

Chen, Alexander Y. K.↗

NASA NTRS1991

Structural stereopsis - Potential for automatic stereo camera calibration

The paper describes the use of extended edge features as a source of primitives for structural stereopsis and considers the design of a system for autonomous camera calibration. It is shown that the structural approach permits greater use of spatial relational constraints, eliminating the coarse-to-fine tracking of point-based algorithms. Experimental results concerning matching and calibration on real images using Laplacian-of-Gaussian contour fragments as primitives in structural stereopsis are presented, and results in graph-theoretic representation and inexact matches, analytical photogrammetry, and other computer vision and image analysis problem domains are examined. Such a system might be used in aerial photogrammetry and cartography, and robotic vision systems; however, the system is still very much under development.

Boyer, Kim L.↗

NASA NTRS1993

Robotic Tube-Gap Inspector

Robotic vision system measures small gaps between nearly parallel tubes. Robot-held video camera examines closely spaced tubes while computer determines gaps between tubes. Video monitor simultaneously displays data on gaps.

Gilbert, Jeffrey L.↗

NASA NTRS1991

Higher-order neural network software for distortion invariant object recognition

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Reid, Max B.↗