An new concept for visually guided manipulator control is introduced. It eliminates the need for a calibration of the manipulator as well as of the vision system and comprises an automatic adaptation to changing param...
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An new concept for visually guided manipulator control is introduced. It eliminates the need for a calibration of the manipulator as well as of the vision system and comprises an automatic adaptation to changing parameters. A key point of the concept is the achievement of a complex and elaborate desired goal by activating an appropriate sequence of rather simple elementary behaviors. Contrary to conventional stereo vision methods it uses an calibration-free camera system and allows a direct transition from image coordinates to motion control commands of a robot. By this approach, the abstract coordinate transformations have been avoided, instead, image data are used directly to control the behavior of the robot, or the interactions of the robot with physical objects. Thus, it makes knowledge of many hard-to-measure optical and mechanical system unnecessary;moreover, it lends itself to the realization of learning and adaptive robots. The concept has been successfully realized and tested in real-word experiments with an visually guided calibration-free 5 degree of freedom (DOF) manipulator involving the grasping of various objects with nearly any shape in arbitrary position (standing, lying, sloping) in the robot' 3-D work space.
This work demonstrates a vision-based control technique that does not require robot or vision system calibration. There are two distinct advantages: first, the approach is generic and can be applied to a variety of sy...
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This work demonstrates a vision-based control technique that does not require robot or vision system calibration. There are two distinct advantages: first, the approach is generic and can be applied to a variety of systems;second, calibration is unnecessary after a reconfiguration or disturbance to the robotic workcell. It has the potential to provide a low-cost, low-maintenance automation solution for unstructured industries and environments. The robot end-effector tracks a moving target using a novel dynamic quasi-Newton control law formulated in the image plane and on-line Jacobian estimation using either a dynamic Broyden's method or a dynamic recursive least squares algorithm. Experimental results demonstrate convergent and stable control of an uncalibrated manipulator tracking a moving target. The method is shown to be robust to system reconfiguration such as modifications to the position and orientation of the camera.
Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items...
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Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering (multiple clusters per class) and how it is vital to achieve useful classification. New clustering methods using class identity and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.
This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-...
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This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-form surface and encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image `Surface Point Signature' (SPS). This scheme can be used as a global representation of the surface as well as a local one and also in a scale independent surface matching. It performs faster registration than existing registration approaches.
Pose characterization refers to the unique representation of an object's pose through a set of image features. In this regard, one of the prerequisites for using visual images in representing an object's pose ...
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Pose characterization refers to the unique representation of an object's pose through a set of image features. In this regard, one of the prerequisites for using visual images in representing an object's pose is a set of view-dependant image features that is unique to a single position and orientation (pose) of the object relative to the camera. Here, we propose an enhancement to the global feature extraction method. It utilizes an active illumination that projects a set of orthogonal grid patterns on the object. The deformations of the grid pattern incident on the complex target object carries important information about the surface structure of the target. This grid-encoded image can be characterized using global descriptors. Results indicate that this illumination increases the sensitivity of the global features to changes in object pose.
This paper addresses the problem of architecture optimization, when implementing an image matching primitive in reconfigurable circuits (Xilinx FPGA, for example). Circuit spatial organization is optimized in terms of...
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This paper addresses the problem of architecture optimization, when implementing an image matching primitive in reconfigurable circuits (Xilinx FPGA, for example). Circuit spatial organization is optimized in terms of processing time, and circuit volume, in order to suit well for real time on board applications. This optimized adaptive spatial and scalable organization of the μPD circuit dedicated to image matching reduces by one order the spatial and temporal performance (compared to a sequential implementation), without altering the quality of matching. The μPD circuit has been validated with the minimal 22 elementary cells architecture with Xilinx 4010 XL circuit working at 12 MHz and occupying 92% of the circuit CLB. It performs the pyramidal 256×256 image matching in less than 1 s.
The purpose of this paper is to describe a high-level path planning logic, which processes the data from a vision system and an ultrasonic obstacle avoidance system and steers an autonomous mobile robot between obstac...
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The purpose of this paper is to describe a high-level path planning logic, which processes the data from a vision system and an ultrasonic obstacle avoidance system and steers an autonomous mobile robot between obstacles. The test bed was an autonomous robot built at University of Cincinnati, and this logic was tested and debugged on this machine. Attempts have already been made to incorporate fuzzy systems on a similar robot, and this paper extends them to take advantage of the robot's ZTR capability. Using the integrated vision system, the vehicle senses its location and orientation. A rotating ultrasonic sensor is used to map the location and size of possible obstacles. With these inputs the fuzzy logic controls the speed and the steering decisions of the robot. With the incorporation of this logic, it has been observed that Bearcat II has been very successful in avoiding obstacles very well. This was achieved in the Ground Robotics Competition conducted by the AUVS in June 1999, where it travelled a distance of 154 feet in a 10 ft. wide path ridden with obstacles. This logic proved to be a significant contributing factor in this feat of Bearcat II.
We are interested in the development of an efficient computational visual system to study dynamic fluids and in this paper we present a methodology to detect patterns in flow-like images. These patterns are called in ...
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We are interested in the development of an efficient computational visual system to study dynamic fluids and in this paper we present a methodology to detect patterns in flow-like images. These patterns are called in the Dynamical Systems theory critical points or single points. This method, like other previous methodologies presented in related literature, involves four steps: estimation of flow directions;detection of critical point candidates;matching of critical points;and critical points description according to the Dynamical Systems theory. To achieve the critical point class, node, saddle or center point, we use templates to match the patterns. The last step of the method is necessary only if we wish a numerical representation of the patterns. We ran the method over several real images of some fluid flow classes and present the results.
Problem of pattern recognition can be interpreted as a problem of acceptance of optimal decision under conditions of uncertainty, caused by absence of the complete and authentic information about a recognized object a...
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Problem of pattern recognition can be interpreted as a problem of acceptance of optimal decision under conditions of uncertainty, caused by absence of the complete and authentic information about a recognized object and its features. The unique adequate (from the point of view of the system analysis) method of solving of pattern recognition problem in the conditions of uncertainty is the decision making by the whole set of available heterogeneous information, taking into account a significance and reliability of each of considered features and their interrelation. Usually the solution of pattern recognition problem is reduced to the task of minimization of distance from an image of the object up to the standard image of the class of objects. In this paper we offer and review the possible approach to generalization of the Mahalonobis metrics, based on properties of fuzzy number in L-R form. The results of the experimental comparison of the effectiveness of pattern recognition using the considered set of fuzzy features and criteria are discussed.
This paper presents a method to reduce the calculation costs of object pose measurements. They are estimated by matching L-shaped line-segments in 3-D object models with those in 2-D object images using a P3P solution...
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This paper presents a method to reduce the calculation costs of object pose measurements. They are estimated by matching L-shaped line-segments in 3-D object models with those in 2-D object images using a P3P solution. The L segments have the minimum pieces of information for the estimation, and, therefore, it produces a large amount of L segments both in the 3-D object models and in the 2-D object images, resulting in enormous correspondences, and P3P calculations. To solve the problem, we propose a strategy enabling to select fewer L segments: as an evaluation function, we utilizes an average of the estimation errors when observing the L segments from representative points on a geodesic dome. Furthermore, we deduce a useful approximate formula for L segments having various shapes.
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