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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemA generalized approach to real-time pattern recognition in sensed data(Georgia Institute of Technology, 1999-12) Howard, Ayanna M. ; Padgett, CurtisMany applications that focus on target detection in an image scene develop algorithms specific to the task at hand. These algorithms tend to be dependent on the type of input data used in the application and thus generally fail when transplanted to other detection spaces. We wish to address this data dependency issue and develop a novel technique which autonomously detects, in real time, all target objects embedded in an image scene irrespective of the imagery representation. We accomplish this task using a heirarchical approach in which we use an optimal set of linear filters to reduce the data dimensionality of an image scene and then spatially locate objects in the scene with a neural network classifier. We prove the generality of this approach by applying it to two distinctly separate applications. In the first application, we use our algorithm to detect a specified set of targets for an Automatic Target Recognition (ATR) task. The data for this application is retrieved from two-dimensional camera imagery. In the second task, we address the problem of sub-pixel target detection in a hyperspectral image scene. This data set is represented by hyperspectral pixel bands in which target objects occupy a portion of a hyperspectral pixel. A summarized description of our algorithm is given in the following section.
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ItemIntelligent learning for deformable object manipulation(Georgia Institute of Technology, 1999-11) Howard, Ayanna M. ; Bekey, George A.The majority of manipulation systems are designed with the assumption that the objects’being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand.‘ Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. Our methodology relies on the implementation of two main tasks. Our first task is to calculate deformation characteristics for a non-rigid object represented by a physically-based model. Using nonlinear partial differential equations, we model the particle motion of the deformable object in order to calculate the deformation characteristics. For our second task, we must calculate the minimum force required to successfully lift the deformable object. This minimum lifting force can be learned using a technique called ‘iterative lifting’. Once the deformation characteristics and the associated lifting force term are determined, they are used to train a neural network for extracting the minimum force required for subsequent deformable object manipulation tasks. Our developed algorithm is validated with two sets of experiments. The first experimental results are derived from the implementation of the algorithm in a simulated environment. The second set involves a physical implementation of the technique whose outcome is compared with the simulation results to test the real world validity of the developed methodology.
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ItemCombining a multirate repetitive learning controller with command shaping for improved flexible manipulator control(Georgia Institute of Technology, 1999-11) Rhim, Sungsoo ; Hu, Ai-Ping ; Sadegh, Nader ; Book, Wayne J.Command shaping, a feedforward approach used to control flexible manipulators, performs most effectively when applied to a linear system. In practice, various nonlinearities are present in a given system that will deteriorate the performance of command shaping. In this work, a multirate repetitive learning controller (MRLC) is used in conjunction with a command shaping method known as the optimal arbitrary time-delay filter (OATF) for discrete-time joint control of a single flexible link manipulator containing nonlinearities. With very little a priori knowledge of the given system, a MRLC is able to cancel the nonlinearities at select frequencies and achieve near-perfect tracking of a periodic desired trajectory. By doing this, a MRLC controls the joint to follow a given shaped command more closely, thus allowing the OATF to more effectively attenuate residual tip vibrations. It is shown both analytically and experimentally that this controller is more effective than a conventional PID and OATF controller at attenuating residual tip vibrations.
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ItemInverse dynamics for commanding micromanipulator inertial forces to damp macromanipulator vibration(Georgia Institute of Technology, 1999-10) Loper, Jeffery Cameron ; Book, Wayne J.A multi degree of freedom manipulator can be commanded to generate base forces by utilization of the dynamic equations relating joint torques or joint accelerations to base forces. This can be extended to a full order case with three base moments and three base forces. In the experiments described two forces were sufficient to damp the fundamental modes. With a force command capability a variety of damping algorithms can be used to determine the desired force. A simple acceleration feedback algorithm was used which approximates a two degree of freedom dynamic vibration absorber where the damping coefficient can be adjusted by the acceleration feedback gain. The resulting damping ratio of the fundamental mode was increased by factors of between 18 and 191.
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ItemTime-Delay Command Shaping Filters: Robust and/or Adaptive(Georgia Institute of Technology, 1999-09) Book, Wayne J. ; Magee, David P. ; Rhim, SungsooTime-delay command shaping filters for reducing the vibrational response of flexible systems are introduced and discussed. Special attention is given to the role played by robustness and adaptation in producing effective filters even when system parameters change. Results from several authors are used to compare and contrast these approaches.
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ItemModeling of the Natural Product Deboning Process Using Biological and Human Models(Georgia Institute of Technology, 1999-09) Daley, Wayne ; He, Tian ; Lee, Kok-Meng ; Sandlin, MelissaOne critical area in automation for commercial deboning systems for meat processing, is the inability of existing equipment to adapt to varying sizes and shapes of products. This usually results in less than desirable outcomes when measured in terms of yield of the operations. In poultry processing for example, the initial cut of wing-shoulder joints is the most critical step in the deboning process. Two approaches for determining a trajectory for the cut is presented. The first is a technique using x-ray and visual images to obtain a 2-D model that locates the shoulder joint with respect to the surface features of the product. The second approach is obtained by determining a 3-D cutting trajectory and the associated forces/torques using a motion analysis system and a force/torque sensor incorporated with a knife. We then discuss the potential application of these results in the design of an automated cutting system that uses the obtained trajectory as a nominal cutting path. The system would make'adjustments during the cut using force feedback so as to emulate the manual cutting process.
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ItemAdaptive Command Shaping Using Adaptive Filter Approach in Time Domain(Georgia Institute of Technology, 1999-06) Rhim, Sungsoo ; Book, Wayne J.Since its introduction, the command shaping method has been applied to the control of many different types of flexible manipulators. A properly designed command shaper cancels the resonance poles of the system regardless of the given reference input to the system. However, designing an effective command shaper requires a priori knowledge about the system parameters. Recently, some efforts have been made to make the command shaper less sensitive to the uncertainty of the system parameters and to make the command shaper adapt to the unknown system parameters. This research is an effort to develop an effective adaptive command-shaping algorithm in the time domain. In the paper, the authors propose an adaptive command-shaping algorithm using an adaptive filtering technique in the time domain and verify the effectiveness of the proposed algorithm with proper experiments.
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ItemControl of flexible manipulators using vision and modal feedback(Georgia Institute of Technology, 1999-05) Obergfell, Klaus ; Book, Wayne J.Literature for end point measurement and control is reviewed. An integrated vision sensor for tip position and an optical deflection sensor are incorporated into the control of a hydraulically actuated, flexible two-link manipulator arm. Analysis and experiments provide a design procedure and performance evaluation. The design procedure is based on successive loop closure and the use of output feedback modified to maintain stability. Point to point positioning performance is improved over alternative controllers.
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ItemIntelligent Target Detection in Hyperspectral Imagery(Georgia Institute of Technology, 1999-03) Howard, Ayanna M. ; Padgett, Curtis ; Brown, Kenneth R.Many applications that use hyperspectral imagery focus on detection and recognition of targets that occupy a portion of a hyperspectral pixel. We address the problem of sub-pixel target detection by evaluating individual pixels belonging to a hyperspectral image scene. We begin by clustering each pixel into one of n classes based on the minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Associated with each cluster is a set of linear filters specifically designed to separate signatures derived from a target embedded in a background pixel from other typical signatures belonging to that cluster. The filters are found using directed principal component analysis which maximally separates the two groups. Each pixel is projected on this set of filters and the result is fed into a trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for generating training and testing data, describe our modified clustering algorithm, explain how the linear filters are designed, and provide details on the neural network classifier. Evaluation of the overall algorithm demonstrates that for pixels with embedded targets taking up no more than 10% of the area, our detection rates approach 99.9% with a false positive rate of less than 10 ⁻⁴.
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ItemBehavior-based Formation Control for Multi-robot Teams(Georgia Institute of Technology, 1999) Arkin, Ronald C. ; Balch, TuckerNew reactive behaviors that implement formations in multi-robot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based Unmanned Ground Vehicles. The technique has been integrated with the Autonomous Robot Architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments.