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Systems, Man and Cybernetics, IEEE Transactions on

Issue 8 • Date Aug 1994

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Displaying Results 1 - 17 of 17
  • A new identification Jacobian for robotic hand/eye calibration

    Page(s): 1284 - 1287
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (316 KB)  

    Hand/eye calibration is the process of identifying the unknown position and orientation of the camera frame with respect to the robot hand frame, when the camera is rigidly mounted on the robot hand. While computationally slightly more involved, one-stage iterative algorithms have two distinguished advantages over traditional two-stage linear approaches: 1) they are less sensitive to noise, and 2) they can handle cases in which the camera orientation information is not available. A more compact and lower dimensional identification Jacobian is derived. The Jacobian, which relates measurement residuals to pose error parameters of the unknown hand/eye transformation, is a crucial component of one-stage iterative algorithms. The derivation procedure for the new Jacobian is straightforward and simple, owing to an alternative mathematical formulation of the hand/eye calibration problem. Observability conditions of the pose error parameters in the unknown hand/eye transformation are also provided based on this identification Jacobian View full abstract»

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  • Constructive stochastic temporal reasoning in situation assessment

    Page(s): 1099 - 1113
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    Due to poor structure of situation assessment tasks (SA-tasks), the knowledge-based approach has been studied intensively by many authors. But little progress up to now has been achieved because the existing techniques usually neglect the constructive nature of a human expert's reasoning in a process of a SA-task solving. The development of a new method enabling one to model constructive reasoning is rather complicated, the scope of this work has been restricted to the temporal aspect only. In the paper, a mathematical framework, which may be used in a rule-based expert system, reasoning about events in a constructive manner, is presented. Time instances and intervals are treated in detail, using a stochastic approach to represent imprecision. The human expert's temporal knowledge is considered imprecise in the stochastical sense, as well. Both forward and backward modes of reasoning are studied. Sufficient conditions for consistency of the knowledge model are also derived. The method has been implemented by the author in a feasibility demonstration prototype expert system View full abstract»

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  • A unified approach to camera fixation and vision-based road following

    Page(s): 1125 - 1141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1800 KB)  

    Both camera fixation and vision-based road following are problems that involve tracking or fixating on 3-D points and features. This paper presents a unified theoretical approach to analyzing camera fixation and vision-based road following. The approach is based on the concept of equal flow circles (EFCs) and zero flow circles (ZFCs). Using EFCs it is possible to locate points in space relative to the fixation point, and predict the behavior. The camera's instantaneous direction of translation and the fixation point determine the plane on which the EFCs can be found. We show that points on an EFC inside the ZFC produce optical flow that is opposite in sign to that produced by points outside the ZFC. When a point in space crosses a ZFC it produces zero flow. For explanation purposes we analyzed a special case of motion. However, a similar approach can be taken for a more general motion of the camera. The analysis for the current motion can also be extended to find equal flow curves View full abstract»

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  • Systems engineering of agricultural robot design

    Page(s): 1259 - 1265
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    This paper presents a systems engineering method to evaluate the performance of an agricultural robot by simulating and comparing different types of robots, number of arms, multiple arm configurations, workspace design and dynamic characteristics. Numerical simulation tools were developed to quantify measures of machine performance such as cycle time and percentage of successful cycles based on an extensive statistical analysis using measured fruit locations and simulated crop parameters. The methodology developed was applied to determine design parameters for a robotic melon harvester. Simulation results indicated that the Cartesian robot was faster than the cylindrical robot for the melon harvesting task. Activating two arms in tandem was the fastest configuration evaluated View full abstract»

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  • Optimal state space partitioning

    Page(s): 1174 - 1190
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    The partitioning problem of a parameter state space Ω into observation subsets is addressed. The initial knowledge about this parameter is a prior probability distribution over Ω. This distribution is recursively updated through parallel observation results, that are actually binary informations about the presence or the absence of the parameter inside subsets ωi of Ω. Each subset is scanned with some errors, corresponding to misdetections and false alarms. It is shown how the partitioning of Ω into the {ωi} may be optimized under different optimality criteria related to various measures of the “information” contained in the posterior probability density function. Simulations results are presented and computability issues are discussed View full abstract»

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  • Rule generation from neural networks

    Page(s): 1114 - 1124
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    The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay dawn required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions View full abstract»

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  • Integration of overlapping maps made by stereo vision using view field information

    Page(s): 1273 - 1279
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    We propose a new method for combining two overlapping maps made by the stereo vision technique utilizing view field information. Degree of matching is evaluated for every matching candidate, where object point pairs and solitary points in the overlapping view fields contribute positive and negative values, respectively. The threshold value used in judging pair is determined by considering the error magnitude, which depends on the distance from the view point. Various kinds of calculation are compared in terms of the freedom of threshold, which is defined as the ratio of the maximum and minimum threshold values, and with which the correct overlap is derived View full abstract»

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  • Approximate clustering via the mountain method

    Page(s): 1279 - 1284
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    We develop a simple and effective approach for approximate estimation of the cluster centers on the basis of the concept of a mountain function. We call the procedure the mountain method. It can be useful for obtaining the initial values of the clusters that are required by more complex cluster algorithms. It also can be used as a stand alone simple approximate clustering technique. The method is based upon a griding on the space, the construction of a mountain function from the data and then a destruction of the mountains to obtain the cluster centers View full abstract»

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  • A reservation-based CSMA protocol for integrated manufacturing networks

    Page(s): 1247 - 1258
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    This paper presents PCSMA, an extension to the CSMA/CD protocol (used in the Ethernet) to support both conventional datagram traffic and real-time traffic. The protocol provides predictable packet-delivery bounds for real-time traffic. It does not require changes to existing Ethernet controllers for datagram traffic. Under the new protocol, periodic sources follow a variation of the CSMA protocol that requires them to reserve transmission slots before they can begin transmission. This protocol is intended particularly for use in the integrated network in a manufacturing shop. Periodic sources include multiple sensors generating disparate types and rates of data. We have carried out an extensive performance evaluation of the protocol using a simulation model. The results are impressive: PCSMA shows fewer collisions than CSMA, equivalent delays for conventional traffic, and no failures to meet deadlines for periodic traffic View full abstract»

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  • Semi-autonomous evolution of object models for adaptive object recognition

    Page(s): 1191 - 1207
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1612 KB)  

    The paper presents a semi-autonomous model evolution approach to object recognition under variable perceptual conditions. The approach assumes that (i) the system has to recognize objects on separate images of a sequence, and (ii) the images demonstrate the variability of conditions under which objects are perceived (gradual change in resolution, lighting, positioning). The adaptation of object models is executed due to perceived, over a sequence of images, variabilities of object characteristics. This adaptation involves (i) the application of learned models to the next image, (ii) the monitoring of recognition effectiveness of the models, and (iii) an activation of learning processes if needed (i.e., when the recognition effectiveness of the models decreases). Model adaptation (evolution) integrates recognition processes of computer vision with incremental knowledge acquisition processes of machine learning in a closed loop. The paper presents both an outline of the iterative evolution methodology and the investigation of an incremental model generalization approach using the example of a texture recognition problem. Experiments were run in a semi-autonomous mode where a teacher secured soundness behavior of the evolution system. The experiments are compared for three system configurations: (i) a one-level control structure, (ii) a two-level control structure, and (iii) a two-level control structure with data filtering. The obtained results are evaluated for system recognition effectiveness, recognition stability, and predictability of evolved models View full abstract»

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  • Acceleration based learning control of robotic manipulators using a multi-layered neural network

    Page(s): 1265 - 1272
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB)  

    This paper presents a nonlinear compensation method based on neural networks for trajectory control of robotic manipulators. A multi-layered perceptron neural network (MLP) is used to predict the required actuator torques of a robot to follow a desired trajectory, and these predicted torques are applied to the robot as feedforward compensations in parallel to a linear feedback controller. An acceleration based learning scheme is proposed to adjust the connection weights in the neural network to form an approximated dynamic model of the robot. Simulation results show that the proposed learning scheme improves the speed of error convergence of the system and reduces the convergent error with the efficient adaptation to the changing system dynamics. The validity of the proposed learning scheme is verified through experiments View full abstract»

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  • ANN implementation of stereo vision using a multi-layer feedback architecture

    Page(s): 1220 - 1238
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    An artificial neural network (ANN), consisting of three interacting neural modules, is developed for stereo vision. The first module locates sharp intensity changes in each of the images. The edge detection process is basically a bottom-up, one-to-one input-output mapping process with a network structure which is time-invariant. In the second module, a multilayered connectionist network is used to extract the features or primitives For disparity analysis (matching). A similarity measure is defined and computed for each pair of primitive matches and is passed to the third module. The third module solves the difficult correspondence problem by mapping it into a constraint satisfaction problem. Intra- and inter-scanline constraints are used in order to restrict possible feature matches. The inter-scanline constraints are implemented via interconnections of a three-dimensional neural network. The overall process is iterative. At the end of each network iteration, the output of the third constraint satisfaction module feeds back updated information on matching pairs as well as their corresponding location in the left and right images to the input of the second module. This iterative process continues until the output of the third module converges to a stable state. Once the matching process is completed, the disparity can be calculated, and camera calibration parameters can be used to find the three-dimensional location of object points. Results using this computational architecture are shown View full abstract»

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  • Identification of fuzzy prediction models through hyperellipsoidal clustering

    Page(s): 1153 - 1173
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    To build a fuzzy model, as proposed by Takagi and Sugeno (1985), the authors emphasize an interactive approach in which knowledge or intuition can play an important role. It is impossible in principle, due to the nature of the data, to specify a criterion and procedure to obtain an ideal fuzzy model. The main subject of fuzzy modeling is how to analyze data in order to summarize it to a certain extent so that one can judge the quality of a model by intuition. The main proposal in this paper is a clustering technique which takes into account both continuity and linearity of the data distribution. The authors call this technique the hyperellipsoidal clustering method, which assists modelers in finding fuzzy subsets suitable for building a fuzzy model. The authors deal with other problems in fuzzy modeling as well, such as the effect of data standardization, the selection of conditional and explanatory variables, the shape of a membership function and its tuning problem, the manner of evaluating weights of rules, and the simulation technique for verifying a fuzzy model View full abstract»

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  • Experiments with a target-threshold control theory model for deriving Fitts' law parameters for human-machine systems

    Page(s): 1089 - 1098
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    A target-threshold control theory model was developed for predicting human-machine movement time. This allows the previously empirical parameters of Fitts' speed and accuracy law (1954) to be determined before system construction. Fitts' law may now serve a role as a predictive design engineering tool for new systems as well as in its traditional role of after-the-fact analysis. The target-threshold model successfully characterized human control movement times, before system construction, in experiments involving camera pointing for a new class of point-and-direct telerobotics. Employing the natural logarithm as the basis for indexing task difficulty, the slope and intercept parameters were found by analysis of calculated step response trajectories at progressively tighter normalized target thresholds corresponding to increasing distances between targets. Tests validated the model for human subjects using the experimental apparatus of the Stanford Point-And-Direct (PAD) Telerobot for which tasks are developed using phrases such as “put that there”. The target-threshold model is a tool for human factors engineers and control system designers that allows fundamental control theory techniques to be used to predict speed of response for new human-machine systems. The technique allows system developers to compare performance of prospective human-machine systems View full abstract»

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  • Simple model of human arm reachable workspace

    Page(s): 1239 - 1246
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (728 KB)  

    The paper introduces a simplified mathematical model of the human arm kinematics which is used to determine the workspace related to the reachability of the wrist. The model contains six revolute degrees of freedom, five in the shoulder complex and one in the elbow joint. It is not directly associated to the anatomical structure of the arm, but represents the spatial motion of two characteristic points, epicondylus lateralis and proc. styloideus. Use of this simplified model for the determination of reachable workspace offers several advantages versus direct measurement: (i) the workspace can be obtained in few minutes on a micro VAX II computer, (ii) patients with various injuries in various stages of recovery can be treated since only a few brief and simple measurements of the model's parameters are needed, and (iii) the calculated workspace includes complete information of the envelope, as well as inside characteristics View full abstract»

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  • Multi-agent resource allocation: an incomplete information perspective

    Page(s): 1208 - 1219
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    Explores a multi-agent resource allocation problem in an environment of incomplete information. The paper posits the existence of a central policy maker who integrates information acquisition with decision making, in order to optimally allocate resources among multiple agents. The study focuses on three salient aspects: first, the policy maker uses a nonparametric revealed preference approach for the elicitation of each agent's preference profiles. This allows the policy maker to avoid distorting the allocation decision and emerging with suboptimal allocations. Second, the process of information gathering and decision making is a challenging task in view of the extremely large numbers of possible profiles for each agent, even for problems of modest size. To find an optimal allocation, the policy maker can use schemes that utilize partial preference information to handle the complexity of the problem. Third, the incorporation of costs into the information process and the minimal assumptions made about the problem environment allow the policy maker to realistically analyze the resource allocation problem View full abstract»

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  • Neuro-controllers for adaptive helicopter hover training

    Page(s): 1142 - 1152
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    This paper presents an application of artificial neural networks in adaptive helicopter hover training of novice student pilots. The design of the adaptive trainer utilizes the hypothesis that novices can be trained to fly a helicopter system automatically (with no human interaction) if the helicopter system adapts to the learning curve of the student. Two different techniques based on the above approach are presented. In the first technique, the helicopter system actively enforces optimality by augmenting the novice's control inputs by amounts necessary to satisfy desired performance criteria. The second technique uses relaxed performance criteria that are not initially optimal, but approach optimality in a graded fashion, based on the learning curve of the student. Adaptive neuro-controllers, together with a critic model, are used to implement the adaptive helicopter system. The results using simulated student models verify the approach adopted, and show that the adaptive neuro-controllers allow the helicopter system to adapt to the novice's learning curve View full abstract»

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