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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Fei-Yue Wang P.J.A. Lever Robotics and Automation Laboratory Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Advanced Mining Systems Laboratory Department of Mining and Geological Engineering University of Arizona Tucson Tucson AZ USA

This paper describes the authors' initial investigation into on-line trajectory planning for autonomous robotic excavation. A dynamic model for arm/environment interaction is proposed. Excavation trajectories are generated using a trapezoidal velocity profile under the maximum force/torque constraints. A scheme for real-time identification of dynamic arm/environment interaction parameters is implemented with a first-order algorithm of Saridis-Stein stochastic approximation. The result of a simulation study is presented to illustrate the proposed trajectory planning approach.< >

关键词： Force measurement Trajectory Robot sensing systems Force sensors Torque measurement robotics and automation Humans Automatic control Fuzzy logic Control systems

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A fuzzy control system for an automated mining excavator

A fuzzy control system for an automated mining excavator

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IEEE International Conference on robotics and Automation (ICRA)

作者： P.J.A. Lever Fei-Yue Wang Deqian Chen Advanced Mining Systems Lab Mining and Geological Engineering Department University of Arizona Tucson Tucson AZ USA Robotics and Automation Lab Systems and Industrial Engineering Department University of Arizona Tucson Tucson AZ USA

To automate a mining excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. Thus, generating rigorous mathematical models as required by conventional control techniques is extremely difficult if possible. This paper presents an excavator controller that uses fuzzy logic to interpret forces and torques gathered from a wrist mounted force/torque sensor during the excavation process. This approach enables the transfer of human heuristics and expert knowledge to the controller. Experimental results from several excavation tests using the fuzzy logic controller are given. These results represent the first step toward an integrated approach that uses force/torque, vision, and other sensor data to perform generic tasks in uncertain mining environments.< >

关键词： Fuzzy control Fuzzy logic Force sensors Automatic control Uncertainty Mathematical model Automatic generation control Force control Torque control Wrist

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A fuzzy-behavior-based approach for controlling mining excavator bucket/rock interactions

A fuzzy-behavior-based approach for controlling mining excav...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： P.J.A. Lever Fei-Yue Wang Xiaobo Shi Deqian Chen Advanced Mining Systems Laboratory Mining and Geological Engineering Department University of Arizona Tucson Tucson AZ USA Robotics and Automation Laboratory Systems and Industrial Engineering Department University of Arizona Tucson Tucson AZ USA

Successful control of an autonomous mining excavator requires sensor feedback from the excavation tool to control unpredictable tool/rock interactions. Conventional control approaches cannot deal effectively with uncertainties in the dynamic and unstructured mining environment. This paper presents a new intelligent control approach that utilizes fuzzy behaviors with fuzzy strategies to control bucket motions for autonomous excavation tasks. In this approach, the control system uses feedback from a bucket mounted force/torque sensor to control excavation motions by sensing and reacting to interactions with rock particles during excavation. A behaviour coordination configuration is developed to improve the controllers effectiveness in reaching its overall excavation goals. It uses fuzzy logic rules to adjust control system output variables and performs goal-driven behaviour selection. This technique is a first step towards realistic control of complex machine/environment interactions in difficult and unpredictable environments, where traditional control approaches are either impossible or computationally excessive. The discussion here focuses on: (a) development of a new control architecture; (b) the individual components of the architecture; and (c) experiments with a simulated excavator using a PUMA 560 robotic arm.< >

关键词： Motion control Control systems Intelligent sensors Fuzzy control Force control Torque control Force feedback Computer architecture Uncertainty Intelligent control

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Spatial characteristics of human hand impedance in multi-joint arm movements

Spatial characteristics of human hand impedance in multi-joi...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： T. Tsuji K. Goto M. Moritani M. Kaneko P. Morasso Computer Science and Systems Engineering Hiroshima University Hiroshima Japan Hiroshima Daigaku Higashihiroshima Hiroshima JP Dept. of Comput. Sci. & Syst. Eng. Hiroshima Univ. Japan Laboratory for Integrated Advanced Robotics DIST-University of Genoa Genoa Italy

The present paper examines spatial characteristics of human hand impedance in multi-joint arm movements. While a subject maintains a hand location, small external disturbance to his hand is applied by a manipulandum. Time changes of the hand displacements and forces caused by the disturbance are measured and the hand impedance is estimated using a second-order linear model. The estimated hand impedance in different subjects and hand locations are summarized as follows: (1) spatial variations of the estimated hand impedance ellipses approximately agree with the experimental results of other researchers (Mussa-Ivaldi et at, 1985: Dolan et al., 1993), (2) the human hand inertia characteristics can be explained from basic biomechanics of the passive inertial effects (3) the grip force of the subject increases the size of the stiffness and viscosity ellipses, and (4) spatial features of the orientation. And the shape characteristics of the stiffness and viscosity ellipses are mostly explained from the kinematics point of view of the human arm.< >

关键词： Humans Impedance Viscosity Robots Kinematics Muscles Shape Control systems Man machine systems Systems engineering and theory

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AN IMPROVED DIGIT-REVERSAL PERMUTATION ALGORITHM

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SIGNAL PROCESSING 1993年第3期32卷 409-415页

作者： YU, XG LOH, NK MILLER, WC Department of Electrical Engineering University of Windsor Windsor Ontario Canada N9B 3P4 Center for Robotics and Advanced Automation Dodge Hall of Engineering Oakland University Rochester MI48309-4401 USA

This paper introduces a new structure to compute the digit-reversal permutation in the radix-B fast Fourier or Hartley transform. By studying the property of base-B numbers, we find that any base-B number, k, can be represented as the sum of three numbers: one base-B number, i;digital reverse of another base-B number, j;and a constant, z, in which the two base-B numbers i and j are no greater than square-root N, where N is the number of data points to be transformed. Then, the digital reverse of k is just the sum of j and the digital reverse of i plus the constant z. Using this property we have developed a new algorithm to compute the digital reverse of any index very efficiently. Since it uses fewer arithmetic operations, the new algorithm is faster than other algorithms known to the authors and the program storage requirements are no greater than Evans' and Walker's algorithms.

关键词： FAST FOURIER TRANSFORM FAST HARTLEY TRANSFORM DIGIT-REVERSAL PERMUTATION BIT-REVERSE ALGORITHM

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Modified backpropagation algorithms for training the multilayer feedforward neural networks with hard-limiting neurons

Modified backpropagation algorithms for training the multila...

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1993 Canadian Conference on Electrical and Computer engineering, CCECE 1993

作者： Yu, Xiangui Loh, N.K. Jullien, G.A. Miller, W.C. Dept. of Electrical Engineering University of Windsor WindsorONN9B 3P4 Canada Center for Robotics and Advanced Automation Dodge Hall of Engineering Oakland University RochesterMI48309-4401 United States

ISBN: (纸本)0780314433

This paper introduces modified backpropagation algorithms for training multilayer feedforward neural networks with hard-limiting neurons. Transforming neuron activation functions are used in all the hidden layers, which are the modified continuous sigmoidal functions with an adaptive steepness factors. In the training process, this steepness factor varies from a small positive value to infinite with the decrease of the sum square error. Thus, a multilayer feedforward neural network can be trained with the resultant architecture only composed of hard limiting neurons. The learning algorithm is similar to the conventional backpropagation algorithm. Only the derivatives of the hidden neural activation functions are modified. Extensive numerical simulations are presented to show the feasibility of the proposed algorithm. In addition, the numerical properties of the proposed algorithm are discussed and comparisons of the proposed algorithm with other algorithms are made. © 1993 IEEE.

关键词： Neurons

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Comparisons of four learning algorithms for training the multilayer feedforward neural networks with hard-limiting neurons

Comparisons of four learning algorithms for training the mul...

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1993 Canadian Conference on Electrical and Computer engineering, CCECE 1993

ISBN: (纸本)0780314433

In this paper, two kinds of learning algorithms that have been developed for training multilayer feedforward neural networks with hard-limiting neurons are reviewed. For the modified backpropagation algorithms, their numerical performances of convergence speed and training efficiency are compared;for the architecture generating methods, the architecture sizes of the neural networks generated are compared and their generalization ability are discussed. For any given application problem, some criteria for selecting a suitable training algorithm are also discussed. © 1993 IEEE.

关键词： Learning algorithms

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The design and training of an intelligent sensor implementation

The design and training of an intelligent sensor implementat...

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1993 Canadian Conference on Electrical and Computer engineering, CCECE 1993

ISBN: (纸本)0780314433

The design and VLSI implementation of an intelligent sensor that can be used for process control applications requiring image capture or non-contact measurement is presented in this paper. The sensor architecture is based on an analog VLSI realization of an artificial neural network with an integrated photosensitive array. A 10×10 array of photosensitive cells has been designed as the input nodes to a two-layer feedforward neural network. Four groups each with 25 photosensors are fully connected to 4 neurons respectively in the hidden layer. The 16 hidden neurons are fully connected to 5 neurons in the output layer. A development program has been written that determines the optimal set of weights using the modified backpropagation algorithm. The neural network has been trained to recognize 18 different types of patterns. The final implementation of this intelligent sensor has the dimensions of 4500×4820 design scale microns and contains approximately 1630 analog devices fabricated using a 3μ single-polysilicon, double-metal, p-well CMOS process. © 1993 IEEE.

关键词： Neurons

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A fast approach for the path planning of telerobotic manipulators

A fast approach for the path planning of telerobotic manipul...

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IEEE International Conference on robotics and Automation (ICRA)

作者： R.V. Mayorga K.S. Ma A.K.C. Wong B. Ressa Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada Advanced Robotics Department Tecnopolis-CSATA Italy

A fast procedure for the robust path planning of telerobotic manipulators is presented. The approach is based on solving a linear system of equations taking into account a null space vector for obstacle avoidance, and an original scheme for the appropriate perturbation of the pseudoinverse matrix. This method allows the simultaneous pursuit of both obstacle avoidance and singularities prevention on-line in a sensor based environment.< >

关键词： Path planning Telerobotics Manipulators Robustness Master-slave Kinematics Null space Vectors End effectors Orbital robotics

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The design and training of an intelligent sensor implementation

The design and training of an intelligent sensor implementat...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Xiangui Yu N.K. Loh G.A. Jullien W.C. Miller Department of Electrical Engineering University of Windsor Windsor ONT Canada Center for Robotics and Advanced Automation Dodge Hall of Engineering Oakland University Rochester MI USA

The design and VLSI implementation of an intelligent sensor that can be used for process control applications requiring image capture or non-contact measurement is presented in this paper. The sensor architecture is based on an analog VLSI realization of an artificial neural network with an integrated photosensitive array. A 10/spl times/10 array of photosensitive cells has been designed as the input nodes to a two-layer feedforward neural network. Four groups each with 25 photosensors are fully connected to 4 neurons respectively in the hidden layer. The 16 hidden neurons are fully connected to 5 neurons in the output layer. A development program has been written that determines the optimal set of weights using the modified backpropagation algorithm. The neural network has been trained to recognize 18 different types of patterns. The final implementation of this intelligent sensor has the dimensions of 4500/spl times/4820 design scale microns and contains approximately 1630 analog devices fabricated using a 3/spl mu/ single-polysilicon, double-metal, p-well CMOS process.< >

关键词： Intelligent sensors Sensor arrays Neurons Very large scale integration Artificial neural networks Neural networks Process control Feedforward neural networks Backpropagation algorithms Pattern recognition

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