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检索条件"机构=Advanced Engineering Robotics"

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Neural network application in robot motion planning

Neural network application in robot motion planning

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： X. Yang M. Meng Advanced Robotics & Teleoperation Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

The application of neural networks to real-time motion planning of robotic systems is studied. The proposed framework, using biologically inspired neural networks, for robot motion planning with obstacle avoidance in a nonstationary environment is computationally efficient. The neural dynamics of each neuron in the topologically organized neural network is characterized by a simple shunting equation derived from Hodgkin and Huxley's (1952) membrane model. The real-time optimal robot motion is planned through the dynamic activity landscape of the neural network that represents the dynamic environment. The proposed model can deal with point mobile robots, manipulation robots, holonomic and nonholonomic car-like robots and multi-robot systems. The efficiency and effectiveness are demonstrated by simulation studies.

关键词： Neural networks Robot motion Motion planning Mobile robots Real time systems Biology computing Computer networks Neurons Equations Biomembranes

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Influence of mode truncation and elongation deformation to a flexible manipulator

Influence of mode truncation and elongation deformation to a...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Yan Liu M. Meng D. Wang ART (Advanced Robotics and Teleoperation) Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

Influences of the mode truncation and the longitudinal elongation on the dynamic response of a single link flexible robotic manipulator are studied. Firstly, the relationship between the mode truncation and the inertia matrix of the flexible manipulator dynamic equation is derived. The condition number of the inertia matrix is computed with respect to the number of mode shapes. The inertia matrix is becoming ill posed with the increasing of the mode number. Secondly, the dynamic model with longitudinal elongation is derived. The influence of the longitudinal elongation on the dynamic response, which is caused by the transversal deformation of the system, is analyzed.

关键词： Manipulator dynamics Robots Deformable models Frequency Payloads Equations Orbital robotics Angular velocity Subspace constraints Shape

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A study of limit cycles and chaotic motions of a single-link robot manipulator during slow motion

A study of limit cycles and chaotic motions of a single-link...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dalong Wang Yan Liu Max Meng ART Advanced Robotics & Teleoperation Lab Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In this paper, limit cycles and chaotic motion of a single-link robot manipulator moving at low speed are investigated. The dynamic model of the one-link robot manipulator is set up in which different joint friction is considered. By using the describing function of the nonlinear part of the robot control system, stable limit cycles are proven to exist. The chaotic motion is shown via a Poincare map. Numerical simulations identify the chaotic zone location near the imaginary axis of the complex plane.

关键词： Limit-cycles Chaos Friction Manipulator dynamics Service robots Nonlinear dynamical systems Robot control Control system synthesis Robot sensing systems Torque

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A neural network approach to real-time collision-free navigation of 3-DOF robots in 2D

A neural network approach to real-time collision-free naviga...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Xianyi Yang M. Meng ART (Advanced Robotics & Teleoperation) Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

A neural network approach to real-time collision-free navigation of holonomic 3-degree-of-freedom (DOF) robots in a nonstationary environment is proposed. This approach is based on a biologically inspired model for dynamic trajectory generation of a point robot or a multi-joint robot manipulator. The state space of the neural network is three-dimensional (3D), where two represent the spatial position in the 2D Cartesian workspace and one represents the orientation of the robot. This model is capable of generating a real-time optimal navigation path for 3-DOF robots through the dynamic neural activity landscape without explicitly optimizing any cost functions, without any learning process, and without any local collision checking procedures. Therefore it is computationally efficient. In addition, this model can deal with real-time navigation with sudden environmental changes, navigation of a robot with multiple targets, and navigation of multiple robots. The stability of the neural network is guaranteed by Lyapunov stability analysis. The effectiveness and efficiency are demonstrated through simulation studies.

关键词： Neural networks Navigation Orbital robotics Biological system modeling Manipulator dynamics Robots State-space methods Cost function Stability analysis Lyapunov method

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An efficient neural network model for path planning of car-like robots in dynamic environment

An efficient neural network model for path planning of car-l...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Xianyi Yang M. Meng ART Advanced Robotics & Teleoperation Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

A neural network model is proposed for real-time path planning with obstacle avoidance of car-like robots in a dynamic environment. Each neuron in this biologically inspired, topologically organised neural network has only local lateral connections. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over the free workspace nor the collision paths, without explicitly optimising any cost functions, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures. Therefore it is computationally efficient. The stability and convergence of the neural network system is proved using Lyapunov stability analysis. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

关键词： Neural networks Path planning Biological system modeling Robots Neurons Cost function Stability analysis Convergence Lyapunov method Computational modeling

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Frequency analysis of a flexible robot manipulator

Frequency analysis of a flexible robot manipulator

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： D. Wang M. Meng ART (Advanced Robotics & Teleoperation) Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In this paper, the effect of longitudinal elongation of a flexible robot manipulator is investigated using frequency analysis. The dynamic model of a flexible manipulator without longitudinal elongation is considered and the basic frequency of the flexible manipulator is computed. With the increasing length and weight of the manipulator, the basic frequency decreases. The instability of the dynamic response will occur when the angular velocity exceeds the basic frequency. In order to avoid the unstable dynamics, the longitudinal elongation that is caused by the flexible deformation is considered in the dynamic model. From the frequency analysis, with the increase of angular speed of the manipulator, the basic frequency will rise.

关键词： Frequency Robots Manipulator dynamics Angular velocity Deformable models Solid modeling Equations Resonance Propellers Nonlinear dynamical systems

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Sensory motor control of wing beat in locust

Sensory motor control of wing beat in locust

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

作者： S.X. Yang M. Meng ART (Advanced Robotics & Teleoperation) Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In this paper, a neural network architecture is proposed for sensory motor control of wing beat in a locust. The proposed neural network model is based on the neural anatomy and function of the neurons and sense organs involved in the flight control of wing beat in a locust. This model consists of a central pattern generator and a sensory motor network. Both the central nervous mechanisms and the sensory feedback are essential for the generation of the rhythmic output in the motoneuron. A larger input amplitude results in a higher frequency of the central pattern generator signal through a shorter down-strike duration. The feedback from sensory organs increase the wing beat frequency by generating an early, rapid increasing component and by modifying the late component of the elevator motoneuron activity. The model predictions are in qualitative agreement with the corresponding data.

关键词： Motor drives Neural networks Sense organs Frequency Anatomy Neurons Aerospace control Output feedback Neurofeedback Signal generators

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Generating artificial force for feedback control of teleoperated mobile robots

Generating artificial force for feedback control of teleoper...

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Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289)

作者： Sun-Gi Hong Ju-Jang Lee Seungho Kim Department of Electrical Engineering KAIST Taejon South Korea Advanced Robotics Department KAERI Taejon South Korea

One of the most important issues in the teleoperation is to provide the sense of telepresence so as to conduct the task more reliably. In particular, teleoperated mobile robots need to have some kind of backup system when the operator is blind to the remote situation owing to failure of the vision system. In Hong S-G et al. (see Mechatronics), the idea of artificial force reflection was proposed to enhance the reliability of operation when the mobile robot travels over plain ground. We extend these results to help the teleoperator even when the robot climbs stairs. The extended artificial force reflection method has two modes: traveling on plain ground and climbing stairs. When traveling over plain ground, the force information is artificially generated using range data from the environment while generating the impulse force when climbing stairs. To verify the validity of our algorithm, we develop a simulator which consists of a joystick and visual display system. Through experiments using this system, we confirm the validity and effectiveness of our new artificial force reflection in teleoperated mobile robots.

关键词： Force control Force feedback Feedback control Mobile robots Reflection Humans Force measurement Teleoperators Orbital robotics Machine vision

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Real-time tracking control of robot manipulators with online learning based approach

Real-time tracking control of robot manipulators with online...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Xianyi Yang M. Meng ART (Advanced Robotics & Teleoperation) Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

An online learning based approach to real-time tracking control of robot manipulators is proposed. This controller involves a single-layered neural network together with a traditional PD feedback loop, which inherits advantages from both the neural networks and the PD controller. It is capable of achieving real-time fine tracking control of robot manipulators under significant uncertainties, without any prior knowledge of the robot dynamics, and without any off-line training procedures. In addition, it is capable of quickly compensating sudden changes of robot dynamics. The proposed controller is computationally simple. The global system stability and convergence are proved using Lyapunov stability analysis. A model variation is presented in the discussion. The effectiveness and the efficiency are demonstrated through simulation and comparison studies.

关键词： Robot control Neural networks Manipulator dynamics Feedback loop PD control Uncertainty Stability analysis Convergence Lyapunov method Computational modeling

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A neural network approach to real-time motion planning and control of robot manipulators

A neural network approach to real-time motion planning and c...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xianyi Yang M. Meng Art Advanced Robotics & Teleoperation Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

关键词： Neural networks Motion control Motion planning Manipulator dynamics Orbital robotics Robot control Neurons Equations State-space methods Parallel robots

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