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Real-time motion planning of car-like robots

Real-time motion planning of car-like robots

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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： Xianyi Yang Max Meng Advanced Robotics and Teleoperation Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

A neural network approach is proposed for real-time collision-free motion planning of holonomic and nonholonomic car-like robots in a nonstationary environment. This model is capable of planning real-time robot motion with sudden environmental changes, motion of a car with multiple targets, and motion of multiple robots. The proposed neural network model is biologically inspired, where the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. There are only local connections among neurons. The real-time optimal robot motion 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 optimizing any cost functions, without any prior knowledge of the dynamic environment, without any learning process, and without any local collision checking procedures. Therefore it is computationally efficient. The stability of the neural network is guaranteed by Lyapunov stability analysis. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

关键词： Motion planning Neural networks Biological system modeling Robot motion Neurons Equations Cost function Stability analysis Lyapunov method Computational modeling

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Neural network application in robot motion planning

Neural network application in robot motion planning

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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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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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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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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...

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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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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...

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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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Influence of shear, rotary inertia on the dynamic characteristics of flexible manipulators

Influence of shear, rotary inertia on the dynamic characteri...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

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

The influence of shear, rotary inertia on the dynamic characteristics of flexible manipulators is studied through frequency analysis. In terms of the various geometric effects, dynamic models of flexible manipulators are set up and characteristic equations are derived. By using the analysis, the influences of shear, rotary inertia, axial force on the flexible manipulator are given.

关键词： Manipulator dynamics Frequency Deformable models Equations Nonlinear dynamical systems Payloads Subspace constraints Robots Solid modeling Energy consumption

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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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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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