检索结果-内蒙古大学图书馆

International Conference on Control and Automation (ICCA)

作者： Hao Li S.X. Yang F. Karray Pattern Analysis and Machine Intelligence (PAMI) Lab Systems Design Engineering University of Waterloo Waterloo ONT Canada Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph ONT Canada

In this paper, a neural dynamics based controller for a nonholonomic mobile robot is proposed. The turn angle of the robot in the proposed model is characterized by a biologically inspired shunting equation derived from Hodgkin and Huxley’s membrane equation. This model is capable of generating smooth steering velocity command that drives the robot to track desired paths. Some parameters in the proposed neural dynamics based controller need to be selected. A genetic algorithm is designed to optimize the model parameters that can guarantee the convergence of tracking error of the mobile robot. Simulation studies of a fourdegree-of-freedom mobile robot are conducted, which demonstrate the effectiveness of the proposed motion controller.

关键词： Mobile robots Genetic algorithms Biological system modeling Equations Biomembranes Algorithm design and analysis Design optimization Convergence Motion control

来源：评论

学校读者我要写书评

暂无评论

Neural network robot controller based on structural learning with forgetting

Neural network robot controller based on structural learning...

引用

IEEE International Symposium on intelligent Control (ISIC)

作者： Xiang Yu S.X. Yang M. Ishikawa Dvanced Robotics and Intelligent Systems (ARK) Lab School of Engineering University of Guelph Guelph ONT Canada Department of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan

In this paper, a neural network based controller is proposed for robot manipulators. By considering the second order term of the Taylor expansion of the robot dynamics, the weight tuning algorithm can guarantee the tracking performance of the robot with unknown dynamics. The generic structure selection problem for the neural network controller is addressed by using the structural learning with forgetting, which can automatically remove the redundancy in the structure. Simulations have been conducted on trajectory tracking for various elliptic trajectories. The result demonstrates the effectiveness of the proposed controller.

关键词： Neural networks Robot control Biological neural networks Manipulator dynamics intelligent robots Control systems Robust stability Trajectory Power system modeling Equations

来源：评论

学校读者我要写书评

暂无评论

Fuzzy control of a behavior-based mobile robot

Fuzzy control of a behavior-based mobile robot

引用

IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： S.X. Yang Hao Li M. Meng Advanced Robotics and Intelligent Systems Lab School of Engineering University of Guelph Guelph ONT Canada Department of Electronic Engineering Chinese University of Hong Kong New Territories Hong Kong China

In this paper, a fuzzy controller is developed for of an autonomous nonholonomic mobile robot, which was successfully built with behavior-based artificial intelligence that is implemented by several levels of competences and behaviors. The Lyapunov's direct method is used to formulate a class of control laws that guarantee the convergence of the steering errors to zero. Certain constraints for the control laws are presented for the selection of a suitable rule base for the fuzzy controller, which makes the system asymptotically stable. The stability of the proposed fuzzy controller is proved theoretically and demonstrated by simulation studies. Experiments are also conducted to investigate the performance of the developed fuzzy controller.

关键词： Fuzzy control Mobile robots Control systems Navigation intelligent robots Fuzzy systems Error correction Artificial intelligence Wheels intelligent systems

来源：评论

学校读者我要写书评

暂无评论

An Improved Self-Organizing Map Neural Network Approach to Path Planning of a Multi-robot System

An Improved Self-Organizing Map Neural Network Approach to P...

引用

2003年中国智能自动化会议

作者： Simon X．Yang Max Q．-H．Meng Advanced Robotics and Intelligent Systems(ARIS)Lab School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Department of Electronic Engineering Chinese University of Hong Kong Shatin N．T. Hong Kong

In this paper, an improved self-organizing map (SOM) neural network approach is proposed for path planning of a multi-robot system. Different from other path planning approaches, which focus on obstacle avoidance for a single robot or collision avoidance between robots, the proposed approach focuses on the coordination of multirobots and a near-optimum solution within a reasonable computation time. Different from the conventional selforganizing map approach that is mainly used for classification or clustering problem, the improved SOM neural network approach can be used for path planning of a multirobot system with fast convergence. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

关键词： An Improved Self-Organizing Map Neural Network Approach to Path Planning of a Multi-robot System

来源：评论

学校读者我要写书评

暂无评论

GA Optimized Sliding Mode Control of a Mobile Robot

GA Optimized Sliding Mode Control of a Mobile Robot

引用

2003年中国智能自动化会议

作者： Simon X．Yang Peter X．Liu Advanced Robotics and Intelligent Systems(ARIS)Lab School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Department of Systems and Computer Engineering Carleton University Ottawa Canada K1S 5B6 School of Computer Science University of Windsor Windsor ON N9B 3P4 Canada

Due to the nonholonomic constraint and restricted mobility, the design of stabilizing control laws for a mobile is a challenging problem. Sliding mode control is a robust design methodology based on a sliding surface and a Lyapunov's stability theorem, where the system uncertainties and external disturbances can be handled under the invariance characteristics of system's sliding condition with guaranteed system stability. In this paper, a genetic algorithm is used to optimize the parameters in sliding mode controller for a nonholonomic mobile robot. The effectiveness of the proposed GA based sliding model controller is demonstrated by simulation studies.

关键词： Mobile GA Optimized Sliding Mode Control of a Mobile Robot

来源：评论

学校读者我要写书评

暂无评论

An evolutionary visual landmark recognition system

An evolutionary visual landmark recognition system

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Hao Li S.X. Yang Advanced Robotics and Intelligent Systems (ARIS) Lab University of Guelph Guelph Ontario Canada

A vision-based landmark recognition system by using the evolutionary principle for robot navigation tasks is implemented in this study. The research is aimed at using the GA to do pattern matching. The basic idea is to use genetic algorithms to find the best matching between nodes of the two patterns. The evaluation function can be defined in terms of total differences in magnitudes of nodes between the desired pattern and the real pattern. A search method based on genetic algorithms for pattern recognition in digital images is implemented as the vision layer for a behavior based mobile robot. The vision layer can recognize artificial landmarks by searching all the pro-defined patterns using the GA. Then it generates the desired behavior corresponding to various landmarks. The results of the algorithm is promising and has a high accuracy in classifying the input patterns. The effectiveness of the developed system is demonstrated by simulation and experimental studies.

关键词： Pattern recognition Genetic algorithms Neural networks Robot vision systems Mobile robots intelligent robots intelligent systems Artificial intelligence Pattern matching Machine intelligence

来源：评论

学校读者我要写书评

暂无评论

Real-time collision-free motion planning of nonholonomic robots using a neural dynamics based approach

Real-time collision-free motion planning of nonholonomic rob...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： H. Xu S.X. Yang Advanced Robotics & Intelligent Systems ARIS Lab School of Engineering University of Guelph Guelph ONT Canada

A novel neural dynamics based approach to smooth, continuous and collision-free path generation of an autonomous nonholonomic mobile robot is proposed. The robot behavior, such as target acquisition and obstacle avoidance, are completely controlled by two control variables, the heading direction and the forward velocity of the robot. The dynamics of these control variables is characterized by a biologically inspired shunting neural model, whose inputs are from the target and obstacles that are acquired relying on measurable sensors information only. The target input produces an attractive force, while the obstacle inputs form repulsive forces to the mobile robot. Each force votes for a certain value of control variables that have unique values at a certain time. The collision-free path and the velocity control commands of the robot are generated through the dynamics of control variables. The kinematic constraints of mobile robot is respected. A series of simulation results show that the proposed approach can be successfully applied to both static and dynamic environments, as well as multi-robot systems with effective and efficient computation.

关键词： Motion planning Velocity control Mobile robots Robot sensing systems Force sensors Biological control systems Biological system modeling Biosensors Sensor phenomena and characterization Voting

来源：评论

学校读者我要写书评

暂无评论

Ultrasonic sensor based fuzzy obstacle avoidance behaviors

Ultrasonic sensor based fuzzy obstacle avoidance behaviors

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Hao Li S.X. Yang Advanced Robotics and Intelligent Systems (ARIS) Lab School of Engineering University of Guelph Guelph ONT Canada

来源：评论

学校读者我要写书评

暂无评论

Real-time torque control of nonholonomic mobile robots with obstacle avoidance

Real-time torque control of nonholonomic mobile robots with ...

引用

IEEE International Symposium on intelligent Control (ISIC)

作者： Tiemin Hu S.X. Yang Advanced Robotics & Intelligent Systems (ARIS) Lab School of Engineering University of Guelph Guelph ONT Canada

In this paper, a novel torque controller is presented for nonholonomic mobile robots with obstacle avoidance. In the proposed controller, based on the artificial potential fields technique, an obstacle torque is introduced in the controller, which acts locally to push the robot away from the obstacles. The environment is initially assumed to be completely unknown, except the target location. Environment information is obtained from onboard robot sensors that have limited visibility range only. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that express the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. System stability and convergence are rigorously proved using a Lyapunov theory, subject to unmodeled disturbance and bounded unstructured dynamics. The real-time fine control of mobile robots is achieved through on-line learning of the neural network without any off-line learning procedures. A series of simulation results show that the proposed controller can be successfully applied to both static and dynamic environments, as well as a multi-robot system.

关键词： Torque control Mobile robots Vehicle dynamics Robot sensing systems Path planning intelligent robots Neural networks Nonlinear dynamical systems Kinematics intelligent systems

来源：评论

学校读者我要写书评

暂无评论

REAL-TIME PATH PLANNING AND TRACKING CONTROL USING A NEURAL DYNAMICS BASED APPROACH

引用

IFAC Proceedings Volumes 2002年第1期35卷 103-108页

作者： Simon X. Yang Eric Hu Advanced Robotics and Intelligent Systems (ARIS) Lab School of Engineering University of Guelph Guelph ON N1G 2W1 Canada

Real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment is investigated using a neural dynamics based approach. The real-time robot path is generated through a dynamic neural activity landscape of a topologically organized neural network that represents the changing environment. The dynamics of each neuron is characterized by an additive neural dynamics model. The real-time tracking velocities are generated by a novel non-time based controller, which is based on the conventional event based control technique and an additive model. The effectiveness and efficiency of this approach are demonstrated through simulation and comparison studies.

关键词： mobile robots path planning obstacle avoidance velocity control neural dynamics neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：