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检索条件"机构=Advanced Robotics and Intelligent Systems（ARIS） Lab.School of Engineering"

共 90 条记录，以下是71-80 订阅

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Optimization of a Neural Dynamics Based Controller for a Nonholonomic Mobile Robot Using Genetic Algorithms

Optimization of a Neural Dynamics Based Controller for a Non...

引用

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

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Behavior-based path planning and tracking control of a nonholonomic mobile robot

Behavior-based path planning and tracking control of a nonho...

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IEEE/ASME (AIM) International Conference on advanced intelligent Mechatronics

作者： Haowen Yang S.X. Yang Q.-H. Max Meng Advanced Robotics and Intelligent Systems (ARIS) Lab University of Guelph Guelph ON Canada Department of Electronic Engineering The Chinese University of Hong Kong Shatin NT Hong Kong

A neural dynamics based approach to real-time path planning and tracking control is proposed in this paper for mobile robot navigation. A collision-free reference position is generated in real time for a point robot by a local behavior-based neural dynamics based path planner. The proposed tracking controller is based on neural dynamics and nonlinear feedback techniques. The nonholonomic constraint of the mobile is considered in the proposed tracking controller. The mobile robot is capable of avoiding the deadlocks, and generating a smooth path and velocity without suffering from any speed jumps that exists in the typical nonlinear feedback tracking controllers.

关键词： Path planning Mobile robots Target tracking intelligent robots Navigation Sliding mode control intelligent systems Control systems Neurofeedback System recovery

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Real-time path planning with deadlock avoidance of multiple cleaning robots

Real-time path planning with deadlock avoidance of multiple ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Chaomin Luo S.X. Yang D.A. Stacey Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph ONT Canada Department of Computing and Information Science University of Guelph Guelph ONT Canada

In this paper, a cooperative sweeping strategy with deadlock avoidance of complete coverage path planning for multiple cleaning robots in a changing and unstructured environment is proposed, using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots. The multiple cleaning robots sweeping will not be trapped in deadlock situations.

关键词： Path planning System recovery Cleaning Orbital robotics Robot sensing systems intelligent robots Neural networks Computational modeling Chaos intelligent systems

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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...

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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

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Area-covering operation of a cleaning robot in a dynamic environment with unforeseen obstacles

Area-covering operation of a cleaning robot in a dynamic env...

引用

IEEE International Symposium on Computational Intelligence in robotics and Automation (CIRA)

作者： S.X. Yang C. Luo Q.-H.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 Hong Kong China

Area-covering operation is a special kind of path planning, which requires the robot path to cover every part of the workspace. Area covering is an essential issue for cleaning robots and many other robotic applications such as painter robots, land mine detectors, lawn mowers, and windows cleaners. In this paper, a novel biologically inspired neural network approach to area-covering operation with avoidance of unforeseen obstacles is proposed for a cleaning robot in a dynamic environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from a biological membrane model. There are only local lateral connections among neurons, thus the computational complexity depends linearly on the neural network size. The proposed approach is compared to fuzzy logic based, rule based and re-planning based models. It shows that the proposed model is capable of planning more reasonable and shorter area-covering path with obstacle avoidance. The proposed model algorithm is computationally efficient, and can also deal with changing environments.

关键词： Cleaning Robots Neural networks Biological system modeling Path planning Landmine detection Neurons Detectors Equations Biomembranes

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Fuzzy control of a behavior-based mobile robot

Fuzzy control of a behavior-based mobile robot

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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

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GA Optimized Sliding Mode Control of a Mobile Robot

GA Optimized Sliding Mode Control of a Mobile Robot

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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

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

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

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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

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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

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Ultrasonic sensor based fuzzy obstacle avoidance behaviors

Ultrasonic sensor based fuzzy obstacle avoidance behaviors

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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

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