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World Congress on intelligent Control and Automation (WCICA)

作者： Chaomin Luo S.X. Yang M. Meng Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Entire region filling is a special type of robot path planning strategy that requires the mobile robot to cover every part of the whole workspace, which has many applications such as cleaning robots, vacuum cleaners, painter robots, land mine detectors, lawn mowers, and window cleaners. In this paper, a novel biologically inspired neural network approach is proposed for entire region filling with obstacle avoidance of a mobile cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. It can deal with an unstructured environment with irregular obstacles. The effectiveness of the proposed model is demonstrated by simulation results.

关键词： Filling Indoor environments Neural networks Mobile robots Equations Cleaning Landmine detection Neurons Computational modeling Path planning

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A real-time cooperative sweeping strategy for multiple cleaning robots

A real-time cooperative sweeping strategy for multiple clean...

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IEEE International Symposium on intelligent Control (ISIC)

作者： Chaomin Luo S.X. Yang Advanced Robot ics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph ONT Canada

In this paper, a cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying 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. Multiple cleaning robots can cooperate to achieve a common sweeping goal effectively. 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.

关键词： Cleaning Orbital robotics Path planning Robot sensing systems intelligent robots Neural networks robotics and automation Computational modeling Landmine detection System recovery

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Real-time area-covering operations with obstacle avoidance for cleaning robots

Real-time area-covering operations with obstacle avoidance f...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Chaomin Luo S.X. Yang Xiaobu Yuan Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph ONT Canada School of Computer Science Windsor University Windsor ONT Canada

An area-covering operation is a kind of complete coverage path planning, which requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum, robots, painter robots, land mine detectors, lawn mowers, and windows cleaners. In this paper, a novel biologically inspired neural network approach is proposed for complete coverage path planning with obstacle avoidance of a cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The proposed model algorithm is computationally efficient, and can also deal with changing environment. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot path.

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

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A remote control system using Java and CORBA architecture

A remote control system using Java and CORBA architecture

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World Congress on intelligent Control and Automation (WCICA)

作者： Hao Li S.X. Yang Fangju Wang Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Computing & Information Science Department University of Guelph Guelph Canada

Membranes hold promise as a new water treatment method of the future. In this study, a device is designed to test the efficiency of membranes. The device is implemented to be controlled remotely. An Internet based remote control system is implemented on the membrane test device to make the users access to it easier. When using the system, a remote operator only needs a general purpose computer with Internet connection to conduct a test. The engineering objective is to perform robust control over the Internet connection. A control architecture that combines computer and the membrane testing hardware is built. This system has two primary parts, the server part and the client part. A server is used to provide the application to the operator to control the hardware. The client part is executed on the remote operator's computer. The client uses a TCP/IP protocol to connect to the server through the Internet. Communication coordination between the client and the server is developed using Java and Common Object Request Broker Architecture (CORBA).

关键词： Control systems Java Biomembranes Internet Web server System testing Computer architecture Hardware Robust control Application software

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An autonomous mobile robot with fuzzy obstacle avoidance behaviors and a visual landmark recognition system

An autonomous mobile robot with fuzzy obstacle avoidance beh...

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International Conference on Control, Automation, robotics and Vision (ICARCV)

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

Multi-sensor fusion has been a hot topic in the field of robotics. Inspired by the modern philosophy's spirit, the behavior-based systems interact with the real world directly. In this study, a fully autonomous mobile robot is developed that extracts all its knowledge from physical sensors and expresses all its goals and desires as physical action to affect its environment. The control software implements behavior-based artificial intelligence, where the coordination between various sensors are realized by layers of several simple and primitive behaviors similar to those observed in animals. In the developed mobile robot, each module itself generates behaviors. Behaviors corresponding to different sensors have different priorities, where the vision system has the lowest priority, and the ultrasonic sensors and bumper sensors have higher priority. The effectiveness of the developed system is demonstrated by experimental studies.

关键词： Mobile robots Fuzzy systems intelligent sensors Robot sensing systems Robot kinematics intelligent robots Sensor systems Fuzzy sets Artificial intelligence Fuzzy logic

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A NEURAL COMPUTATION MODEL FOR REAL-TIME COLLISION-FREE ROBOT NAVIGATION

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IFAC Proceedings Volumes 2002年第1期35卷 323-328页

作者： Simon X. Yang Max Meng Hao Li Advanced Robotics and Intelligent Systems (ARIS) Lab School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Advanced Robotics and Teleoperation (ART) Lab Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2G7 Canada

A biologically inspired neural computation model is proposed for dynamic planning and tracking control of robots. The dynamic environment is represented by a neural activity landscape of a topologically organized neural network, where each neuron is characterized by a shunting equation. The collision-free path is generated in real-time through the activity landscape without any prior knowledge of the dynamic environment. The real-time tracking control of robots to follow the planned path is also designed using shunting equations. The effectiveness is demonstrated through case studies. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach.

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

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A new model of the visual feedback coordinate transformation in humans based on disturbance noise and feedback error that accounts for time delays

A new model of the visual feedback coordinate transformation...

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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： E. Oyama K.F. MacDorman T. Maeda S. Tachi A. Agah Science and Technology (AIST) National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Intelligent Robotics Laboratory Graduate School of Systems Engineering Wakayama University Wakayama Japan School of Engineering University of Tokyo Bunkyo Tokyo Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

关键词： Humans Delay effects Adaptive control Error correction Biological system modeling Feedback control Convergence Kinematics intelligent robots Velocity control

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Tracking control of a mobile robot with kinematic and dynamic constraints

Tracking control of a mobile robot with kinematic and dynami...

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

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

A path tracking control method with both kinematic and dynamic constraints is proposed for a nonholonomic mobile robot. By incorporating a biologically inspired shunting model into the conventional bang-bang control technique, the proposed tracking controller is capable of generating real-time acceleration commands that can produce smooth, continuous robot velocities, and drive the mobile robot to track the desired trajectories. In the controller design, the accelerations are bounded and the nonholonomic kinematic constraints are satisfied. The stability of the control system and the convergence of tracking errors to zero are proved using a Lyapunov stability theory. The effectiveness of the proposed tracking controller is demonstrated by simulation studies.

关键词： Mobile robots Kinematics Acceleration Biological system modeling Bang-bang control Velocity control Biological control systems Trajectory Stability Control systems

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An efficient neural controller for a nonholonomic mobile robot

An efficient neural controller for a nonholonomic mobile rob...

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

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

In this paper, a novel neural network based controller is developed for real-time fine motion control of a nonholonomic mobile robot with completely unknown robot dynamics and under unmodeled disturbance. By taking advantage of the robot regressor dynamics that express the highly nonlinear robot dynamics in a linear form in terms of the robot dynamic parameters, the neural network consists of a single layer feedforward structure, and the learning algorithm is computationally efficient. Unlike previous works that use a typical backstepping velocity planner as the control input, a novel neural dynamics based velocity planner is used as input. The stability of the proposed control system and the convergence of tracking errors to zero are rigorously proved using the Lyapunov theory. The fine control of mobile robot is achieved through the online learning of the neural network without any off-line learning procedures. The effectiveness and efficiency of the proposed controller is demonstrated by simulation studies.

关键词： Mobile robots Neural networks Motion control Nonlinear dynamical systems Feedforward neural networks Computer networks Backstepping Velocity control Stability Control systems

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Dense range image smoothing using adaptive regularization

Dense range image smoothing using adaptive regularization

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IEEE International Conference on Image Processing

作者： Yiyong Sun Joon-Ki Paik J.R. Price M.A. Abidi Department of Electrical and Computer Engineering Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA Department of Image Engineering Graduate School of Advanced Imaging Science Chung-Ang University Seoul South Korea

We propose an adaptive regularization algorithm for smoothing dense range images using a novel, first order stabilizing function. The stabilizer we suggest is based upon minimizing the reconstructed surface area and is derived in the native, spherical coordinate system of the range scanner. This allows adjustments to be made along only the direction of measurement, thereby preventing the data overlapping problem that can arise in dense images. Adaptation is achieved by adjusting the regularization parameter according to the results of 2D edge analysis. Results indicate effective noise suppression along with well preserved edges and details in the reconstructed, 3D surfaces.

关键词： Smoothing methods Surface reconstruction Image reconstruction Image edge detection intelligent robots Robot kinematics intelligent systems Laboratories Laser noise Noise measurement

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