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检索条件"机构=Advanced Robotics and Intelligent Systems ARIS Laboratory School of Engineering"

共 160 条记录，以下是131-140 订阅

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Genetic algorithm based path planning for a mobile robot

Genetic algorithm based path planning for a mobile robot

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Jianping Tu S.X. Yang Advanced Robotics and Intelligent Systems ARIS Laboratory School of Engineering University of Guelph Guelph ONT Canada

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

关键词： Genetic algorithms Path planning Mobile robots Biological cells Fuzzy logic Evolutionary computation intelligent robots Employment Robot sensing systems Fuzzy systems

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A neuro-fuzzy approach to obstacle avoidance of a nonholonomic mobile robot

A neuro-fuzzy approach to obstacle avoidance of a nonholonom...

引用

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

作者： Xiaochuan Wang S.X. Yang Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph ONT Canada

In this paper, a neuro-fuzzy control system is developed for obstacle avoidance of nonholonomic mobile robot built in the aris lab. A combination of four infrared sensors is equipped to detect the distance to obstacles around the mobile robot. The distance information is processed by the proposed neuro-fuzzy controller to adjust the velocities of two separate driven wheels of the robot. Eighteen fuzzy rules are developed for the obstacle avoidance. Experiments demonstrate the effectiveness of the developed neuro-fuzzy controller.

关键词： Mobile robots Fuzzy logic intelligent robots Control systems Wheels Neural networks Automatic control Robot sensing systems Service robots Orbital robotics

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Tracking control of a nonholonomic mobile robot by integrating feedback and neural dynamics techniques

Tracking control of a nonholonomic mobile robot by integrati...

引用

Proceedings 2003 IEEE/RSJ International Conference on intelligent Robots and systems (IROS 2003) (Cat. No.03CH37453)

作者： Haowen Yang S.X. Yang G.S. Mittal Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph ONT Canada

Mobile robots have been one of the challenging topics of control due to their nonholonomic property and restricted mobility. This paper presents the design of a novel tracking controller for a mobile robot by integrating the neural dynamics model into a conventional feedback controller. The proposed controller is capable of generating real-time smooth velocities, and driving the mobile robot to track desired trajectories. It resolves the speed jump problem existing in some previous tracking controllers. Lyapunov stability theory is used to prove the stability of the control system and the convergence of tracking errors to zero. The effectiveness of the proposed controller is demonstrated by simulation and comparison studies.

关键词： Mobile robots Neurofeedback Adaptive control Velocity control Trajectory Lyapunov method Stability Control systems Convergence Error correction

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An improved self-organizing map approach to traveling salesman problem

An improved self-organizing map approach to traveling salesm...

引用

IEEE International Conference on robotics, intelligent systems and Signal Processing

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

ISBN: (纸本)078037925X

In this paper, an improved self-organizing map approach to solving the traveling salesman problem is proposed by fixing the number of nodes in the output layer of neural network, modifying the neighborhood function, and modifying the weight update rules. An overview of previous work on solving the traveling salesman problem is given. An extension of the proposed algorithm can also be used to solve multiple traveling salesman problems and robot path planning. The simulation results demonstrate that the proposed algorithm is capable of providing a better solution within a reasonable time and much faster than conventional self-organizing map algorithms.

关键词： Traveling salesman problems Cities and towns Computer aided instruction intelligent robots intelligent systems Neural networks Path planning Costs Scheduling Books

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A biologically inspired approach to real-time tracking control of a mobile robot

A biologically inspired approach to real-time tracking contr...

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

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

A novel tracking controller is proposed in this paper for real-time navigation of a nonholonomic mobile robot based on a neural dynamics model and a conventional backstepping model. By taking advantage of the features of the neural dynamics in a gated dipole model, the proposed controller is capable of generating smooth, continuous control signals to track a desired path. In addition, it can deal with arbitrarily large initial tracking errors. It resolves the velocity jump problem in conventional backstepping controllers resulted from sudden changes of tracking errors. The proposed approach is not very sensitive to model parameters and converges faster than a previous tracking control model. The effectiveness and efficiency of the proposed approach is demonstrated by a series of simulation and comparison studies.

关键词： Biological control systems Mobile robots Backstepping Torque control intelligent robots Sliding mode control Neural networks Error correction Robot control Real time systems

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Optimal control of supercritical fluid extraction with a hybrid model

Optimal control of supercritical fluid extraction with a hyb...

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IEEE International Symposium on intelligent Control (ISIC)

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

Supercritical fluid extraction is an environmental friendly separation technique in food and chemical industry. Optimal control is an essential issue in supercritical fluid extraction. The objective of this study is to improve the extraction processes to achieve the optimal performance in terms of a higher yield rate with a lower operating cost and a lower safety risk. In this paper, a hybrid control model combining the radial basis function network and the Peng-Robinson equation of state is used to model the supercritical fluid extraction process. An optimal control strategy is developed to select the process parameters. The effectiveness of the proposed control strategy is demonstrated by simulation studies.

关键词： Optimal control Chemical industry Mathematical model Predictive models Equations intelligent robots Cost function Radial basis function networks Separation processes Neural networks

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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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A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot

A fuzzy neural dynamics based tracking controller for a nonh...

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

作者： Yanrong Hu S.X. Yang Advanced Robotics and Intelligent Systems (AFUS) Laboratory School of Engineering University of Guelph Guelph ONT Canada

In this paper, a fuzzy neural dynamics based tracking controller for nonholonomic wheeled mobile robots is proposed. The nonholonomic kinematic constraints are considered in the development of the controller. The proposed model is suitable for both continuous and discrete paths. Fuzzy rules are formulated to deal with the discontinuity in path directions. This model is capable of generating smooth velocity commands to drive the robot to track the desired paths. In the situation with large initial errors, the proposed model can automatically generate a smooth curve to reach the desired robot path from an arbitrary initial configuration without any explicit algorithms for the connection curve. At sharp turns, this model can automatically round off the sharp turns with a smooth curve. The effectiveness of the proposed tracking controller is demonstrated by simulation studies.

关键词： Fuzzy control Mobile robots robotics and automation Robot kinematics Wheels intelligent robots intelligent systems Control systems Navigation Councils

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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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Analysis and optimization of a non-time based motion controller for a nonholonomic mobile robot

Analysis and optimization of a non-time based motion control...

引用

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

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

In this paper, a non-time based tracking controller of a nonholonomic mobile robot is first analyzed. Non-time based motion controllers have been successfully applied to many areas such as robot motion control, multi-robot coordination, force control, robotic teleoperation and manufacturing automation. However, by the traditional non-time based motion controller many suffer from oscillations in both the linear and angular velocities when there is a large initial tracking error. In this paper, a traditional non-time based tracking controller is optimized using a genetic algorithm, which is used to generate the model parameters that could guarantee the system stability and convergence of tracking error. Simulations using a nonholonomic mobile robot model with a four degree of freedom are conducted to investigate the performance of the proposed controller. The results using the proposed model is compared to those of the conventional model. Generally the proposed model performs better than the conventional model because the genetic algorithm can provide better parameters to minimize tracking error and the oscillation.

关键词： Motion analysis Motion control Mobile robots Automatic control Force control Error correction Genetic algorithms Robot motion Robot control Robot kinematics

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