A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of ...A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.展开更多
Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so...Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.展开更多
Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the cont...Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the control of herds of cattle, based on polygon coordinate definition of boundaries is limited in the area of land mass coverage and dynamism. This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle, with a view to increase land mass coverage in virtual fencing techniques and dynamism. The monocular vision based depth estimation will be modeled using concept of global Fourier Transform (FT) and local Wavelet Transform (WT) of image structure of scenes (boundaries). The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image;while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution. Each scene picture or image is defined by features v, which contain the set of global (FT) and local (WT) statistics of the image. Scenes or boundaries distances are given by estimating the depth D by means of the image features v. Sound cues of intensity equivalent to the magnitude of the depth D are applied to the animal ears as stimuli. This brings about the desired control as animals tend to move away from uncomfortable sounds.展开更多
A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning...A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.展开更多
This article introduced the vision building concept about human influenza pandemic prevention and control.Different visions were built by creating different shapes of building blocks which also represented different o...This article introduced the vision building concept about human influenza pandemic prevention and control.Different visions were built by creating different shapes of building blocks which also represented different organizations and physical facilities,respectively.The around-view reflection is required to be developed in the process of building so as to search for the ideal pattern.The correlation of all sectors and systems are established to combine different kinds of things,from one family to another,from communities,towns,counties,cities,rural areas, provinces to the state to handle trivial problems.These training objectives have been successfully accomplished,which has not only enriched the knowledge about prevention and control of influenza pandemic between different departments but also clarified the roles and responsibility. It lays the firm foundation for next cooperation between different departments,and make a bridge for the objective and choice of channel over human influenza pandemic prevention and control.展开更多
目前主流工业机器人为封闭式控制结构,存在不开源、二次开发难的问题,因此设计一种基于TwinCAT3(the windows control and automation technology)的跨平台、可移植性好的机器人控制系统架构。该架构包含视觉、运动控制和算法集成与仿...目前主流工业机器人为封闭式控制结构,存在不开源、二次开发难的问题,因此设计一种基于TwinCAT3(the windows control and automation technology)的跨平台、可移植性好的机器人控制系统架构。该架构包含视觉、运动控制和算法集成与仿真控制模块,采用倍福自动化设备规范(automation device specification,ADS)通信技术和实时工业以太网总线技术(ethernet for control automation technology,EtherCAT),建立以计算机(personal computer,PC)和倍福控制器为EtherCAT主站,控制多组从站执行器的一主多从工作模式。该模式结合离线与在线控制、集成数字孪生技术,完成虚拟样机与物理样机的联动;采用开源可扩展架构,便于视觉算法、智能算法等算法集成。经实验验证,此架构具有拓展性好、实时性强的特点。展开更多
针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。...针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。将传统机器学习算法与文章所提出的算法在相同行驶条件下的实时响应进行比较,仿真结果表明,CTNN算法在恶劣的行驶环境中,实时性、鲁棒性均得到一定程度的提高。展开更多
基金This project was supported by the research foundation of China Education Ministry for the scholars from abroad (2002247).
文摘A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.
基金supported by National Key Basic Research and Development Program of China (973 Program,Grant No. 2009CB320602)National Natural Science Foundation of China (Grant Nos. 60834004,61025018)+2 种基金National Science and Technology Major Project of China(Grant No. 2011ZX02504-008)Fundamental Research Funds for the Central Universities of China (Grant No. ZZ1222)Key Laboratory of Advanced Engineering Surveying of NASMG of China (Grant No.TJES1106)
文摘Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.
文摘Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the control of herds of cattle, based on polygon coordinate definition of boundaries is limited in the area of land mass coverage and dynamism. This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle, with a view to increase land mass coverage in virtual fencing techniques and dynamism. The monocular vision based depth estimation will be modeled using concept of global Fourier Transform (FT) and local Wavelet Transform (WT) of image structure of scenes (boundaries). The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image;while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution. Each scene picture or image is defined by features v, which contain the set of global (FT) and local (WT) statistics of the image. Scenes or boundaries distances are given by estimating the depth D by means of the image features v. Sound cues of intensity equivalent to the magnitude of the depth D are applied to the animal ears as stimuli. This brings about the desired control as animals tend to move away from uncomfortable sounds.
基金This work was supported by the National High Technology Research and Development Program of China under Grant 2002AA422160 by the National Key Fundamental Research and the Devel-opment Project of China (973) under Grant 2002CB312200.
文摘A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.
基金supported by a grant form by the World Bank(FIo-016) for China capacity building for highly pathogenic avian influenza prevention and human pandemic influenza preparedness projectgranted by Australia, European Union,and other donor countries
文摘This article introduced the vision building concept about human influenza pandemic prevention and control.Different visions were built by creating different shapes of building blocks which also represented different organizations and physical facilities,respectively.The around-view reflection is required to be developed in the process of building so as to search for the ideal pattern.The correlation of all sectors and systems are established to combine different kinds of things,from one family to another,from communities,towns,counties,cities,rural areas, provinces to the state to handle trivial problems.These training objectives have been successfully accomplished,which has not only enriched the knowledge about prevention and control of influenza pandemic between different departments but also clarified the roles and responsibility. It lays the firm foundation for next cooperation between different departments,and make a bridge for the objective and choice of channel over human influenza pandemic prevention and control.
文摘目前主流工业机器人为封闭式控制结构,存在不开源、二次开发难的问题,因此设计一种基于TwinCAT3(the windows control and automation technology)的跨平台、可移植性好的机器人控制系统架构。该架构包含视觉、运动控制和算法集成与仿真控制模块,采用倍福自动化设备规范(automation device specification,ADS)通信技术和实时工业以太网总线技术(ethernet for control automation technology,EtherCAT),建立以计算机(personal computer,PC)和倍福控制器为EtherCAT主站,控制多组从站执行器的一主多从工作模式。该模式结合离线与在线控制、集成数字孪生技术,完成虚拟样机与物理样机的联动;采用开源可扩展架构,便于视觉算法、智能算法等算法集成。经实验验证,此架构具有拓展性好、实时性强的特点。
文摘针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。将传统机器学习算法与文章所提出的算法在相同行驶条件下的实时响应进行比较,仿真结果表明,CTNN算法在恶劣的行驶环境中,实时性、鲁棒性均得到一定程度的提高。
