The Experts below are selected from a list of 333 Experts worldwide ranked by ideXlab platform
Rongjong Wai - One of the best experts on this subject based on the ideXlab platform.
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adaptive moving target tracking control of a vision based mobile robot via a Dynamic petri recurrent fuzzy neural network
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Rongjong Wai, Youwei LinAbstract:In this study, an adaptive moving-target tracking control (AMTC) scheme via a Dynamic Petri recurrent fuzzy neural network (DPRFNN) is constructed for a vision-based mobile robot with a tilt camera. In this study, the Dynamic model of a vision-based mobile robot system, including a nonholonomic mobile robot and a tilt camera based on the concepts of mechanical geometry and motion Dynamics, is developed first. Then, a continuously adaptive mean shift algorithm is adopted for the moving-object detection, and a model-based conventional sliding-mode control (CSMC) strategy is introduced. In order to relax the control design dependent on detailed system information and alleviate chattering phenomena caused by the inappropriate selection of uncertainty bounds, it further designs a model-free AMTC scheme with a DPRFNN to imitate the CSMC strategy. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed AMTC scheme. The corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence, as well as robust control performance without detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed AMTC scheme is verified by numerical simulations under different target tracking, and its superiority is indicated in comparison with the CSMC system. Furthermore, experimental results are also provided to verify the validity of the proposed AMTC scheme in practical applications.
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robust path tracking control of mobile robot via Dynamic petri recurrent fuzzy neural network
Soft Computing, 2010Co-Authors: Rongjong Wai, Chiaming Liu, Youwei LinAbstract:This study focuses on the design of robust path tracking control for a mobile robot via a Dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.
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design of Dynamic petri recurrent fuzzy neural network and its application to path tracking control of nonholonomic mobile robot
IEEE Transactions on Industrial Electronics, 2009Co-Authors: Rongjong Wai, Chiaming LiuAbstract:This paper focuses on the design of a Dynamic Petri recurrent fuzzy neural network (DPRFNN), and this network structure is applied to the path-tracking control of a nonholonomic mobile robot for verifying its validity. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal-feedback loops are incorporated into a traditional FNN to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. Moreover, the supervised gradient-descent method is used to develop the online-training algorithm for the DPRFNN control. In order to guarantee the convergence of path-tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by experimental results, and its superiority is indicated in comparison with FNN, RFNN, Petri FNN, and PRFNN control systems.
Youwei Lin - One of the best experts on this subject based on the ideXlab platform.
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adaptive moving target tracking control of a vision based mobile robot via a Dynamic petri recurrent fuzzy neural network
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Rongjong Wai, Youwei LinAbstract:In this study, an adaptive moving-target tracking control (AMTC) scheme via a Dynamic Petri recurrent fuzzy neural network (DPRFNN) is constructed for a vision-based mobile robot with a tilt camera. In this study, the Dynamic model of a vision-based mobile robot system, including a nonholonomic mobile robot and a tilt camera based on the concepts of mechanical geometry and motion Dynamics, is developed first. Then, a continuously adaptive mean shift algorithm is adopted for the moving-object detection, and a model-based conventional sliding-mode control (CSMC) strategy is introduced. In order to relax the control design dependent on detailed system information and alleviate chattering phenomena caused by the inappropriate selection of uncertainty bounds, it further designs a model-free AMTC scheme with a DPRFNN to imitate the CSMC strategy. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed AMTC scheme. The corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence, as well as robust control performance without detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed AMTC scheme is verified by numerical simulations under different target tracking, and its superiority is indicated in comparison with the CSMC system. Furthermore, experimental results are also provided to verify the validity of the proposed AMTC scheme in practical applications.
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robust path tracking control of mobile robot via Dynamic petri recurrent fuzzy neural network
Soft Computing, 2010Co-Authors: Rongjong Wai, Chiaming Liu, Youwei LinAbstract:This study focuses on the design of robust path tracking control for a mobile robot via a Dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.
Daniela Rus - One of the best experts on this subject based on the ideXlab platform.
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Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturing
IEEE Robotics and Automation Letters, 2018Co-Authors: Jeffrey I. Lipton, Aidan J. Fay, Daniela RusAbstract:We demonstrate a low-cost telerobotic system that leverages commercial virtual reality (VR) technology and integrates it with existing robotics control infrastructure. The system runs on a commercial gaming engine using off-the-shelf VR hardware and can be deployed on multiple network architectures. The system is based on the homunculus model of mind wherein we embed the user in a VR control room. The control room allows for multiple sensor displays, and Dynamic Mapping between the user and robot. This Dynamic Mapping allows for selective engagement between the user and the robot. We compared our system with state-of-the-art automation algorithms and standard VR-based telepresence systems by performing a user study. The study showed that new users were faster and more accurate than the automation or a direct telepresence system. We also demonstrate that our system can be used for pick and place, assembly, and manufacturing tasks.
Chiaming Liu - One of the best experts on this subject based on the ideXlab platform.
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robust path tracking control of mobile robot via Dynamic petri recurrent fuzzy neural network
Soft Computing, 2010Co-Authors: Rongjong Wai, Chiaming Liu, Youwei LinAbstract:This study focuses on the design of robust path tracking control for a mobile robot via a Dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.
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design of Dynamic petri recurrent fuzzy neural network and its application to path tracking control of nonholonomic mobile robot
IEEE Transactions on Industrial Electronics, 2009Co-Authors: Rongjong Wai, Chiaming LiuAbstract:This paper focuses on the design of a Dynamic Petri recurrent fuzzy neural network (DPRFNN), and this network structure is applied to the path-tracking control of a nonholonomic mobile robot for verifying its validity. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal-feedback loops are incorporated into a traditional FNN to alleviate the computation burden of parameter learning and to enhance the Dynamic Mapping of network ability. Moreover, the supervised gradient-descent method is used to develop the online-training algorithm for the DPRFNN control. In order to guarantee the convergence of path-tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by experimental results, and its superiority is indicated in comparison with FNN, RFNN, Petri FNN, and PRFNN control systems.
Jeffrey I. Lipton - One of the best experts on this subject based on the ideXlab platform.
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Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturing
IEEE Robotics and Automation Letters, 2018Co-Authors: Jeffrey I. Lipton, Aidan J. Fay, Daniela RusAbstract:We demonstrate a low-cost telerobotic system that leverages commercial virtual reality (VR) technology and integrates it with existing robotics control infrastructure. The system runs on a commercial gaming engine using off-the-shelf VR hardware and can be deployed on multiple network architectures. The system is based on the homunculus model of mind wherein we embed the user in a VR control room. The control room allows for multiple sensor displays, and Dynamic Mapping between the user and robot. This Dynamic Mapping allows for selective engagement between the user and the robot. We compared our system with state-of-the-art automation algorithms and standard VR-based telepresence systems by performing a user study. The study showed that new users were faster and more accurate than the automation or a direct telepresence system. We also demonstrate that our system can be used for pick and place, assembly, and manufacturing tasks.
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baxter s homunculus virtual reality spaces for teleoperation in manufacturing
arXiv: Robotics, 2017Co-Authors: Jeffrey I. LiptonAbstract:Expensive specialized systems have hampered development of telerobotic systems for manufacturing systems. In this paper we demonstrate a telerobotic system which can reduce the cost of such system by leveraging commercial virtual reality(VR) technology and integrating it with existing robotics control software. The system runs on a commercial gaming engine using off the shelf VR hardware. This system can be deployed on multiple network architectures from a wired local network to a wireless network connection over the Internet. The system is based on the homunculus model of mind wherein we embed the user in a virtual reality control room. The control room allows for multiple sensor display, Dynamic Mapping between the user and robot, does not require the production of duals for the robot, or its environment. The control room is mapped to a space inside the robot to provide a sense of co-location within the robot. We compared our system with state of the art automation algorithms for assembly tasks, showing a 100% success rate for our system compared with a 66% success rate for automated systems. We demonstrate that our system can be used for pick and place, assembly, and manufacturing tasks.
