Adaptive Kinematic Control of Mobile Robot based on Neural Networks: Case Study: National Instruments Starter Kit 2 (DANI Robot)
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This item is printed on demand - Print on Demand Neuware - The applications of mobile robots have grown significantly during the last few years due to the fast development in sensors and microprocessors systems. These applications include material handling, hospital services, military applications . etc. The need for a precise motion control becomes very crucial in order to support such applications. The adaptive motion control for mobile robot is one of the important areas of research. The design of an adaptive kinematic controller for a nonholonomic differential drive mobile robot based on neural network topology is considered in this work, and work is divided into three stages. Firstly, to identify the inverse kinematics behavior of the differential drive mobile robot system, the Multi-Layer Perceptron neural network has been used. The second stage is the simulation test to check the robustness of the controller. The final stage is the practical work. A National Instrument differential drive mobile robot platform has been used which is perfectly compatible with the LabVIEW2011 software. The controller is implemented in LabVIEW2011 and then deployed to the mobile robot kit through a wireless communication. 124 pp. Englisch.

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Case Study: National Instruments Starter Kit 2 (DANI Robot), The applications of mobile robots have grown significantly during the last few years due to the fast development in sensors and microprocessors systems. These applications include material handling, hospital services, military applications ... etc. The need for a precise motion control becomes very crucial in order to support such applications. The adaptive motion control for mobile robot is one of the important areas of research. The design of an adaptive kinematic controller for a nonholonomic differential drive mobile robot based on neural network topology is considered in this work, and work is divided into three stages. Firstly, to identify the inverse kinematics behavior of the differential drive mobile robot system, the Multi-Layer Perceptron neural network has been used. The second stage is the simulation test to check the robustness of the controller. The final stage is the practical work. A National Instrument differential drive mobile robot platform has been used which is perfectly compatible with the LabVIEW2011 software. The controller is implemented in LabVIEW2011 and then deployed to the mobile robot kit through a wireless communication.

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The applications of mobile robots have grown significantly during the last few years due to the fast development in sensors and microprocessors systems. These applications include material handling, hospital services, military applications … etc. The need for a precise motion control becomes very crucial in order to support such applications. The adaptive motion control for mobile robot is one of the important areas of research. The design of an adaptive kinematic controller for a nonholonomic differential drive mobile robot based on neural network topology is considered in this work, and work is divided into three stages. Firstly, to identify the inverse kinematics behavior of the differential drive mobile robot system, the Multi-Layer Perceptron neural network has been used. The second stage is the simulation test to check the robustness of the controller. The final stage is the practical work. A National Instrument differential drive mobile robot platform has been used which is perfectly compatible with the LabVIEW2011 software. The controller is implemented in LabVIEW2011 and then deployed to the mobile robot kit through a wireless communication. Paperback, Label: LAP LAMBERT Academic Publishing, LAP LAMBERT Academic Publishing, Produktgruppe: Book, Publiziert: 2015-11-03, Freigegeben: 2015-11-03, Studio: LAP LAMBERT Academic Publishing.

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