scholarly journals DYNAMIC MODEL AND CONTROL OF 2-DOF ROBOTIC ARM

2018 ◽  
Vol 8 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Mesut Hüseyinoğlu ◽  
Tayfun ABUT
Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 71
Author(s):  
Luz M. Sanchez-Rivera ◽  
Rogelio Lozano ◽  
Alfredo Arias-Montano

Hybrid Unmanned Aerial Vehicles (H-UAVs) are currently a very interesting field of research in the modern scientific community due to their ability to perform Vertical Take-Off and Landing (VTOL) and Conventional Take-Off and Landing (CTOL). This paper focuses on the Dual Tilt-wing UAV, a vehicle capable of performing both flight modes (VTOL and CTOL). The UAV complete dynamic model is obtained using the Newton–Euler formulation, which includes aerodynamic effects, as the drag and lift forces of the wings, which are a function of airstream generated by the rotors, the cruise speed, tilt-wing angle and angle of attack. The airstream velocity generated by the rotors is studied in a test bench. The projected area on the UAV wing that is affected by the airstream generated by the rotors is specified and 3D aerodynamic analysis is performed for this region. In addition, aerodynamic coefficients of the UAV in VTOL mode are calculated by using Computational Fluid Dynamics method (CFD) and are embedded into the nonlinear dynamic model. To validate the complete dynamic model, PD controllers are adopted for altitude and attitude control of the vehicle in VTOL mode, the controllers are simulated and implemented in the vehicle for indoor and outdoor flight experiments.


With the development of information technology, many applications of robots are increasingly being applied to support research, learning, and teaching. This paper mainly investigates the modeling and simulation of a robotic arm with 3 degrees of freedom (dofs) for different applications. First, Kinematics and dynamics model of the robot based on the standard Denavit Hartenberg (D-H) modeling method, where the forward kinematics of robot is analyzed and computed to obtain by using the inverse kinematics, and then the solution of the robot dynamics is derived. Second, a CAD model of the robot is designed on CATIA software to convert to MapleSim software to simulation and control. Final, numerical simulation is presented to display results. This work provides a potential basis for the realization of the robotic arm in the industrial, education, and research field, which is of great significance for improving manufacturing efficiency and support teaching and research in the robot field.


Author(s):  
Yi-Chang Wu ◽  
Huan-Chun Wang

Robots have been used in various areas to replace manpower, reduce costs, and facilitate more effective resource allocation. This study sought to assist the business of the bureau by developing two robots using the Robot Operating System. The developed robots have autonomous intelligent navigation functions and are suited to monitor the environment of <br /> the laboratories in the bureau. One robot had a temperature and humidity sensor and an infrared thermal camera, and it could be used to patrol and monitor the laboratory environment. The other robot had drawers in which specimens could be placed; robotic arm in the elevator could coordinate and control elevators, enabling the robot to move and transport specimens autonomously. Plenty of tests were conducted to verify the feasibility <br /> and practicality.


ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Mi-Hyun Park ◽  
Eun-Gyeong Shin ◽  
Heung-Reol Lee ◽  
In-Soo Suh

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.


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