Robotic systems used in industries and other complex applications need huge investment, and testing of them under robust conditions are highly challenging. Controlling and testing of such systems can be done with ease with the support of hardware-in-the-loop (HIL) simulation technique and it saves lot of time and resources. The chapter deals on the various interaction methods of robotic systems with physical environments using tactile, force, and vision sensors. It also discusses about the usage of hardware-in-the-loop technique for testing of grasp and task control algorithms in the model of robotic systems. The chapter also elaborates on usage of hardware and software platforms for implementing the control algorithms for performing physical interaction. Finally, the chapter summarizes with the case study of HIL implementation of the control algorithms in Texas Instruments (TI) C2000 microcontroller, interacting with model of Kuka's youBot Mobile Manipulator. The mathematical model is developed using MATLAB software and the virtual animation setup of the robot is developed using the Virtual Robot Experimentation Platform (V-REP) robot simulator. By actuating the Kuka's youBot mobile manipulator in the V-REP tool, it is observed to produce a tracking accuracy of 92% for physical interaction and object handling tasks.
Abstract:This paper investigates the hardware-in-the-loop (HIL) simulation approach for dynamic control of a three-link rigid robot manipulator that possesses ambiguous dynamics and kinematics. The task with two adaptive control schemes has been realized with the objective of task space trajectory-tracking of the end effector of the robotic manipulator. Both proposed controllers are designed by considering the joint reference velocities and the additional separation property.Based on these, the controllers can be referred to as reference velocity (RV) and reference velocity separation (RVS) adaptive controllers, respectively. The RV adaptive controller can yield better performance with proper alterations, without the cost of conventional gain choice. The HIL simulations are carried out with the aid of a model of threelink rigid robotic manipulator, developed using MATLAB/Simulink, and the RV and RVS adaptive controllers were implemented with the C2000 real-time controller. From the HIL simulation, the performance of the two adaptive controllers is analyzed for task space tracking of the robotic manipulator.
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