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Research on robot trajectory correction on a curved surface has seen a surge in the last two decades to deal with issues in many machining processes. Due to the intricacies involved in complex machining, the real-time correction of robot end-effector position and orientation is highly challenging. The existing approaches are either computationally expensive or inadequate for high accuracy in desired trajectory tracking. Hence, this research explores and implements a novel sophisticated approach, barring minor complications, to empower industrial robots to adjust poses in real-time automatically. In contrast with other studies, the proposed technique does not require any prior geometric information of the workpiece, such as a CAD model or a 3D scan. Our proposed technique relies solely on force/torque sensor feedback information obtained from a 6-axis force/torque sensor installed at the end-effector. When the developed tool comes in contact with the curved surface, the force sensor transmits the data to the pose correction algorithm. The pose correction algorithm estimates the adjustment required to make the tool normal to the surface. The effectiveness and feasibility of the proposed scheme were validated through numerous experiments carried out with a 6-DOF industrial robot. The final results of the curved surface trajectory correction demonstrates that the contact-based robot pose correction has significantly improved. The estimated average error on depth (Z-axis), and angles (Rx,Ry) are 0.7 mm, 0.7 • , and 0.9 • , respectively.

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Recently, unmanned aerial vehicles (UAVs) have witnessed immense popularity in various fields, ranging from surveillance, rescue, and fire fighting to other more sophisticated military and commercial applications. However, due to their highly nonlinear nature and dynamic operational environment, the control of UAVs is still a challenging task. Linear Quadratic-Gaussian Regulator (LQG), is an optimal control technique, which has been very popular for UAVs control. However, for robust performance, an accurate dynamic model of a system is required. In order, to overcome this limitation, the present work couples an integral sliding mode controller with the LQG controller to deal with the modelling inaccuracies. Experimental results of pitch control of the laboratory-based twin rotor MIMO system (TRMS), validate the performance of ISMC-LQG controller.

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