Automation is creating revolution in the present industrial sector, as it reduces manpower and time of production. Our project mainly deals around the shearing operation, were the sheet is picked manually and placed on the belt for shearing which involves risk factor. Our challenge is designing of pick and place operator to carry the sheet from the stack and place it in the shearing machine for the feeding. We have gone through different research papers, articles and had observed the advanced technologies used in other industries for the similar operation. After related study we have achieved the design of a 3-jointed robotic arm were the base is fixed and the remaining joints move in vertical and horizontal directions. The end effector is also designed such that to lift the sheet we use suction cups were the sheet is uplifted with a certain pressure. Here we used Creo-Parametric for design and Autodesk-Inventor 2017 to simulate the designed model.

<p dir="ltr">This dissertation presents the findings and research conducted during my Ph.D. study, which focuses on developing grasp policies for modular end-effectors on mobile manipulation platforms operating in cluttered environments. The primary objective of this research is to enhance the performance and accuracy of robotic manipulation systems in complex, real-world scenarios. The work has potential implications for various domains, including the rapidly growing Industry 4.0 and the advancement of autonomous systems in space habitats.</p><p dir="ltr">The dissertation offers a comprehensive literature review, emphasizing the challenges faced by mobile manipulation platforms in cluttered environments and the state-of-the-art techniques for grasping and manipulation. It showcases the development and evaluation of a Modular End-Effector System (MEES) for mobile manipulation platforms, which includes the investigation of object 6D pose estimation techniques, the generation of a deep learning-based grasping dataset for MEES, the development of a suction cup gripper grasping policy (Sim-Suction), the development of a two-finger grasping policy (Sim-Grasp), and the integration of Modular End-Effector System grasping policy (Sim-MEES). The proposed methodology integrates hardware designs, control algorithms, data-driven methods, and large language models to facilitate adaptive grasping strategies that consider the unique constraints and requirements of cluttered environments.</p><p dir="ltr">Furthermore, the dissertation discusses future research directions, such as further investigating the Modular End-Effector System grasping policy. This Ph.D. study aims to contribute to the advancement of robotic manipulation technology, ultimately enabling more versatile and robust mobile manipulation platforms capable of effectively interacting with complex environments.</p>