Modeling and Data Analysis of Robotic Arm-Assisted Device for Detection of Violently Infectious Viruses

This paper addresses the challenge of operational accuracy in robots operating in hazardous environments from a data analytics perspective, particularly in the detection of highly infectious viruses. We examine the issue of bending deformation in the robotic arm when handling objects of differing weights and coordinates, which can lead to significant discrepancies in the claw's position relative to its set target. To circumvent this, we propose an L-shaped auxiliary positioning device and employ numerical analysis to assess its efficacy. Two models were constructed using Abaqus software: a standard robot model (Model-A) and a modified version incorporating our auxiliary device (Model-B). Simulations were run under varying load conditions on the mechanical claw. Results demonstrate that the auxiliary device significantly reduces bending deformation, thereby improving the mechanical claw's operational accuracy. Model-B consistently exhibited superior accuracy compared to Model-A under different working conditions. The study concludes that the proposed auxiliary device, with its simplicity and ease of maintenance, offers a promising solution to enhance robotic accuracy while potentially reducing operational and maintenance costs in similar industrial applications.