Research on Recognition of Grasping Arbitrary Objects by Autonomous Mobile Robot Based on YoloX-Tiny

As intelligent manufacturing enters a new era, autonomous mobile robots have entered a stage of high-quality development. To propose 3D positioning that better meets the needs of arbitrary position capture in the manufacturing industry, we need to focus on innovative ideas based on deep learning. Based on the dynamic evolution of RGB-D camera development, the theoretical analysis framework of 3D positioning development is built according to the internal logic of data-driven. Researchers explain the 3D positioning development mechanism jointly generated by depth model and pose estimation related to RGB-D camera and YoloX-Tiny. In addition, we will continue to explore the possibility of switching to high-image-quality development from the perspective of changing image quality and experimentally deriving 3D positioning. The purpose of developing this technology is to provide the desired results for the manufacturing industry, and researchers are committed to continuously improving the success rate and efficiency of grasping. Therefore, we take a series of measures, including object identification and positioning control based on deep information quality inner loop, constructing the interaction and coordination mechanism between the vision system and robot, and establishing the evaluation system of grasping performance and user satisfaction to realize the high-quality development of technology. It will boost intelligent manufacturing and meet the needs of the manufacturing industry.