As an important part of a factory’s automated production line, industrial robots can perform a variety of tasks by integrating external sensors. Among these tasks, grasping scattered workpieces on the industrial assembly line has always been a prominent and difficult point in robot manipulation research. By using RGB-D (color and depth) information, we propose an efficient and practical solution that fuses the approaches of semantic segmentation and point cloud registration to perform object recognition and pose estimation. Different from objects in an indoor environment, the characteristics of the workpiece are relatively simple; thus, we create and label an RGB image dataset from a variety of industrial scenarios and train the modified FCN (Fully Convolutional Network) on a homemade dataset to infer the semantic segmentation results of the input images. Then, we determine the point cloud of the workpieces by incorporating the depth information to estimate the real-time pose of the workpieces. To evaluate the accuracy of the solution, we propose a novel pose error evaluation method based on the robot vision system. This method does not rely on expensive measuring equipment and can also obtain accurate evaluation results. In an industrial scenario, our solution has a rotation error less than two degrees and a translation error < 10 mm.

Advances in flexible and multifunctional electronic devices have enabled the realization of sophisticated skin for robotics applications. In this paper, a large-scale, flexible and self-powered tactile sensing array (TSA) for sensitive robot skin is demonstrated based on the triboelectric effect. The device, with 4 × 4 sensing units, was composed of a top triboelectric polyethylene terephthalate (PET) layer, a bottom triboelectric copper (Cu) layer and a bottom PET substrate. A low-cost roll-to-roll ultraviolet embossing fabrication process was induced to pattern the large-scale top PET film with microstructures for high-output performance. The working mechanism and output performance of the triboelectric TSA were demonstrated and characterized, exhibiting good stability and high sensitivity. By integrating a tactile feedback system, the large-scale TSA, acting as intelligent skin for an industrial robot, was able to realize emergency avoidance and safety stop for various unknown obstacles under various working conditions. The system also has good real-time performance. By using a large-scale roll-to-roll fabrication method, this work pushes forward a significant step to self-powered triboelectric TSA and its potential applications in intelligent robot skin.