# Advanced Applications of Industrial Robotics: New Trends and Possibilities
## 3 Recent Achievements in Industrial Robotics Classified according to Implementation Area
### Object Recognition
\begin{table}
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**Objective** & **Technology** & **Approach** & **Improvement** & **Ref.** \\ \hline Extended default ”program from demonstration” feature of collaborative robots to adapt them to environments with moving objects. & Franka Emika Anda cobot with 7 degrees of freedom, with a Realsense D45 RGB-D camera mounted on the end-effector. & Grasping method to fine-tune using reinforcement learning techniques. & The system can grasp various objects from a demonstration, regardless of their position and orientation, in less than 5 min of training time. & [35, 46] \\ \hline Introduction of a set of metrics for primary comparison of robotic systems’ detailed functionality and performance. & Robot with different grippers. & Recognition method and the grasping method. & Developed original robot performance metrics and tested on four robot systems used in the Amazon Robotics Challenge competition. Results of analysis showed the difference between the systems and promising solutions for further improvements. & [35, 47] \\ \hline To build a low-cost system for identifying shapes to program industrial robots for the 2D welding process. & Robot ABB IR 140 with a digital camera, which detects contours on a 2D surface. & A binarisation and contour recognition method. & A low-cost system based on an industrial vision was developed and implemented for the simple programing of the movement path. & [48, 49] \\ \hline The patch-based density forecasting networks (PDFNs) directly forecast crowd density of future frames instead of trajectories of each moving person in the crowd. & & Density Forecasting in Image Space. & Proposed patch-based models, PDFN-S and PDFN-ST, outperformed baselines on all the datasets. PDFN-ST successfully forecasted dynamics of individuals, a small group, and a crowd. The approach cannot always forecast sudden changes in walking directions, especially when they happened in the later frames. & [45] \\ \hline To separate the objects from a set according to their colour. & Pneumatic Robot arm response to applied pressure. & Forec in response to applied pressure. & The proposed robotic arm may be considered for sorting. Servo motors and image processing camera can be used to achieve higher repeatability and accuracy. & [37, 50] \\ \hline An image processing-based method for coal and gauge sorting. Development of a positioning and identification system. & & Threshold segmentation methods. Clustering method. Morphological corrosion and expansion methods. The centre of mass method. & Efficiency is evaluated using the images of coal and gauge, which are randomly picked from the production environment. The average coordinate errors in the x and y directions are 2.735 and 2.72%, and the identification accuracy of coal and gauge samples is 88.3% and 90.0%, respectively, the sum of the time for identification, positioning, and opening the camera for a single sample averaged 0.130 s. & [41, 52] \\ \hline A computer vision-based robotic sorter is capable of simultaneously detecting and sorting objects by their various colours and heights and segments. & A 5 or 6 DOF robotic arm and a camera with the computer vision software detecting various colours and heights and geometries. & Computer Vision methods with the Haar Cascade algorithm. The Camy edge detection algorithm is used for shape identification. & A robotic arm is used for picking and placing objects based on colour and height. In the proposed system, colour and height sorting efficiency is around 99%. Effectiveness, high accuracy and low cost of computer vision with a robotic arm in the sorting process according to color and shape are revealed. & Meeting the real-time requirements and ensuring high precision. Achieved 49.1% mean average precision, processing images in real-time at 35.3 FS on one single Nvidia GTX1080 GPU. & [40, 55] \\ \hline \hline \end{tabular}
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Table 3: Research focused on object recognition in robotics.