# Advanced Applications of Industrial Robotics: New Trends and Possibilities
## 3 Recent Achievements in Industrial Robotics Classified according to Implementation Area
### Object Recognition  
Object recognition is a typical issue in industrial robotics applications, such as sorting, packaging, grouping, pick and place, and assembling (Table 3). The appropriate recognition method and equipment selection mainly depends on the given task, object type, and the number of recognisable parameters. If there are a small number of parameters, simpler sensing technologies based on typical approaches (geometry measuring, weighing, material properties' evaluation) can be implemented. Alternatively, if there are a significant number of recognisable parameters, photo or video analysis is preferred. Required information in two- or three-dimensional form from image or video can be extracted using computer vision techniques such as object localisation and recognition. Various techniques of vision-based object recognition have been developed, such as appearance-, model-, template-, and region-based approaches. Most vision recognition methods are based on deep learning [34] and other machine learning methods. In a previous study [35], a lightweight Franka Emika Panda, cobot with seven degrees of freedom and a Realsense D435 RGB-D camera, mounted on an end effector, was used to extend the default robots' function. Instead of using a large dataset-based machine learning technique, the authors proposed a method to program the robot from a single demonstration. This robotic system can detect various objects, regardless of their position and orientation, achieving an average success rate of more than 90% in less than 5 min of training time, using an Ubuntu 16.04 server running on an Intel(R) Core(TM) i5-2400 CPU (3.10 GHz) and an NVIDIA Titan X GPU. Another approach for grasping randomly placed objects was presented in [36]. The authors proposed a set of performance metrics and compared four robotic systems for bin picking, and took first place in the Amazon Robotics Challenge 2017. The survey results show that the most promising solutions for such a task are RGB-D sensors and CNN-based algorithms for object recognition, and a combination of suction-based and typical two-finger grippers for grasping different objects (vacuum grippers for a stiff object with large and smooth surface areas, and two-finger grippers for air-permanent items). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Interaction** & **Approach** & **Solution** & **Ref.** \\ \hline \multirow{3}{*}{Fustration} & \multirow{3}{*}{Close cooperative work} & \multirow{3}{*}{Controlled coordination} & Sense of control of frustration, affective computing. & [29] \\  & & & & Affective computing. \\ \multirow{3}{*}{Emotion recognition} & \multirow{3}{*}{By collecting different kinds of data.} & Discrete models describing emotions used, facial expression analysis, camera positioning. & Empowering robots to observe, interpret and express emotions. Endow robots with emotional intelligence. & [30] \\  & & & & The dynamic involvement of the mirror neuron systems (MNS) and the theory of mind Tok/mentialsing network during action observation. & [31] \\ \multirow{3}{*}{Verbal and non-verbal communication} & \multirow{3}{*}{Interactive communication.} & \multirow{3}{*}{Symbol grounding} & Composition of grounded semantics, online negotiation of meaning, affective interaction and closed-loop affective dialogue, mixed speech-motor planning, massive acquisition of data-driven models for human-robot communication through crowd-sourced online games, real-time exploitation of online information and services for enhanced human-robot communication. & [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interfering aspects in human–robot interaction. Similar localisation and sorting tasks appear in the food and automotive industries, and in almost every production unit. In [37], an experimental method was proposed using a pneumatic robot arm for separation of objects from a set according to their colour. If the colour of the workpiece is recognisable, it is selected with the help of a robotic arm.