# Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
## 2 Significance of Smart Industrial Robots in Manufacturing  
Since the first discussions on smart industrial robots, the developments and achievements in sensor fields have highly grown [17]; similarly, the trends in industrial robot control methods have significantly changed. The achievements in the field of AI are contributing highly to smart industrial robot control trends. The AI usage in robotic systems is strongly becoming one of the main areas of focus as the manufacturing sector requires increased performance, adaptability to product variations, increased safety, and reduced costs, etc. [18]. However, these requirements are neither feasible nor sustainable to be achieved by standard control methods. The methods and techniques of traditional industrial robot control are similar to factories that can be single-purpose [19], meaning that methods that are focused on a single task can be hardly adjustable to different scenarios if adjustable at all. As one of the criteria to succeed is to be flexible to changes, industrial robots need algorithms that can adjust to different scenarios with minor modifications that can be performed by the machine operators. The need for smart industrial robot control methods arises when there is uncertainty in the environment. With the environment in this context, we mainly understand the region in the environment that can be reached by the robot, e.g., the robots' workspace. Typically this workspace scales down to a region of interest or objects of interest to which the robot is manipulating. Nevertheless, there can be different types of uncertainties in the robots' workspace such as randomly placed objects [20], dynamic obstacles [21], and human presence, etc. Usually, such tasks cannot be preprogrammed and the robotic system strictly depends on the information that it receives from the environment and the learned strategies. To avoid collisions, succeed in the given tasks and interact with the environment the usage of additional sensor systems is beneficial, for example, in the field of human-robot collaboration, at least 13 different sensors/devices are used [11]. Additionally, there is also a need to reduce the time spent on programming industrial robots as traditional approaches of handcrafting trajectories [8] is a laborious, complicated and error-prone task. Smart industrial robot control is an important element yet only a fraction of the smart manufacturing ecosystem. In this paper, the focus is concentrated on components, methods and strategies that act as enablers of smart industrial robot control. Therefore, also only the required core functionalities [22; 23] that differ them from traditional industrial robots are investigated and essentially are oriented towards cognitive robotics [24]. The overall concept of smart industrial robot control follows the principle of \"see-think-act\". Even though this principle originally has been addressed in the field of mobile robotics [25], it is also becoming more relevant in the context of smart manufacturing. The diagram illustrated in Figure 1 is built upon this principle and integrates such core functionalities of smart industrial robots as:  
* Perception;
* High-level instruction and context-aware task execution;
* Knowledge acquisition and generalization;
* Adaptive planning;  
Perception is understood as a system that provides the robots with the perceiving, comprehending and reasoning abilities. This includes sensory data processing, representation and interpretation [26]. Within functionality of perception, several key elements can be distinguished that fundamentally differ from the listed methods and strategies in the following sections. These components are mainly associated with the way how knowledge is acquired, generated by different strategies and applied in context-aware task execution. Essentially, the knowledge and database is the core element that enables smart industrial robot control. Acquiring new knowledge and generalizing that knowledge enables one to undertake new tasks based on the history of decisions.