# Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
## 6 Challenges and Open Issues  
### Smart Industrial Robot Deployment and Control  
Diverse conditions on the manufacturing floor, lack of skilled personnel, functional correctness and reliability--these are only some of the challenges but definitely the core ones that are holding back the wider applicability of smart industrial robots. Dedicated systems adjusted for distinct environmental conditions are no longer sustainable in smart manufacturing. Diverse conditions, especially in the context of CV-based systems, such as lighting conditions, a field of view distance and a variety of object types can considerably decrease the precision of the system. The adaption to the changes in the environment can require modifications in software or hardware replacement. The respective component change should have no (or only minimal) impact on other components within the system; therefore, the characteristic of modularity [104] is one of the prerequisites of the smart industrial robot system.  
The automation and deployment of industrial robot systems replace tasks that are repetitive, lack meaningfulness [105], poses a high risk of injuries [106] and also low-skill tasks that traditionally required human intelligence and dexterity. In the process of automation, more creative job openings are introduced; however, there is a lack of skilled personnel to operate and maintain advanced technologies that also includes smart industrial robot systems. In fact, it is estimated that this skill gap could leave 2.4 million positions unfilled between 2018 and 2028 in the United States alone [107]. The process behind a smart robotic system can be remarkably complex, even for skilled operators. The maintainability of the system and adaptability to other specified goals should be doable without any deep specific knowledge of the underlying target technology.  
The ability of the smart industrial robot system to accomplish services as specified and maintain its level of performance can become a major issue within AI-based robotic systems and especially when adapting the system to new environments [108]. In manufacturing, these systems are put into continuous operation and it can be a mission-critical production environment with short maintenance times. AI must not compromise productivity through an unreliable or imprecise operation that requires regular human intervention or leads to failures. In smart industrial robot systems, this concern can be highly connected to an issue that is commonly referred to as: \"reality gap\" [109]. Even though reliable operation can be achieved in simulations, the trained models can perform unreliably when moved to a real environment. As real-world maintenance times can be very short, a more reliable sim-to-real transfer is required.  
Even though in some situations robotic grasping is reliable and functions correctly, it still remains a fundamental challenge in industrial robotics. Several methods on the grasping of unknown and geometrically complex objects are proposed; however, a precise placing of such objects has not been widely addressed. In some scenarios, the grasping process can include several additional operations, which is the case of tangled objects, where proposed methods only partially address this issue by mostly avoiding grasping such kinds of objects.