# Seamless Human-Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices
## 1 Introduction  
Applications of human-robot collaboration (HRC) have already made their way into industrial practice, hence the Industry 4.0 revolution [1], allowing human operators and robots to coexist in the same space [2, 3]. HRC applications offer a number of advantages over non-collaborative ones, as they allow to combine benefits of both actors, namely humans and robots. Robots can offer high accuracy and repeatability, can lift heavy weights, and can reduce the cycle time compared to a merely manual production. On the other hand, humans show high levels of cognition and dexterity. Safety concerns arise though in HRC applications, as robots have to share space or tasks with humans and without proper application human safety may be violated [4]. The driving force for such investigation has been the pursuit of manufacturing flexibility which allows companies to efficiently align their production with the demand for low volume, highly customized products [5]. Robots have proven very capable of being reconfigured and repurposed even between cycles within the same shift, however, their productivity potential becomes hindered when humans are present in their workspace. Coexistence in a workplace does not automatically result in productivity gains as seamless collaboration requires intelligence and autonomy on both sides of the collaborating entities. Speaking of intelligence, researchers have been utilizing AI in various research fields, such as health care [6, 7], education [8], transportation [9], engineering [10], and industry. Specifically, for industry, AI has been utilized in human -robot collaborative applications, in topics such as cognition to enable autonomy [4], task planning and allocation of tasks among humans and robots [11], and operator support [12]. Humans can perform the most delicate and dexterous processes employing their intelligence and senses. Robots on the other hand are powerful, fast, and accurate machineswith limited interaction capabilities [13]. Efficient collaboration calls for a reduction of the interfaces required to exchange information between them and this can be achieved by increasing the perception and cognition capabilities of robots to emulate the ones of humans [14]. However, in order to achieve conditions that are similar to the case of human-to-human collaboration, the triggering of alternative actions on the robot side is merely the first step. A human counterpart would:  
* reason upon the evolution of a task by combining observations of both his/her co-worker (movement, posture, intention) as well as the working environment (positioning of parts, tools, machine state indicators, etc.). * Increase the efficiency of collaboration by adapting his/her actions to both directly communicated requirements as well as indirect observations and intuition. A case of collaborative behavior would include the change of the assembly task sequence initiated arbitrarily by one operator and followed by the compliance of the second, without verbal communication. The instinctive adaptation of holding height during the co manipulation of a large part to better distribute the weight according to the operator's physiology is another example. Transferring this notion to the case of HRC (Figure 1), a high payload collaborative robot would need to continuously assess the workflow execution status using information from equipment controllers, process sensors, and direct human input as well as data from wearable devices, which have been widely used in other sectors such as health care [15]. All data can be hosted in a Shopfloor Digital Representation (Digital Twin [16; 17]) of the system and made available to all cognitive functions to achieve the following:  
* Detection of parts or tools to be manipulated by human operators and localization of their position. Although this field has been fairly researched in the past using both RGB and 3D sensors [18], the use of data coming from wearable devices is one of the novelties of this work.