# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 1 Introduction  
Future manufacturing environments are characterized by workplaces in which human workers and robots work closely together [1]. The industrial assembly has a key impact on productivity and profitability of companies [2] and is to date still largely operated fully manually [3, 4]. In view of increasing flexibility requirements due to individualized products, dynamically changing market demands, and shorter product life cycles [5], appropriate solutions to ensure companies' efficiency and profitability must be implemented [6]. When effectively combining the strengths of humans and robots, productivity for small to medium batch sizes can be improved compared to purely manual assembly [7], while maintaining a higher degree of flexibility compared to fully automated systems [8]. Consequently, industrial assembly represents one of the main fields for the transformation towards hybrid workplaces in human-robot collaboration (HRC) [9, 10]. From a human-centric perspective, which belongs to the key concepts for the evolution towards Industry 5.0 [11], particularly Operator 5.0 [12], employees working in such hybrid assembly workplaces should be supported in a meaningful way, with the aim of reducing monotonous, repetitive, or ergonomically unfavorable tasks to a minimum, and relieving the employees' cognitive stress [13]. Therefore, the combination of information-based assembly assistance systems, which are already widely used in industrial manual assembly, with collaborative robots as a physical support system offers a promising solution approach for the design of hybrid assembly workplaces [14, 15, 16]. The aforementioned envisioned potentials of collaborative robots are reflected in a strongly growing market volume for collaborative robots [17]. The dominant industrial application is the field of assembly, which accounts for a share of more than 70% of collaborative robot applications [18] and is also the most studied area in HRC research [13]. The main tasks of collaborative robots are gripping, mounting or joining, quality control, pick and place, packing and palletizing, and machine loading [18; 19]. Driving sectors are especially the automotive and electronics industries [13; 18; 20]. However, smaller companies in particular rarely use collaborative robots to date [21]. Moreover, in most industrial applications of collaborative robots, human workers and robots work in separate workspaces and do not collaborate closely together yet [18; 22; 23]. Here, two major topic areas were identified as challenges explaining this lack of application in which humans and robots share the same collaborative workspace, namely (1) safety aspects and (2) planning aspects [8; 24]. This article addresses the planning challenge of task allocation, i.e., an effective division of labor between humans and robots. The allocation of tasks to humans or robots can be performed either statically, i.e., prior to task execution, or dynamically, i.e., in response to changes during the task execution [25; 26]. In the literature, various methods for both static and dynamic task allocation have been proposed (see Section 1.2). However, on the one hand, apart from validating the principal functionality of the respective methods or evaluating them in simulation studies, no comprehensive evaluation in an experimental test setup has yet been conducted that systematically examines and empirically assesses the advantages and disadvantages of both approaches to task allocation. On the other hand, related approaches do not focus on an intuitive and integrated creation of the HRC assembly processes. To overcome this research gap, this work presents on the one hand the development of an approach for dynamic task allocation as well as its implementation into a robot no-code programming framework, including the visualization of worker instructions and the assembly progress. On the other hand, it focuses on a systematic evaluation of the dynamic task allocation approach in comparison with static task allocation in a laboratory user study.