A novel concept for a configurable robotic system is presented, addressing the complexities inherent in aircraft production processes. The proposed architecture encompasses three fundamental components: (1) a robot abstraction layer that enables integration of heterogeneous robot vendors; (2) a behavior framework based on a reconfigurable industrial control system, eliminating the need for reprogramming when adapting to new tasks, and (3) a standardized service interface that enables uniform supervision and control of robotic services. The implementation leverages standardized protocols, specifically SRCI for robot abstraction and MTP for providing service interfaces. The system’s adaptability is enhanced by a reconfigurable industrial control system.

Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive underlying personal data. Therefore, local processing is necessary and the limited energy budget in such systems can be addressed by Inertial Measurement Units (IMU) instead of common camera sensing. The central trade-off between accuracy and efficient use of hardware resources is rarely discussed in research. We address this trade-off by a simulative Design Space Exploration (DSE) of a varying quantity and positioning of IMU -sensors. First, we generate IMU-data from a publicly available body model dataset for different sensor configurations and train a deep learning model with this data. Additionally, we propose a combined metric to assess the accuracy-resource trade-off. We used the DSE as a tool to evaluate sensor configurations and identify beneficial ones for a specific use case. Exemplary, for a system with equal importance of accuracy and resources, we identify an optimal sensor configuration of 4 sensors with a mesh error of 6.03 cm, increasing the accuracy by 32.7 % and reducing the hardware effort by two sensors compared to state of the art. Our work can be used to design health applications with well-suited sensor positioning and attention to data privacy and resource-awareness.