Human-Robot Collaboration (HRC) offers potential for flexible manufacturing, especially in small and medium-sized enterprises, but existing systems often lack adaptability to dynamic environments and human needs. This paper presents a novel, skill-based framework for user-centric, Quality of Service (QoS) based orchestration of HRC workflows. Our three-layer architecture separates non-real-time orchestration and workflow management from real-time, safety-critical execution, coordinated via a user-friendly interface using our Gantt chart-based Robot Collaboration Language. The framework allows users to define QoS goals (e.g., speed, energy efficiency, ergonomics), influencing high-level orchestration and low-level motion planning. This paper details the implementation and demonstrates the framework’s structure. It contributes towards more flexible, efficient, and human-aware robotic systems by providing an integrated approach to adaptive HRC workflow execution.

Domain-specific languages (DSLs) can simplify the programming of human-robot collaboration (HRC) systems for non-experts. However, executing skill-based DSLs for HRC programming requires integrating various production resources such as robots, humans, and sensors. Therefore, domain overlapping requirements must be considered when choosing an appropriate execution approach. This paper identifies and explains key requirements such as flexible interfaces, dynamic capability discovery, task dependency resolution, spatial awareness, continuous monitoring, and user interfaces. Through a comprehensive literature review and gap analysis, we identify trade-offs in execution methodologies. Based on this, we propose an orchestration-based strategy that offers centralized coordination, extendable interface integration, and monitoring capabilities to address current limitations in executing DSLs in HRC applications.

The evolution from Industry 3.0 to 5.0 has marked significant advancements in human-robot collaboration (HRC), with the latest stage emphasizing a human-centric approach that integrates robots as collaborative partners. Despite the potential of HRC, challenges such as communication barriers, safety concerns, and the complexity of dynamic interactions persist, potentially hindering optimal collaboration. To address these challenges, this research introduces a modeling approach that incorporates a detailed kinematic description of human movements within the workflow. By mapping human movements and calculating robot paths proactively, the proposed method aims to enhance interaction safety and efficiency, transitioning from reactive to proactive path planning.

The collaboration between humans and robots offers opportunities that are not achievable separately. However, a strategy for collaboration is required, as they differ in their behavior and perception. This work-in-progress paper outlines future research activities in this field, focusing on the integration of collision avoidance into robot movements. Therefore, trajectory planning is formulated as an optimization problem, which considers the human being but also finds the optimal trajectory according to a certain criterion. This reduces the cycle time or the energy required to drive the robot. The description as a multibody system enables a digital twin allowing planning in advance, but also the possibility of real-time control. This creates the basis for trust in the collaboration.