Agile Interaction with Industrial Robots

Human-robot interaction (HRI) has gained significant attention for its potential to combine human adaptability and problem-solving skills with robotic precision and efficiency in labor-intensive tasks. Recent advancements in robotics, Artificial Intelligence (AI), and Digital Twin (DT) technologies enhance the efficiency and safety of HRI in industrial settings. However, transitioning from manual processes to HRI presents several challenges, such as capturing all knowledge of skilled operators, coping with high variability, and ensuring safety in shared workspaces.

To address these challenges, this dissertation proposes a systematic solution focusing on three key aspects: 1) Agility: this aspect involves capturing and digitalizing operators’ expertise in manual processes, exemplified by the collaboration with LEGO, where a Bill of Process (BOP) is leveraged to combine this expertise with existing data from the company’s Product Lifecycle Management (PLM) database. Through this aspect, the results allow for addressing high variability in product types, component sizes and weights, and complex assembly sequences, and integrating with other platforms (e.g., the DT platform) through export features enabled by PLM. 2) Interaction: a multi-robot system, comprising a collaborative robot (cobot) and an industrial robot (IR), is implemented to assist operators in completing assembly operations by providing distinct support: the cobot handles repetitive operations, while the IR manages tasks requiring high payload capacity. This HRI setup aims to achieve different levels of interaction scenarios between operators and robots, enabling flexible use of industrial robots beyond traditional constraints such as strictly defined workspaces and restricted interaction scenarios. 3) Safety: a workspace monitoring system, incorporating conventional and supportive safety measures, is conceptualized to ensure the safety of human operators and all components in the workcell. The system focuses on implementing two collaborative modes provided by industry standards, i) safety-rated monitored stop and ii) speed and separation monitoring, by exploring various sensor combinations integrated with AI and DT technologies. This workspace monitoring system encourages switching along the spectrum of safety solutions from conventional industry standards to more experimental safety measures.

This study aims to transition existing manual assembly processes to HRI setups by digitalizing accumulated knowledge, investigating interaction scenarios, and exploring safety concerns. By combining strengths from PLM, DT, and sensor technology, this study efficiently improves agility, interaction, and safety of HRI settings throughout the entire manufacturing workflow.