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In the truck industry, manual final assembly and material handling processes can be complex and crowded, making their automation difficult using traditional industrial robots. Collaborative robot systems, on the other hand, offer a flexible and user-friendly alternative that can free up human workers from repetitive and non-ergonomic tasks, allowing them to focus on more value-adding operations. Despite the considerable efforts made by researchers and within the industry to promote collaborative robots, they are often underused and their use is limited to handling simple automation tasks without perimeter fences.

The aim of this thesis is to enhance our understanding of human-robot collaboration and the challenges faced by complex industries when implementing intelligent and collaborative automation. The goal is to create a sustainable workplace where robots and humans can work together safely and efficiently in a flexible environment.

Through several industrial use cases, two demonstration setups were developed to identify a set of industrial challenges and requirements. These requirements include safe, efficient, and intuitive interactions, as well as deliberative and robust control, reliable communication, variant handling, and an efficient engineering process. However, the most critical requirement is ensuring the safety of both machines and humans. It was found that current safety standards trade safety for efficiency, flexibility, and cost, which limits the implementation of intelligent and adaptive collaborative systems in complex applications.

To address these issues, a new safety approach called deliberative safety is proposed, which allows for switching between different safety measures depending on whether flexibility or efficiency is required to attain production goals. A taxonomy is proposed to better support the design of deliberative safety, along with five safety measures ranging from currently existing measures like perimeter safety to planned and active safety. These measures can enable intelligent human-robot collaboration.

However, incorporating intelligence and using the deliberative safety concept may introduce new types of risks, which necessitates the development of new risk assessment and risk reduction methods. To address this, a risk assessment method based on reliability theory is combined with a novel method based on system theory to identify system requirements in the early stages of development and to identify risky scenarios and related risk reduction methods.

The findings of this research will be beneficial to manufacturing industries seeking to use intelligent and collaborative automation to increase flexibility when automating. Additionally, they will provide valuable inputs for the development of related safety standards and risk assessment procedures.