Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory-generated data to introduce cost-effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.

In this paper we present the actual stage in the development of a mobile robot assistant that is going to be capable of safe cooperation with humans in a populated environment. The service robot is intended to assist users in biological and pharmaceutical laboratories by carrying out routine jobs such as filling and transportation of microplates. Relevant safety requirements are outlined and a safety concept is devised, which consists of various sensor systems such as laser scanners, thermographic components and artificial skin. An overview of the design of the mobile platform with a robotic arm is provided. Moreover, the approaches to object recognition and intuitive multimodal human-machine interaction using speech and touchpad input are described. All aspects are regarded concerning safety since the robot and humans share a common environment and interact closely.

Capable mobile manipulators require sophisticated sensing and environment perception. An important task is the precise localization of objects to be handled. In order to be able to deal with a wide variety of applications, sensor systems need to be able to work with glossy, shiny or transparent objects. Here we present an approach to that problem based on analyzing specular reflections using a custom build, head mounted light-field camera. We derive a suitable algorithm including highlight feature detection as well as highlight depth estimation and will present exemplary results.

In this paper we present a service oriented architecture for distributed acquisition and fusion of sensor data. The architecture is designed to fit the special requirements of service and inspection robots. The requirements are motivated by an autonomous inspection system for sewer channels. The paper gives an overview of the underlying concepts of design including time-synchronization, data buffering and notification schemas. Two examples are given to illustrate the application in the context of sewer inspection.