Automated and Continuous Risk Assessment for ROS-Based Software-Defined Robotic Systems

Abstract

In modern and complex production systems, the focus is shifted toward the software part. Software-Defined Manufacturing (SDM) and Cyber-Physical Production Systems (CPPS) characterize this trend. SDM and CPPS enable the concept of adaptive, flexible, and self-configuring production systems. These software-intensive robotic systems are safety-critical because they usually are applied in the same environments as human workers. Therefore they require a continuous risk assessment. The uploading of a new software to the system can change its behavior drastically and therefore, the risk assessment needs to be redone. Key enabling technologies are digital twins, advanced and hybrid risk models, and Model-to-Model (M2M) transformation methods. In this paper, we introduce a new approach to the automated and continuous risk assessment based on Robot Operating System (ROS) code of a software-defined robotic system. The approach pipelines four key elements: (i) a logger that logs the data of the digital twin, (ii) an adder algorithm that creates risk annotated code based on the given ROS code, the output of the logger, and the hardware description including risk data of robot parts, (iii) an M2M transformation algorithm that automatically generates hybrid risk models from risk-annotated code, and (iv) OpenPRA solvers for numerical evaluation of the generated hybrid risk models.