Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator

Industrial cyber-physical systems (ICPS) are becoming more complex due to increasing behavioral and structural complexity. This increases the likelihood of faults, errors, and failures. This can lead to economic losses and even hazardous events. Fault injection is an efficient method to estimate the potential risk of safety-critical ICPS. In this paper, we propose a new fault injection-based risk analysis method for Robot Operating System (ROS) and demonstrate its applicability with a robot manipulator case study. We conducted extensive fault injection experiments using a pick-and-place task. We injected two types of sensor signal faults: bias and noise. First, fault injections were implemented on a ROS/Gazebo model of the manipulator with randomly selected fault parameters such as fault type, location, magnitude, and duration. The experiments helped to identify potential failure scenarios and to find critical fault locations. The most important factor contributing to system failures was the operational phase during which the faults were injected. We then tested our fault injection method on a real Franka Emika Panda collaborative manipulator to validate the effectiveness of the proposed ROS-based fault injection method. We observed that the digital model showed similar behavior to the real manipulator.