A Time-Series Data Generation Tool for Risk Assessment of Robotic Applications

Robotic systems increasingly rely on artificial intelligence (AI) to enhance their capabilities in performing complex tasks across various domains.The development and evaluation of AI systems usually require high-quality datasets.In addition to normal datasets, faulty datasets are critical for enabling anomaly detection and failure prevention, which are essential for ensuring the safety and reliability of safety-critical robotic applications.However, faults are rare in real-world environments.Although fault injection techniques allow for the manual injection of configurable faults, deploying such methods directly in real-world settings is rather risky.As such, it is important to develop a data generation tool which is low-cost, safe, and efficient.To address this, we developed a time-series data generation tool for the risk assessment of robotic applications.In this paper, we used Robot Operating System (ROS) Quigley et al. ( 2009) as the middleware.This ROS-based simulation tool integrates three key modules: (1) a Gazebobased scene generator that can configure different working scenarios (e.g., drilling and welding) by adjusting endeffectors, workpieces, and hand positions; (2) an online fault injector that can introduce faults into robotic systems with configurable parameters; and (3) a risk monitor that records faulty data and safety violations in real time by measuring the distance between hands and end-effectors.Proposed tool facilitates the generation of time-series fault data and helps identify faults that may pose risks in human-robot collaboration scenarios.Additionally, the proposed simulation tool enables fast and safe deployment for other robot-related research areas, e.g., deep learning-based anomaly detection, failure prediction, and risk assessment.