Simulation-Based Testing of Autonomous Robotic Systems for Surgical Applications

Autonomous surgery involves surgical tasks performed by a robot with minimal or no human involvement. Thanks to its precise automation, surgical robotics offers significant benefits in enhancing the consistency, safety, and quality of procedures, driving its growing popularity. However, ensuring the safety of autonomous surgical robotic systems remains a significant challenge. To address this, we propose a simulation-based validation method to detect potential safety issues in the software of surgical robotic systems, complemented by a digital twin to estimate the gap between simulation and reality. The validation framework consists of a test case generator and a monitor for validating properties and evaluating the performance of the robotic system during test execution. Using a robotic arm for needle insertion as a case study, we present a systematic test case generation method that ensures effective coverage measurement for a three-dimensional, irregular model. Since no simulation can perfectly replicate reality due to differences in sensing and actuation, the digital twin bridges the gap between simulation and the physical robotic arm. This integration enables us to assess the discrepancy between virtual simulations and real-world operations by verifying whether the data from the simulation accurately predicts real-world outcomes. Through extensive experimentation, we identified several flaws in the robotic software. Co-simulation within the digital twin framework has highlighted these discrepancies that should be considered.