A Digital Twin Enabled Runtime Analysis and Mitigation for Autonomous Robots Under Uncertainties

Autonomous mobile robots are increasingly deployed in various application domains, often operating in environments with uncertain conditions. Such robots rely on the state and performance assessments at runtime to autonomously control the robot functionality. However, uncertainty can significantly impact the robot sensors and actuators making it challenging to assess the robot state and quantify its performance reliably. This paper proposes a digital twin (DT) asset for the runtime estimation and validation of state and performance for a mobile autonomous robot "Turtlebot3" (TB3) operating under uncertainties, namely Lidar sensor obstruction and unknown floor friction and density. The proposed DT setup enables real-time state synthesis post-uncertainty, so that to estimate the performance and validate it using TeSSLa monitors, and compute mitigation actions. To maintain the robot autonomy, our DT intervenes only when an uncertainty is identified. The experimental results demonstrate that our DT enables to eliminate 70% of the related uncertainty while it mostly maintains the real-time synchronization with the physical TB3 robot operating a frequency of 0.2s.