Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise*
Andrea Ruo, Lorenzo Sabattini, Lorenzo Sabattini · 2025
In recent years, an increasing number of service robots have been deployed in human environments. The growing complexity of modern robotic systems and the environments in which they operate necessitates careful consideration of safety, increasing the challenges associated with socially responsible navigation. Moreover, localization measurements are often affected by random noise, which can lead to unsafe behavior if not addressed in the control design. Therefore, it is crucial to ensure robustness against measurement noise. This paper addresses navigation challenges by presenting a robust CBF-based STL motion planning algorithm, integrated with an unscented Kalman filter. This methodology mitigates the effects of state disturbances and measurement noise, ensuring task completion at any time within a specified time interval for dynamic systems subject to nonlinear velocity constraints and collision avoidance. A simulation study is conducted to validate the methodology, demonstrating its ability to ensure safety.