Example Concept: Sensor dropout refers to the temporary loss of data from one or more sensors, which can disrupt the robot's ability to perceive its environment accurately. To handle this, sensor fusion algorithms, such as Kalman filters or particle filters, are used to combine data from multiple sensors, allowing the system to estimate missing information and maintain reliable operation. These algorithms can weigh the reliability of each sensor input and adjust the robot's behavior accordingly, ensuring continued functionality even when some data sources are unavailable.