Robots handle incomplete or noisy sensor data through a combination of probabilistic modeling, sensor fusion, and redundancy. These approaches allow them to make informed decisions even when inputs are unreliable. By estimating uncertainty, combining multiple data sources, and designing fallback mechanisms, robots maintain functionality despite imperfect sensor conditions. This adaptability is critical in real-world environments where sensors can fail, suffer interference, or provide ambiguous readings.
To address incomplete data, robots often use probabilistic models to fill gaps or predict missing values. For example, a robot navigating indoors might use a particle filter to estimate its location when GPS signals are unavailable. The filter combines motion sensor data (like wheel encoders) with a pre-built map to generate probable positions, updating predictions as new data arrives. Redundancy is another key strategy: robots may use multiple sensors of the same type (e.g., dual cameras) to compensate for failures. In ROS (Robot Operating System), tools like the message_filters package synchronize data streams from redundant sensors, ensuring at least one valid input is available. Autonomous drones demonstrate this by switching to inertial measurement units (IMUs) when GPS signals drop, using accelerometer and gyroscope data to maintain stable flight.
For noisy data, robots apply filtering and sensor fusion. A Kalman filter is widely used to reduce noise in time-series data, such as smoothing erratic lidar distance measurements by weighting sensor inputs against system dynamics. Sensor fusion combines complementary sensors: self-driving cars merge camera images (prone to lighting noise) with lidar point clouds (accurate but sparse) to create robust object detection. Machine learning techniques like autoencoders can denoise images by training on clean/noisy data pairs, while recurrent neural networks (RNNs) handle sequential sensor data (e.g., cleaning erratic microphone input for voice commands). These methods enable robots to distinguish meaningful signals from noise without overreacting to transient errors.