Robots handle uncertainty and errors in sensor data through a combination of probabilistic modeling, redundancy, and adaptive algorithms. Sensors like cameras, lidar, or accelerometers often produce noisy or incomplete data due to environmental factors (e.g., lighting changes, physical interference) or hardware limitations. To address this, robots use probabilistic techniques such as Bayesian filters (e.g., Kalman filters or particle filters) to estimate their state (like position or orientation) by combining sensor measurements with predictions from motion models. For example, a robot navigating a room might use a Kalman filter to fuse lidar distance measurements with wheel encoder data, continuously updating its belief about its location while accounting for measurement noise.
Another approach involves redundancy through sensor fusion. By integrating data from multiple sensors, robots cross-validate readings to reduce reliance on any single error-prone source. For instance, a drone might combine GPS, inertial measurement units (IMUs), and visual odometry to maintain stable flight. If GPS signals drop out (e.g., in a tunnel), the drone can rely on IMU and camera data to estimate its position. Developers often implement sensor fusion using frameworks like the Robot Operating System (ROS), which provides tools for synchronizing and processing data streams. Redundancy also extends to software: algorithms like SLAM (Simultaneous Localization and Mapping) allow robots to build maps while tracking their position, iteratively refining both as new data arrives.
Finally, robots employ error detection and recovery strategies. Outlier rejection algorithms (e.g., RANSAC) identify and discard inconsistent sensor readings, such as a lidar point cloud including phantom objects from reflections. Adaptive control systems, like PID controllers with dynamic tuning, adjust robot behavior in real time when sensor errors cause deviations from expected outcomes. For example, a self-driving car might detect a mismatch between wheel speed sensors and camera-based lane tracking, triggering a recalibration routine. Machine learning techniques, such as neural networks trained on historical sensor data, can also predict and correct systematic errors. These layers of error handling ensure robots operate reliably despite imperfect data, though developers must carefully balance computational cost and responsiveness for real-time systems.