How do robots detect and correct errors during task execution?

Robots detect and correct errors during task execution through a combination of sensors, algorithms, and feedback loops. Sensors like cameras, LiDAR, torque sensors, or encoders provide real-time data about the robot’s environment and internal state. Algorithms process this data to compare expected outcomes against actual results. For example, a robotic arm assembling parts might use force sensors to detect if a component isn’t fitting correctly, while a self-driving car uses cameras to identify unexpected obstacles. When discrepancies arise, the system flags an error and triggers corrective actions.

Error correction typically involves predefined recovery strategies or adaptive algorithms. Preprogrammed responses might include retrying a motion, adjusting grip force, or recalculating a path. For more complex scenarios, robots use machine learning models or control systems like PID (Proportional-Integral-Derivative) controllers to adapt dynamically. A drone navigating wind gusts, for instance, might adjust propeller speeds based on IMU (Inertial Measurement Unit) data to stabilize flight. Industrial robots often employ error-handling routines—like pausing operations and alerting operators—when anomalies exceed safe thresholds. These systems prioritize minimizing downtime while ensuring safety.

Real-world examples highlight how these components work together. In manufacturing, collaborative robots (cobots) use vision systems to detect misaligned parts and reposition them using inverse kinematics calculations. Autonomous warehouse robots scan barcodes to verify item locations and reroute if a package is misplaced. Some systems also log errors to improve future performance; a robot vacuum might map areas where it frequently gets stuck and avoid them. For developers, implementing robust error handling requires testing edge cases, tuning sensor thresholds, and designing fallback protocols. Frameworks like ROS (Robot Operating System) provide tools for integrating sensors, motion planning, and fault detection into cohesive workflows, ensuring errors are managed systematically.