Robots detect anomalies and take corrective actions by combining sensor data, predefined rules, and adaptive algorithms. They continuously monitor their environment and internal states using sensors like cameras, lidar, or accelerometers. When sensor readings deviate from expected patterns—such as unexpected obstacles, irregular motor temperatures, or positioning errors—the system flags these as anomalies. Algorithms then analyze the severity and context of the issue. For example, a robot arm might detect a sudden spike in torque, signaling a collision, while a self-driving car could identify a pedestrian stepping into its path. These systems prioritize real-time processing to minimize response delays.
Once an anomaly is detected, robots use predefined logic or machine learning models to decide corrective actions. Industrial robots often rely on rule-based systems: if a motor overheats, they might pause operations and trigger a cooling routine. Autonomous drones, on the other hand, might switch to redundant sensors or reroute their flight path if GPS signals are lost. More advanced systems, like those using reinforcement learning, adapt dynamically. For instance, a warehouse robot encountering a fallen object might recalculate its path using updated spatial maps. The corrective step depends on the robot’s design constraints—safety-critical systems prioritize stopping, while others might attempt recovery without human intervention.
Post-action, robots often log anomalies and refine their behavior. A common approach is integrating feedback loops: after adjusting to an error, the system updates internal models to handle similar issues better. For example, a service robot that misidentifies an object might retrain its vision model with new data. Simpler systems, like those using PID controllers, continuously adjust parameters (e.g., motor speed) to reduce positional drift. Developers typically implement these workflows using frameworks like ROS (Robot Operating System), which streamline sensor integration, decision-making, and actuator control. Testing edge cases—like simulating sensor failures—ensures robustness, allowing robots to handle real-world unpredictability.