Robots use sensors to perceive their environment, process data, and make decisions to navigate or perform tasks. Sensors act as the robot’s eyes, ears, and touch, converting physical inputs into digital signals that algorithms interpret. For example, a robot vacuum uses infrared sensors to detect walls, while an industrial arm relies on torque sensors to adjust grip strength. These inputs enable robots to operate autonomously, avoid obstacles, and interact with objects precisely.
In navigation, robots combine sensors like LiDAR, cameras, and ultrasonic rangefinders to build spatial awareness. LiDAR creates 3D maps by measuring laser reflection times, helping robots like self-driving cars detect road boundaries. Cameras capture visual data for object recognition—a drone might use this to avoid trees. Ultrasonic sensors detect nearby obstacles, often used in warehouse robots to prevent collisions. Inertial Measurement Units (IMUs) track acceleration and rotation, aiding drones or rovers in maintaining stability. These systems often use Simultaneous Localization and Mapping (SLAM) algorithms to navigate unknown environments by correlating sensor data in real time.
For task execution, sensors provide feedback to ensure accuracy. Force-torque sensors in robotic arms measure pressure during assembly tasks, allowing precise screw tightening. Tactile sensors on grippers detect slip or texture, enabling delicate object handling—like a robot sorting packages without crushing them. Vision systems with depth cameras identify object positions, guiding robots to pick items from bins. In manufacturing, proximity sensors verify part alignment before welding. Sensor fusion—combining data from multiple sources—reduces errors; for instance, a robot might cross-check camera images with LiDAR scans to confirm an object’s location. Developers must design systems to handle sensor noise, latency, and redundancy to ensure reliability in dynamic environments.