The ‘Sensory System’ Sensors for Robot-Environment Interaction

Robot sensors are the “sensory system” that enables robots to interact with their environment. They are responsible for collecting information about internal states and external environments, forming the core foundation for robots to achieve autonomous decision-making, precise operations, and environmental adaptability.

Application Scenario Examples

• Industrial Robots: Force control assembly (torque sensors) + Visual positioning (cameras)

• Service Robots: LiDAR navigation + Microphone voice interaction + TOF obstacle avoidance

• Agricultural Robots: Multispectral cameras (crop health) + GPS (automatic path planning)

• Space Robots: Radiation-resistant sensors + Stereovision (Mars rover rock analysis)

Cutting-edge Trends

1. Bionic Sensors: Mimicking biological senses (e.g., insect compound eye vision, bat ultrasonic perception).

2. Flexible/Wearable Sensing: Human-machine interaction interfaces for exoskeletons and rehabilitation robots.

3. AI-driven Smart Sensing: Running neural networks directly on the sensor side (e.g., pulse neural networks for event cameras).

4. Multimodal Fusion Upgrades: Cross-modal learning (e.g., jointly recognizing object attributes from vision + sound).

5. Nanosensors: Medical micro-nano robots achieving cellular-level operations.

Key Considerations When Choosing Sensors

Factor	Description	Example Scenario Requirements
Accuracy/Resolution	Data accuracy and detail level	Surgical robots require sub-millimeter precision
Range	Effective working range of the sensor	Drones need 5-20 meters detection for obstacle avoidance
Response Speed	Data update frequency (Hz)	High-speed sorting robots require millisecond response
Environmental Adaptability	Resistance to light, dust, water, and electromagnetic interference	Mining robots need explosion-proof and waterproof designs
Power Consumption and Size	Mobile robots are limited by battery and space	Portable robots require micro low-power designs
Cost	A critical factor for commercial deployment	Consumer-grade products strictly control sensor costs
Data Processing Complexity	Whether a dedicated processor or complex algorithms are needed	Edge computing devices require lightweight models

Core Classifications (by Function)

1. Internal State Sensors (Body Perception)

• Encoders (Rotary/Linear): Measure joint angles, motor speeds, and wheel rotations (e.g., incremental/absolute encoders).

• Inertial Measurement Units: Combine gyroscopes (angular velocity), accelerometers (linear acceleration), and magnetometers (direction) to provide attitude and motion state information (easily affected by noise, often requiring fusion algorithms).

• Force/Torque Sensors: Installed at joints or end effectors to measure contact forces (e.g., assembly, polishing).

• Current Sensors: Indirectly estimate output torque through motor current.

External Environment Sensors (External Perception)

2.1 Proximity/Distance Sensors:

2.2 Visual Sensors:

• Ultrasonic sensors (low cost, poor interference resistance)

• Infrared sensors (short-range obstacle avoidance)

• LiDAR (high-precision 2D/3D ranging, core of SLAM)

• Millimeter-wave radar (resistant to harsh weather, accurate speed measurement)

• Monocular/Binocular/RGB-D cameras (object recognition, face detection, navigation, 3D reconstruction)

• Thermal imagers (night vision, temperature detection)

• Event cameras (respond to pixel-level brightness changes, high-speed low-latency)

2.3 Tactile Sensors:

• Array pressure sensors (mimicking skin tactile for dexterous grasping)

• Flexible tactile sensors (curved surface fitting)

2.4 Sound Sensors: Microphone arrays (voice interaction, sound source localization, abnormal sound detection).

2.5 Environmental Sensors: Temperature, humidity, gas, and light sensors (environment monitoring robots).

Key Sensors Explained

1. LiDAR

• Principle: Emit laser beams and measure reflection time to calculate distance.

• Uses: Positioning for autonomous vehicles, mapping for vacuum cleaners, obstacle avoidance for drones.

• Trends: Solid-state LiDAR (reducing costs), FMCW technology (stronger interference resistance).

Visual Sensors (Cameras)

• Core Algorithm Dependency: Requires integration with OpenCV, deep learning (e.g., YOLO) for image processing.

• RGB-D Cameras (e.g., Kinect, RealSense): Directly output depth maps, significantly simplifying 3D perception.

• Challenges: Variations in lighting, high computational complexity, real-time requirements.

Inertial Measurement Unit (IMU)

• Typical 6-axis Combination: 3-axis accelerometer + 3-axis gyroscope.

• Core Issues: Gyroscope drift, accelerometer noise.

• Solutions: Fusion with GPS, vision, and wheel speed sensors (e.g., Kalman filtering).

Force/Torque Sensors

• Types: Strain gauge type, piezoelectric type, optical type.

• Application Scenarios: Precision assembly (e.g., mobile phone parts), surgical robots (tactile feedback), collaborative robot safety control.

Sensor Fusion: The Core of Intelligent Decision-Making

Single sensors have limitations (e.g., cameras affected by lighting, LiDAR failing in fog). Multi-sensor fusion technology integrates data from multiple sources through algorithms (Kalman filtering, particle filtering, deep learning) to enhance system robustness.Typical Applications:

• Autonomous Driving: LiDAR + Cameras + Radar + GPS + IMU

• Drones: Visual odometry + IMU + Barometer (VIO technology)

• Industrial Robotic Arms: Force sensors + Visual guidance

The technological evolution of robot sensors directly drives the boundaries of intelligence—from precise industrial manufacturing to deep-sea exploration and even interstellar roaming, all rely on the continuous evolution of these “senses.” In practical design, it is necessary to balance performance, cost, and complexity according to task requirements to build the optimal perception system.