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Crane hook anti-collision warning system

2025-07-29 01:26:10

The anti-collision warning system of the crane hook is a key technology to ensure operational safety, avoid equipment damage and casualties, especially in complex working conditions (such as multi-machine coordination, narrow space or high-risk areas). The following is the core technical framework and implementation plan of the system:

1. System core functions

  • Real-time monitoring : Detect the distance and relative speed between the hook, load and surrounding obstacles (buildings, equipment, personnel, etc.).

  • Multi-level warning : triggers sound and light alarms, automatic deceleration or emergency braking according to the degree of danger.

  • Data logging : Stores collision event data for accident analysis and optimized operations.

2. Key technical components

(1) Sensor technology

  • LiDAR

    • Scan the surrounding environment and generate 3D point cloud data with an accuracy of up to ±2cm, suitable for high-precision obstacle avoidance.

    • Disadvantages : high cost, requires dust and shock proof design.

  • Ultrasonic Sensors

    • Short-distance (0.1~5m) ranging, economical and durable, but easily affected by temperature and airflow.

  • Millimeter wave radar

    • It has strong anti-interference ability and is suitable for adverse weather (rain, fog), with a detection distance of up to 50m.

  • Visual sensor (camera + AI)

    • Identify obstacle types (such as people and vehicles) through deep learning and obtain depth information with RGB-D cameras.

(2) Data processing and algorithms

  • Multi-sensor fusion

    • Combine lidar, millimeter wave, and vision data to improve detection reliability through Kalman filtering or neural networks.

  • Dynamic path prediction

    • Predict the time to collision (TTC) based on the hook's trajectory and obstacle speed.

  • Priority Judgment

    • Obstacles are classified into different levels (e.g., personnel > equipment > buildings) to trigger different response strategies.

(3) Control and execution

  • Warning output

    • Sound and light alarm : buzzer + LED flashing to remind the operator.

    • Cab HUD : Displays obstacle distance and direction in real time.

  • Automatic intervention

    • The crane is controlled to slow down or stop via PLC (needs to be linked with the main control system).

    • Case : A port crane automatically cuts off the lifting power when it detects people entering a danger zone.

3. System architecture design

plaintext 
Sensor layer (LiDAR/Radar/Vision)  
       ↓  
Data fusion layer (multi-source information processing)  
       ↓  
Decision-making layer (collision risk assessment algorithm)  
       ↓  
Execution layer (alarm/brake/logging)

4. Technical Challenges and Solutions

  • Real-time requirements

    • Use edge computing (such as NVIDIA Jetson) to reduce data processing latency, with response time less than 100ms.

  • Complex environmental interference

    • Adaptive filtering algorithm eliminates false alarms (such as fluttering flags, flying birds).

  • Wide coverage

    • Multi-sensor arrays are deployed on crane arms and hooks to eliminate blind spots.

5. Cutting-edge technology direction

  • Digital Twin + Simulation Early Warning

    • Rehearse the lifting path through the virtual model and mark potential collision points in advance.

  • UWB (Ultra-Wideband) Positioning

    • Tag personnel and equipment to achieve centimeter-level real-time tracking.

  • 5G+Cloud Collaboration

    • Multi-machine data sharing avoids collisions during group tower operations.

6. Application Cases

  • Construction tower cranes : After installing the lidar + camera system, collision accidents were reduced by 70%.

  • Automated warehouse : Anti-collision coordination between hooks and AGVs is achieved through UWB tags.

7. Standards and Verification

  • ISO 10218-2 : Safety standard for industrial robots (some clauses apply to cranes).

  • Test method :

    • Static obstacle test : Verify the minimum detection distance.

    • Dynamic simulation test : Response speed when a moving target intrudes into the path.

Summarize

The anti-collision warning system needs to customize the sensor combination and algorithm parameters according to the type of crane (bridge, tower, portal), and will develop in the direction of intelligence, low power consumption, and high integration in the future . In actual deployment, the sensors need to be calibrated regularly and used in conjunction with traditional safety measures (such as physical limiters).

The anti-collision warning system of the crane hook is a key technology to ensure operational safety, avoid equipment damage and casualties, especially in complex working conditions (such as multi-machine coordination, narrow space or high-risk areas). The following is the core technical framework and implementation plan of the system:

1. System core functions

  • Real-time monitoring : Detect the distance and relative speed between the hook, load and surrounding obstacles (buildings, equipment, personnel, etc.).

  • Multi-level warning : triggers sound and light alarms, automatic deceleration or emergency braking according to the degree of danger.

  • Data logging : Stores collision event data for accident analysis and optimized operations.

2. Key technical components

(1) Sensor technology

  • LiDAR

    • Scan the surrounding environment and generate 3D point cloud data with an accuracy of up to ±2cm, suitable for high-precision obstacle avoidance.

    • Disadvantages : high cost, requires dust and shock proof design.

  • Ultrasonic Sensors

    • Short-distance (0.1~5m) ranging, economical and durable, but easily affected by temperature and airflow.

  • Millimeter wave radar

    • It has strong anti-interference ability and is suitable for adverse weather (rain, fog), with a detection distance of up to 50m.

  • Visual sensor (camera + AI)

    • Identify obstacle types (such as people and vehicles) through deep learning and obtain depth information with RGB-D cameras.

(2) Data processing and algorithms

  • Multi-sensor fusion

    • Combine lidar, millimeter wave, and vision data to improve detection reliability through Kalman filtering or neural networks.

  • Dynamic path prediction

    • Predict the time to collision (TTC) based on the hook's trajectory and obstacle speed.

  • Priority Judgment

    • Obstacles are classified into different levels (e.g., personnel > equipment > buildings) to trigger different response strategies.

(3) Control and execution

  • Warning output

    • Sound and light alarm : buzzer + LED flashing to remind the operator.

    • Cab HUD : Displays obstacle distance and direction in real time.

  • Automatic intervention

    • The crane is controlled to slow down or stop via PLC (needs to be linked with the main control system).

    • Case : A port crane automatically cuts off the lifting power when it detects people entering a danger zone.

3. System architecture design

plaintext 
Sensor layer (LiDAR/Radar/Vision)  
       ↓  
Data fusion layer (multi-source information processing)  
       ↓  
Decision-making layer (collision risk assessment algorithm)  
       ↓  
Execution layer (alarm/brake/logging)

4. Technical Challenges and Solutions

  • Real-time requirements

    • Use edge computing (such as NVIDIA Jetson) to reduce data processing latency, with response time less than 100ms.

  • Complex environmental interference

    • Adaptive filtering algorithm eliminates false alarms (such as fluttering flags, flying birds).

  • Wide coverage

    • Multi-sensor arrays are deployed on crane arms and hooks to eliminate blind spots.

5. Cutting-edge technology direction

  • Digital Twin + Simulation Early Warning

    • Rehearse the lifting path through the virtual model and mark potential collision points in advance.

  • UWB (Ultra-Wideband) Positioning

    • Tag personnel and equipment to achieve centimeter-level real-time tracking.

  • 5G+Cloud Collaboration

    • Multi-machine data sharing avoids collisions during group tower operations.

6. Application Cases

  • Construction tower cranes : After installing the lidar + camera system, collision accidents were reduced by 70%.

  • Automated warehouse : Anti-collision coordination between hooks and AGVs is achieved through UWB tags.

7. Standards and Verification

  • ISO 10218-2 : Safety standard for industrial robots (some clauses apply to cranes).

  • Test method :

    • Static obstacle test : Verify the minimum detection distance.

    • Dynamic simulation test : Response speed when a moving target intrudes into the path.

Summarize

The anti-collision warning system needs to customize the sensor combination and algorithm parameters according to the type of crane (bridge, tower, portal), and will develop in the direction of intelligence, low power consumption, and high integration in the future . In actual deployment, the sensors need to be calibrated regularly and used in conjunction with traditional safety measures (such as physical limiters).

The anti-collision warning system of the crane hook is a key technology to ensure operational safety, avoid equipment damage and casualties, especially in complex working conditions (such as multi-machine coordination, narrow space or high-risk areas). The following is the core technical framework and implementation plan of the system:

1. System core functions

  • Real-time monitoring : Detect the distance and relative speed between the hook, load and surrounding obstacles (buildings, equipment, personnel, etc.).

  • Multi-level warning : triggers sound and light alarms, automatic deceleration or emergency braking according to the degree of danger.

  • Data logging : Stores collision event data for accident analysis and optimized operations.

2. Key technical components

(1) Sensor technology

  • LiDAR

    • Scan the surrounding environment and generate 3D point cloud data with an accuracy of up to ±2cm, suitable for high-precision obstacle avoidance.

    • Disadvantages : high cost, requires dust and shock proof design.

  • Ultrasonic Sensors

    • Short-distance (0.1~5m) ranging, economical and durable, but easily affected by temperature and airflow.

  • Millimeter wave radar

    • It has strong anti-interference ability and is suitable for adverse weather (rain, fog), with a detection distance of up to 50m.

  • Visual sensor (camera + AI)

    • Identify obstacle types (such as people and vehicles) through deep learning and obtain depth information with RGB-D cameras.

(2) Data processing and algorithms

  • Multi-sensor fusion

    • Combine lidar, millimeter wave, and vision data to improve detection reliability through Kalman filtering or neural networks.

  • Dynamic path prediction

    • Predict the time to collision (TTC) based on the hook's trajectory and obstacle speed.

  • Priority Judgment

    • Obstacles are classified into different levels (e.g., personnel > equipment > buildings) to trigger different response strategies.

(3) Control and execution

  • Warning output

    • Sound and light alarm : buzzer + LED flashing to remind the operator.

    • Cab HUD : Displays obstacle distance and direction in real time.

  • Automatic intervention

    • The crane is controlled to slow down or stop via PLC (needs to be linked with the main control system).

    • Case : A port crane automatically cuts off the lifting power when it detects people entering a danger zone.

3. System architecture design

plaintext 
Sensor layer (LiDAR/Radar/Vision)  
       ↓  
Data fusion layer (multi-source information processing)  
       ↓  
Decision-making layer (collision risk assessment algorithm)  
       ↓  
Execution layer (alarm/brake/logging)

4. Technical Challenges and Solutions

  • Real-time requirements

    • Use edge computing (such as NVIDIA Jetson) to reduce data processing latency, with response time less than 100ms.

  • Complex environmental interference

    • Adaptive filtering algorithm eliminates false alarms (such as fluttering flags, flying birds).

  • Wide coverage

    • Multi-sensor arrays are deployed on crane arms and hooks to eliminate blind spots.

5. Cutting-edge technology direction

  • Digital Twin + Simulation Early Warning

    • Rehearse the lifting path through the virtual model and mark potential collision points in advance.

  • UWB (Ultra-Wideband) Positioning

    • Tag personnel and equipment to achieve centimeter-level real-time tracking.

  • 5G+Cloud Collaboration

    • Multi-machine data sharing avoids collisions during group tower operations.

6. Application Cases

  • Construction tower cranes : After installing the lidar + camera system, collision accidents were reduced by 70%.

  • Automated warehouse : Anti-collision coordination between hooks and AGVs is achieved through UWB tags.

7. Standards and Verification

  • ISO 10218-2 : Safety standard for industrial robots (some clauses apply to cranes).

  • Test method :

    • Static obstacle test : Verify the minimum detection distance.

    • Dynamic simulation test : Response speed when a moving target intrudes into the path.

Summarize

The anti-collision warning system needs to customize the sensor combination and algorithm parameters according to the type of crane (bridge, tower, portal), and will develop in the direction of intelligence, low power consumption, and high integration in the future . In actual deployment, the sensors need to be calibrated regularly and used in conjunction with traditional safety measures (such as physical limiters).

Pre: Specification for multi-machine collaborative operation of crane hooks
Next: Research on the Anti-unhooking Technology of Crane Hook

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