3D Vision-Guided Robotic Bag Depalletizing Solution
MRDVS provides a 3D vision-guided solution for detecting, locating, and depalletizing bagged materials from pallets. The solution combines 3D sensing, bag detection, grasp pose estimation, and robot task coordination. Supported bag types, working distance, cycle time, and pick performance are evaluated according to the bag specifications, pallet pattern, robot, end effector, and operating environment.
Core Vision Capabilities for Bag Depalletizing
The system captures 3D data from the pallet surface, identifies a pickable bag, calculates the position and orientation required by the robot, and updates the scene after each pick to support continuous depalletizing operations.
01. 3D Scene Capture
Four Core Vision Functions
Captures depth data and 3D point clouds from the pallet surface to analyze bag positions, contours, and stacking conditions.
02. Bag Detection and Segmentation
Identifies individual bags in irregular, tilted, or partially occluded stacks and determines which target can be picked first.
03. Grasp Pose Estimation
Calculates the position, orientation, and coordinate information required for robotic picking based on the bag surface and spatial location.
04. Scene Update and Re-detection
Re-captures the pallet surface after each pick, updates the remaining bag positions, and generates information for the next task cycle.
System Integration and Compatibility
A depalletizing system requires coordinated operation between the 3D sensor, processing unit, perception software, robot, end effector, and line control system. Interfaces, installation methods, and scope responsibilities are defined according to the project configuration.
01. Robot Integration
Four Integration Areas
Defines pose-data transmission and task execution according to the robot brand, model, controller version, and available task interfaces.
02. End-Effector Integration
Evaluates vacuum, clamping, or other end-effector options based on bag material, weight, surface characteristics, and picking direction.
03. PLC and Line Communication
Exchanges task, status, completion, and exception data with the PLC, conveyor system, and upstream or downstream equipment.
04. Calibration and Installation
Completes coordinate calibration and confirms the accessible working range according to the sensor mounting position, robot coordinate system, and available site space.
Supported Bags and Operating Envelope
Bag material, dimensions, weight, surface characteristics, and stacking conditions directly affect vision detection and robotic picking. The final operating envelope should be confirmed using representative bag samples, pallet images, and site conditions.
| Bag Type | Flexible Packages,Cartons,Jute Bags |
|---|---|
| Pallet Pattern | Supports 2-bag, 3-bag, 5-bag, 6-bag interlocking patterns, and pinwheel pattern and more |
| Package Status | • Minor deformation, adhesion, and underfilling allowed • Severe deformation or surface strap marks require separate evaluation |
| Working Distance | 2.8-3.2m |
| Lighting Conditions | • Keep lights on during system operation • Prevent direct sunlight in depalletizing area (use curtains near windows) |
| Pallet Stacking | • Place on flat ground; tilt angle ≤3° • All packages in the same stack must have identical size and packaging |
Performance Data and Validation Conditions
System Architecture
The solution consists of 3D data acquisition, vision processing, perception software, and robot execution modules. The final scope of supply depends on the project agreement and system integration model.
How It Works
The system completes pallet-surface capture, 3D data generation, bag detection, grasp pose calculation, task-data transmission, and scene updating in sequence.
The Role of the M10 in the Bag Depalletizing Solution
The M10 provides depth data, intensity information, and 3D point clouds for the depalletizing system, helping the system obtain pallet geometry and bag positions. The actual mounting distance, coverage area, and system cycle time depend on the sensor configuration, site space, and project requirements.
| Depth Technology | dToF |
|---|---|
| Data Outputs | Depth Map, Intensity Map, 3D Point Cloud, I/O |
| Depth Resolution | 480 × 320 |
| Frame Rate | Up to 20 FPS |
| Field of View | 90° × 60° (±3°) |
| Interfaces | JST; I/O, Serial, Virtual Ethernet |
Application Scenarios
Animal Feed
Food and Grain
Warehousing and Logistics
Fertilizers and Chemicals
FAQ
What is robotic bag depalletizing?
Robotic bag depalletizing uses a vision system to capture pallet data, identify a bag to be picked, and calculate its pick position and pose. The resulting information is sent to the robot for execution. A complete system typically involves a vision sensor, processing unit, robot, end effector, and site control system.
What types of bags can the solution handle?
Bag material, dimensions, weight, surface reflectivity, deformation, and pallet pattern can all affect perception and picking performance. The suitability of woven bags, paper bags, multi-layer bags, and other bag types should be evaluated using actual samples and site conditions.
How does 3D vision guide a robot during depalletizing?
The vision system first captures 3D data from the pallet surface. It then identifies a pickable bag and calculates its pick position and pose. After coordinate transformation and system communication, the information is sent to the robot controller. The robot uses its end effector to complete the pick, after which the pallet data is updated for the next cycle. The exact workflow depends on the project configuration.
Which robots and end effectors are compatible?
Compatibility with robots, controllers, PLCs, communication protocols, and end effectors must be confirmed for each project. An evaluation normally requires the robot model, controller version, communication interface, end-effector type, payload, and available working envelope.
What information is required for a depalletizing project assessment?
Please provide the bag material, dimensions, weight, and photos, together with images of full, nearly empty, and irregular pallets. The evaluation also requires pallet dimensions, maximum pallet height, target cycle time, robot model, end-effector type, available space, lighting, dust conditions, and upstream and downstream equipment interfaces. Actual bag samples can support further feasibility validation.