Smart Vacuum Picking Skill

Overview

A vacuum gripper belongs to the group of astringent grippers in which a continuous holding force is applied without compressive stress. By creating a vacuum inside the gripping system, objects are attracted and held onto the gripper accordingly if the vacuum is maintained sufficiently. The Smart Vacuum Picking Skill is designed for single-suction vacuum grippers.

Depending on the setting of the skill, a certain number of possible pick points is displayed in the camera image. The color coding provides information on the interpretation of the pick points. Points marked in yellow represent potential pick points that also meet the quality requirements of the system. The blue point, on the other hand, defines the best pick point selection, which best meets the requirements. The selection and sorting of the pick points can be set via the parameters in the skill. The pick point position (in meters) and the quality value are also displayed in the Graphical User Interface.

A classic use case for this skill is, for example, singulation in a department store, since this skill can grab a large number of unknown parts. It does not focus on specific parts, but on an optimal grasping point on any object. This means that numerous items, such as pens, packaging, plates and even bags, can be singled out and placed in the shipping carton with just one skill.

Boxes

Sample Tubes

Random

Programm application

The skill is already pre-configured in a way that enables it to handle a wide variety of parts. To test the skill, go to "Option" - "Test". There you have the possibility to run the skill and view the predictions of the grip points.

If the skill is to be optimized for the parts, this can also be implemented in the test area. After the skill has been selected, the parameters can be optimized in the lower right corner. After saving the new parameters, the skill can be executed again immediately. The image on the left shows the new prediction of the skill. With the "Parameter Download" button, the parameters can easily be downloaded, so that they can be restored at a later time.

The different recordings of the skill are stored in the "Capture" area.

Skill Result Information

Specifications

Parameter Example

To ensure accurate identification of various types of objects, the skill parameters can be easily adjusted to fit your specific needs. Here are some recommendations to help you find the perfect parameters for your application. For your convenience, we've only included descriptions of parameters that differ from the default settings.

Boxes

Sample Tubes

Random

• Used objects: boxes of varying sizes, matt and shiny surfaces

• Camera distance: 550mm

• Camera mount: 30° angled

• Parameters:

  • Min Quality: 0.32

  • other parameters: default values

• Used objects: Sample Tubes of varying sizes, shiny surfaces

• Camera distance: 550mm

• Camera mount: 30° angled

• Skill Parameters:

  • Min Quality: 0.3 (Can help in situations with difficult lighting)

  • Shuffle range: 30 (Affects the maximum number of returned pick points, allows for the detection of more sample tubes at once)

  • other parameters: default values

• Used objects: Variety of products of different sizes and shapes, matt and shiny surfaces

• Camera distance: 550 mm

• Camera mount: 30° angled

• Skill parameters:

  • Min Quality: 0.32 (due to difficult lighting)

  • other parameters: default values

Technical Parameter Description

Parameter

Detections

{
  "detections":[
    {
      "pose":{
        "x":0.04955317931897811,
        "y":-0.3533660500320835,
        "z":0.3585186826107776,
        "rx":0.5325403821778465,
        "ry":0.005309205709819894,
        "rz":0.001448072456787665
      },
      "pixel":[
        463,
        146
      ],
      "quality":0.9912103414535522,
      "optimization_angle":0.0052687569243785185
    },
    {
      "pose":{
        "x":-0.09056620356773959,
        "y":-0.2685424399923941,
        "z":0.4337687332757568,
        "rx":0.5325403821778465,
        "ry":0.005309205709819894,
        "rz":0.001448072456787665
      },
      "pixel":[
        258,
        309
      ],
      "quality":0.9461788535118103,
      "optimization_angle":0.0052687569243785185
    },
    {
      "pose":{
        "x":-0.0912795197455176,
        "y":-0.26732958879254204,
        "z":0.43448801347182353,
        "rx":0.5325403821778465,
        "ry":0.005309205709819894,
        "rz":0.001448072456787665
      },
      "pixel":[
        257,
        311
      ],
      "quality":0.9578912258148193,
      "optimization_angle":0.0052687569243785185
    },
    {
      "pose":{
        "x":0.13534519716223037,
        "y":-0.17736243163479792,
        "z":0.4895606104602468,
        "rx":0.5325403821778465,
        "ry":0.005309205709819894,
        "rz":0.001448072456787665
      },
      "pixel":[
        577,
        457
      ],
      "quality":0.9114859700202942,
      "optimization_angle":0.0052687569243785185
    },
    {
      "pose":{
        "x":0.13250216629596673,
        "y":-0.17738318705110978,
        "z":0.48956632535729555,
        "rx":0.5325403821778465,
        "ry":0.005309205709819894,
        "rz":0.001448072456787665
      },
      "pixel":[
        573,
        457
      ],
      "quality":0.9156248569488525,
      "optimization_angle":0.0052687569243785185
    }
  ]
}