Smart Parallel Picking Skill
Detection of different objects.
Overview
A typical mechanical type of gripping is the parallel grip, in which the mostly linear stroke movement of the gripper jaws enables objects to be lifted or gripped.
The possible gripping axes of the gripper jaws are represented in the GUI by the lines on the objects. There are three different color codes, blue describes the current gripping candidate. Yellow stands for possible other gripping points, while red indicates that these gripping axes are rejected. Depending on the settings, more or less probable grab possibilities are shown here. Limiting the possible grab point options allows an individually adjustable balance between quality and speed.
Similar to the "Smart Vacuum Picking Skill", this skill can be optimally used for separation tasks. It can be used in many ways after it recognizes the best picking candidate on unknown parts in the source box. Classically, it is used in logistics areas, where the variety of objects is often not very clear. It solves the problem of kitting in the automotive industry, as well as the separation in warehouse processes.
Boxes
Cylinders
M10 nuts
Random Items
Skill Result Information
Position
Position of the grasp point relative to the robot’s coordinate system (in m)
Orientation
Rotation of the grasp point relative to the robot’s coordinate system (in m)
Width
Determined grip width (in m)
Specifications
Conditions
Camera mount:
Dynamic
Static
Camera distance:
35 – 45 cm
Parts dimensions:
2 – 6 cm
Parts material:
matt - low reflection
Specs
Avg. recognition time:
< 0.5 seconds
Supported grippers:
Two-finger-gripper
Robotiq 2F-85
Zimmer GEH6040IL
OnRobot
Features
Collision detection
Generalized to a variety of object types
Picking from bulk
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.
Used objects: small cardboard boxes (70x50x50mm), matt surface
Camera distance: 550mm
Camera mount: 30° angled
Skill parameters:
Min score: 0.5
other parameters: default values
Technical Parameter Description
Parameter
Edge sensitivity
edge_sensitivity
float
Determines the sensitivity for the edge-detection algorithm. A larger sensitivity can help to detect small steps in depth values. However, a higher sensitivity also leads to more false edge detections and a longer runtime.
Min score
min_score
float
Minimum quality score for grasp candidates. The quality score is estimated by a neural network.
Max candidates
max_candidates
int
The maximum number of grasp candidates that are evaluated by the neural network. While larger numbers increase the runtime, they can also improve the robustness of the skill.
Friction coefficient
friction_coefficient
float
The friction coefficient between the gripper fingers and items. The default value of 0.2 fits for most materials.
Min grasp distance
min_grasp_distance
float
The minimum distance between two grasp candidates. This parameter is used to limit the number of very similar candidates and, thus, increasing the robustness and reducing the runtime of the skill.
Grasp offset
grasp_offset
float
An offset for the grasp width. This parameter is used to increase the predicted grasp with to avoid collisions between the gripper and the item to be picked.
Detections
pose (Transformation)
The pick pose for the parallel gripper
width (float)
Grasp width in meters
quality (float)
Grasp quality as estimated by the neural network.
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