Reliable part detection
Locate selectable components despite overlap, changing bin depth and normal presentation variation.
Vision-guided random-part handling
Robotic Bin Picking Systems
Bin-picking software uses a calibrated 2D image or 3D point cloud to estimate a component pose, select a reachable grip and plan a collision-aware robot path. The complete system also needs suitable lighting, tooling, bin presentation, failed-pick recovery and a defined downstream hand-off.
Where bin picking may fit
Start with the parts and the bin.
Representative trials establish whether the parts can be found, gripped and removed reliably under realistic presentation conditions.
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Components arrive randomly arranged in bins, totes or stillages
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Manual picking restricts machine autonomy or exposes operators to repetitive work
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Part pose can be measured with suitable 2D or 3D vision
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A defined downstream nest, conveyor or machine hand-off is available
Production outcomesWhat the system
What the system
must prove.
Performance is the sustainable accepted-pick rate across the real part mix and bin condition—not the robot's headline speed.
Verified gripping
Select an accessible grip, confirm pickup and recover safely after a failed or double pick.
Controlled hand-off
Present the component in the orientation and condition required by the next process.
Complete system scopeEvery bin-picking element
Every bin-picking element
engineered together.
Vision software is one part of a production system that also depends on optics, tooling, motion, recovery and safe access.
2D or 3D sensing
Camera, optics, lighting and calibration selected around the part surface and required pose.
Pose and path planning
Software ranks reachable components and calculates a collision-aware approach and withdrawal.
Robot and tooling
Reach, payload, gripper geometry and services matched to the complete moving load.
Recovery logic
Defined actions for no-pick, failed grip, obscured parts, empty-bin and downstream faults.
Safeguarding
Replenishment, access and operating modes based on the complete cell risk assessment.
Engineering checks01 02 03 04
Decisions that shape reliable picking.
Part behaviour
Reflective surfaces, tangling, nesting, deformation and contamination affect what the sensor and gripper can achieve.
Bin geometry
Wall height, corners, usable depth and replenishment method influence reach and collision risk.
Accepted-pick rate
Specify good parts delivered at the hand-off after imaging, path calculation and normal recovery events.
Trial method
Test representative variants and realistic fill levels, then record successful picks, cycle and failure modes.
Bin-picking FAQs
Questions to resolve before specification.
Final answers depend on representative parts, presentation, rate and the required hand-off.
When is 3D vision needed for robotic bin picking?
Three-dimensional sensing is commonly needed when parts overlap at different heights or the robot must estimate full pose. Flat, well-separated parts may permit a simpler 2D approach.
Can reflective or tangled parts be bin picked?
Possibly, but reflections, occlusion, nesting and entanglement increase application risk. Representative parts and realistic bin conditions must be trialled.
What determines bin-picking cycle time?
Image acquisition, pose calculation, robot travel, grip verification, failed-pick recovery, bin depth and downstream placement all contribute to sustainable output.
Related engineeringConnect vision, tooling
Connect vision, tooling
and the product route.
Start with representative parts
Discuss your robotic bin-picking project.
Send part samples or drawings, bin dimensions, presentation photos, the target accepted rate and downstream hand-off. We will define the next technical step.