Automated Tray Unloading: How Robots Handle Plastic, Transparent, and Semitransparent Trays

Automated tray unloading sounds like a straightforward depalletizing problem. The robot picks trays off a pallet and places them onto a conveyor or into a downstream process. Straightforward until the trays are plastic.

Plastic trays, and particularly semitransparent or translucent plastic trays, are among the most difficult objects for standard 3D vision systems to handle reliably. They lack the surface features that help cameras locate and identify objects. They transmit and scatter light in ways that produce inconsistent or missing depth data. And they often arrive stacked with very little height difference between the top tray and the ones below it, making layer discrimination a precision requirement rather than a general sensing problem.

This post explains why tray unloading is a challenging vision application, what makes plastic and semitransparent trays specifically difficult, and how to build a robot cell that handles them reliably.

Why Trays Are Harder Than Cases

Most automated depalletizing content focuses on cardboard cases. Cases are relatively cooperative for vision systems: they have matte surfaces that produce diffuse reflection, printed graphics and barcodes that provide surface features for cameras to lock onto, and substantial height differences between layers that make layer discrimination easy.

Plastic trays behave differently in almost every relevant dimension.

Surface characteristics - Plastic trays are often smooth, uniform in color, and low in surface texture. They provide few visual features for the camera to differentiate between the top surface of the top tray and the surface below it. Semitransparent trays are worse: they partially transmit light rather than reflecting it from the surface, which causes structured light cameras to generate inconsistent depth readings because the light pattern penetrates into the material rather than reflecting cleanly from the top surface.

Stacking geometry - Trays are designed to stack efficiently, which means they nest together with very little vertical clearance between layers. The height difference between the top tray and the one below it may be only a few millimeters. This demands significantly higher Z-axis precision from the vision system than standard case depalletizing, where layer height differences are measured in centimeters.

Edge geometry - Tray edges and rims are thin and often beveled. A camera generating a point cloud of a stacked tray set needs to resolve the rim of the top tray from the rim of the tray below it at close spacing. For standard depth cameras, this is at or beyond their practical resolution limit.

What the Vision System Needs to Handle

Reliable automated tray unloading requires a vision system that addresses these specific challenges rather than a general-purpose depalletizing solution.

Accurate depth data on low-texture surfaces - The camera needs to produce reliable point clouds on smooth, featureless plastic surfaces where standard stereo cameras struggle for lack of surface disparity. Structured light cameras, which generate their own texture by projecting a pattern onto the surface, are significantly more robust on low-texture materials. They do not depend on the surface having features to match between images.

Semitransparent material handling - For trays that transmit or scatter light, HDR exposure control and advanced signal processing help recover depth data from surfaces that would defeat standard single-exposure systems. The goal is to capture the top surface reflection accurately despite the material's partial transparency.

Precise layer discrimination - The vision system needs to identify the top tray with enough Z-axis accuracy to confirm it is the topmost layer and calculate a grasp point that does not contact the tray below. For tightly nested trays, this requires sub-millimeter depth precision in the layer separation measurement.

Consistent grasp point selection - Tray rims and lips are the natural grasp targets for most end-of-arm tools. The vision system needs to locate the rim geometry reliably and calculate approach angles that allow the gripper to engage the rim without sliding down into the nested stack.

End-of-Arm Tooling Considerations

Camera performance is only part of the tray unloading challenge. The end effector needs to be designed for the tray geometry.

Vacuum grippers are the most common tool for plastic tray unloading. They work well on the flat base surface of the tray and do not require precise alignment to a specific feature. The challenge is that the base surface of a stacked tray is resting on the tray below it, making it inaccessible until the top tray is lifted. This means vacuum grippers need to engage from the rim or side wall rather than the base, which requires the gripper geometry to match the tray's rim profile.

Custom gripper configurations that clamp the rim from both sides, or suction cups positioned to engage the tray wall at a specific height, are the most reliable solutions for nested plastic trays. The gripper design is often as consequential as the camera selection for reliable production performance.

Which Arms Handle Tray Unloading

Tray unloading payload requirements depend on tray size, material, and whether trays are picked individually or in stacks. Most plastic trays used in food processing, electronics, and consumer goods assembly fall well within the 5 to 10 kg range for individual picks.

The Fairino FR5 ($6,999) covers light tray unloading applications where individual tray weight stays under 5 kg. Its 924 mm reach and 6-axis flexibility allow it to approach tray rims from the angles that vision-calculated grasp points require.

For heavier trays or stacked picks where combined weight exceeds 5 kg, the Fairino FR10 ($10,199) provides 10 kg of payload capacity alongside the reach needed to cover a full pallet footprint from a fixed mount.

For cells integrating a vision platform, Blue Sky Robotics' Blue Argus platform provides a 3D depth camera, compute unit, and vision SDK as a pre-validated kit compatible with both arms through Python SDK integration.

Getting Started

Use our Cobot Selector to match an arm to your tray handling requirements. Browse our full Fairino lineup and UFactory cobots with current pricing. Request a Blue Argus demo to test vision performance on your specific tray type, or book a live demo to discuss your full cell design. To learn more about computer vision software visit Blue Argus.

FAQ

Why are plastic trays difficult for robot vision systems?

Plastic trays present three challenges: smooth, low-texture surfaces that provide few features for cameras to lock onto; semitransparent materials that transmit rather than reflect light, causing inconsistent depth readings; and tight nesting geometry with very small height differences between stacked layers that demand high Z-axis precision from the vision system.

What type of camera works best for semitransparent tray unloading?Structured light cameras with HDR exposure control handle semitransparent and low-texture surfaces more reliably than standard stereo cameras. Structured light cameras project their own pattern onto the surface rather than relying on surface features, which is what makes them effective on materials that defeat feature-matching approaches.

What end effector works best for plastic tray unloading?

Vacuum grippers engaging the tray wall or rim are the most common solution for nested plastic trays where the base surface is inaccessible. Custom gripper geometry matched to the specific tray rim profile produces the most reliable pick results. The gripper design is often as important as the camera selection for consistent tray unloading performance.