Emerging 3D Vision Technologies for Industrial Robots

As more industries come to terms with the need for robots in their operations, the number of industrial robots installed worldwide will experience positive growth within the next five years. With this developing interest in automation comes more investments into research and development. The outcome is leaner robotic systems with more advanced components. A growing trend for robotic workcells is 3D automatic vision. This technology allows the robot to identify an object’s position, size, depth, and color. Sectors like logistics, food processing, life science, and manufacturing are finding ways to automate their processes using visual components.

What are the different approaches to 3D vision?

Vision technology is not a “one size fits all” tool. Certain factors such as application, equipment, product, environment, and budget will determine how to integrate vision into the process. There is no standard when it comes to setting up real-time 3D imaging in a robotic system. However, there are a few standard techniques used by vision-integration experts, each tailored to benefit specific tasks. These techniques are stereo vision, time-of-flight (TOF), laser triangulation, and structured light.

Laser Triangulation

Objects pass through a beam of light emitted by a laser scanner. A camera positioned at a specific angle records an image of the laser line as the item passes through, distorting the beam and creating a profile of the object.

Structured light

A projector creates a thin band of light to project a pattern on an object. Cameras from different angles observe the various curved lines from the light to develop a 3D image of the object.

Time of Flight (ToF)

A camera uses a high-power laser scanner to emit light reflected from the object back to the image sensor. The distance from the camera to the object is calculated based on the time delay between transmitted and received light.

Stereo vision

The robotic system uses two cameras to record the same 2D view of an object taken from two different angles. The software then uses the established position of the two cameras and compares corresponding points in the two flat images to identify variations and produce an image.

What applications are using 3D robotic vision?

There is a need for the modern industrial robot to detect objects, recognize parts, and grip components at the right angle. While traditional robots are perfect for locating parts consistently, modern robotics can coordinate corrections to detect where the piece is. Instead of an entire production line coming to a stop because subsequent actions are not indefinite order, the system quickly recognizes a change and adapts to it. As a result, an array of industrial applications across industries invest in 3D robotic vision. These include the logistics, food processing, life science, manufacturing, and automotive industries. With so many sectors automating, the use of vision technology is expanding into new territory. Depalletizing applications use 3D vision components to scan pallets filled with various types of shipping boxes for sorting. They use scanners to send the image to software to allow the robot to detect box types based on texture patterns and send them to designated areas. A food processing plant uses multispectral vision tech and special lighting to inspect the product and detect spoilage. Applications that have traditionally used vision technology are upgrading to more innovative equipment. An aerospace company replaced traditional inspection tools with 3D scanning to inspect turbine blades for imperfections, reducing inspection time from 18 hours to 45 minutes. Vision technology will continue to expand, with future trends predicted in logistics applications, multispectral machine vision, adaption using machine learning with 3D vision, and liquid lenses to allow more precise images from greater distances.

Crucial subsystems and components for vision applications

The most coordinated automation systems have more than a single automated control system and components integrated to make an efficient workcell assembly. When it comes to incorporating advanced 3D vision options like object tracking, product profiling, and bin picking into a process line, the system should generate 3D imagery data. The use of 3D vision in robotic systems requires integrating various components to facilitate adequate power supply, real-time processing, and safety. Another critical component of successful automation is communication capability. It is good practice in the digital age to have connectivity ports to digitally connect a system to other pieces of equipment for data sharing. Emerging robot technologies facilitate Wi-Fi connectivity for the same purpose. At the design stage, driving a risk assessment study is the only way to identify and remove problems from a system that could risk malfunction. A 3D vision-enabled robot can safely stop equipment to prevent injury and damage to equipment. If buyers invest in the research and upfront planning, the result will be a flexible and easy-to-use automated system.

Conclusion

Modern manufacturing demands more out of less, with leaner production lines needing to provide greater output. The influence of robotic vision will continue to expand into different production areas and find brand-new ways to improve automated processes. Expect more 3D visual components to become common in automatic systems in the future.