3D vision systems are steadily becoming a mainstay in the manufacturing industry. These systems aid in quality assurance and production debugging efforts with their analytical capabilities. By enhancing productivity and efficiency, 3D vision improves production lines from inception to end-of-life disposal.
Picking may seem as simple as detecting, picking, and placing, but errors are introduced at each stage, leading to significant uncertainty in object handling. Therefore, before the benefits of 3D vision can be realized, several key challenges must be addressed.
What is 3D Vision?
Mimicking human vision, 3D vision systems use a pair of cameras to capture images from two different angles to reconstruct the spatial layout of an object. Based on 3D data about an object's shape, size, position, and orientation in three-dimensional space, 3D vision equips machines with the ability to “see” their surroundings in real time and in detail. This ability enables machines to make decisions based on a comprehensive view of an object or environment. Utilizing advanced hardware, 3D vision systems are able to sense the color, size, and shape of products. Deep learning technology leverages this data to examine product quality, inspect parts, and identify defects. In essence, 3D vision allows for continuous monitoring of production lines without human intervention.
Key Challenges for 3D Vision in Picking Systems
1. Precision of 3D Vision Systems
The precision of 3D vision systems is integral to picking applications. Robots must accurately pick objects based on visual data. This is where the “detect” part of the automated system is crucial. Higher precision enhances confidence in object detection, capturing all necessary details. The precision of a 3D vision system significantly impacts how and where an object is picked.
Mech-Mind's high-performance 3D vision products generate high-quality point clouds and accurate recognition results for different objects.
2. Speed of Capture
As was mentioned above, robots should execute tasks as effectively as humans to achieve a favorable return on investment (ROI). Speed is a critical factor in determining whether an automated picking system is a worthwhile investment, especially given increasing production demands, warehouse logistics, and order fulfillment. To compete with existing solutions, the 3D vision system must be able to handle at least 600 picks per hour; otherwise, high-quality data becomes irrelevant.
3. High Dynamic Range
As picking processes become increasingly automated, such as random picking, 3D vision systems with high dynamic range (HDR) are essential. In other words, to accurately pick from a pile of objects with a wide contrast spectrum, the vision system must deliver comprehensive data on each object. This is possible if the vision system in use can deliver information across the entire range, rather than only on a portion of the contrast spectrum. For example, if dark and bright/shiny pieces are mixed in a bin and you attempt to collect data on all of them in a single shot, significant difficulties will arise due to the two extremes unless the 3D vision system is capable of handling this range.
4. Handling Complicated Scenarios
While 3D scanners can effectively scan most objects, certain surfaces present challenges. When scanning objects with reflective surfaces such as metal or mirrors, the laser light is reflected off the surface, resulting in inaccurate 3D models due to a lack of or erroneous data received by the scanner. Transparent materials such as glass also pose challenges for 3D scanning, as they do not reflect laser light and fail to transmit data back to the scanner.
Mech-Mind's structured light imaging algorithm enables high-quality imaging of both dark and shiny reflective surfaces.
5. System Stability
The expansion of robotics across various industries has introduced “harsh” operating conditions that can degrade the quality of 3D data provided by vision systems over time. This is a major challenge for automated picking tasks. 3D vision systems are highly susceptible to environmental conditions due to the optics involved. There is an urgent need for a robust 3D vision system that can adapt to external conditions while maintaining reliable performance over time. In short, the 3D vision system should be industrial-grade.
Conclusion
In summary, these challenges in 3D vision have historically limited the use of automated picking systems, but this is no longer the case. Mech-Mind has effectively addressed these challenges to provide the picking system developers with an accurate and seamless 3D vision experience. Combining high accuracy with rapid scanning speed, extended FOV, and improved stability, the Mech-Eye industrial 3D cameras deliver exceptional performance and efficiently handle demanding applications such as random bin picking and high-precision piece picking. For more information, please contact the Mech-Mind experts at info@mech-mind.net.
