The global robotic vision market is witnessing significant demand. Growing applications of robotic vision technology in humanoid and industrial robots that boost automation in industrial sectors drive market growth. Besides, growing industrial automation and the rising demand for autonomous industrial machines equipped with robotic vision boost the market size.
Citing the growing use of autonomous robots in image processing and customized production solutions, MRFR expects the global robotic vision market valuation to reach USD 9 BN by 2023, growing with a 12% CAGR throughout the assessment period (2018–2023). Advances in futuristic machine learning and deep learning technologies are bringing opportunities for improved image processing and market development.
Robotic Vision Garners Significant Market Prominence
Robotic vision technology empowers manufacturers to solve automation-related challenges efficiently. The technology is also extensively used to locate three dimension robot-handled objects all from a single image when accuracy and speed matter.
A robotic vision system comprising one or more cameras connected to a computer enables machines to see, identify, navigate, and inspect tasks. The computerized processing software program helps the robot interpret what it sees and complete the specified task specified by the manufacturing facility’s staff.
Additional elements such as lighting, image sensors, communications devices, and other components add to the machine’s overall capabilities. The capabilities of robotic vision systems are seemingly endless.
Today, robots are used in manufacturing facilities to handle various tasks, such as product sorting, measuring, depalletizing, and many more. Robotic vision systems are used in product assembly lines to switch between parts and products simultaneously.
The system allows greater flexibility in constructing different units on the same line without needing a program. Using robotic systems helps improve the efficiency and productivity of an assembly plant.
Besides, the precision and productivity that the robotic vision system offers increase its market demand. Incorporating a vision system with a robot offers additional benefits, such as reduced downtime to install parts in the assembly process and the ability to pick the correct part from assorted products.
Additionally, the vision system provides greater flexibility to robots enabling them to complete various tasks rather than requiring a specific placement. The improved adaptability allows the assembly process to move quickly without being sunk by imprecise placements. Vision systems also offer greater quality control, allowing robots to determine defects that would otherwise go unnoticed by humans.
Applications of Robotic Vision
Automation in manufacturing sectors is boosted by the growing competition among manufacturers, encouraging them to enhance productivity and reduce overall operating costs. Robots with a sound vision system account for variables in conducting in-process inspection operations in different work environments. Industrial robots fitted with sophisticated vision systems become even more dynamic.
The flexibility of robotic vision systems drives their applications. Robotic vision systems are used in various activities, including taking measurements, scanning & reading barcodes, inspecting & examining the surface and engine parts, orientating modules and directing & verifying different parts, detecting product defects, etc.
3D, the Future of Vision Technology
Industrial automation has become imperative, but without 3D Vision, many automation tasks are impossible. Fast, accurate 3D Vision allows robots to work in unstructured or varying environments.
Deploying 3D Vision is more cost-effective and quicker than any other technique in automated manufacturing lines. 3D vision technology comes with quick and easy installation features and offers unmatched speed & accuracy essential for integrators or manufacturers.
Room for Improvements in Robotic Vision
Robotic Vision has evolved dramatically, reaching a level of sophistication in handling complex and demanding tasks. However, there remains enormous scope for improvement, particularly in identifying individual objects from a bin of assorted products, where some parts get hidden behind others.
Robotic vision systems are designed to pick the correct part, but identifying occluded parts requires large datasets of objects. To respond to such scenarios, many types of research are ongoing currently to identify new methods that can enable robotics to detect specific objects without explicitly reasoning over occluded areas.
It can reduce data collection efforts and improve performance in a complex environment. These new methods can enable occlusion reasoning by introducing hierarchical occlusion modelling (HOM) in their system. An assigned hierarchy to multiple extracted features and their prediction order can validate the HOM scheme’s effectiveness and help achieve state-of-the-art performance.
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