ROBOTPHOENIX
To create excellent quality with highly-sophisticated technology and to provide professional and highly efficient service.
With the rapid development of industrial automation, vision technology plays an increasingly important role in intelligent manufacturing. The robotic vision system, as an extension of the “eyes” of industrial robots, enables robots to recognize and locate targets, thereby performing various tasks such as picking, placing, and inspection. Below is a brief introduction to the classification of robot vision, the characteristics of various vision systems, and their application value in industrial automation, as well as an exploration of Robotphoenix’s advantages in vision technology.
Robot vision is a function that enables robots to recognize and locate targets in order to complete specific actions (such as placement, removal, etc.). By using vision sensors (typically 2D or 3D cameras) to capture images of the target, the data is analyzed and processed with algorithms, ultimately sending the positional information of the target to the robot for operation. It is an important means for industrial robots to perceive the world, enhancing their adaptability and flexibility in complex environments.
The 2D vision system is the most common form, mainly utilizing image sensors to capture two-dimensional images. By analyzing features such as area, shape, texture, and color of the target, along with 2D vision algorithms, it can achieve functions like target recognition, position detection, and size measurement.
Advantages: Lower cost; mature software; widely used.
Limitations: 2D vision algorithms cannot acquire spatial coordinates from 2D cameras; sensitive to lighting conditions, with image color and grayscale significantly affected by lighting.
The 3D vision system captures 3D data of the target using 3D cameras or 3D sensors, resulting in “point clouds.” Using 3D algorithms and features derived from point cloud data, it can guide robots or control automated devices to complete tasks. 3D vision can be used for picking scattered materials, depth object detection, 3D defect detection, and 3D measurement, with applications across industries like food and beverage, pharmaceuticals, chemicals, construction materials, cosmetics, metalworking, electronics manufacturing, packaging, and automotive.
Advantages: Can recognize the 3D shape, spatial position, and posture of objects. 3D vision provides not only 2D image information but also spatial data, which can be easily derived from 2D data using 2D-3D vision algorithms.
Disadvantages: Higher cost; greater computational resources required; system debugging is more complex.
Deep learning vision is a vision technology based on neural network models, which is a subfield of machine learning in artificial intelligence.
The key for deep learning is that learning much features information from dataset to promote prediction accuracy of model, by building Machine Learning model with lots of hidden layers and loading mass dataset. So, “deep model” is strategy, and “features learning” is purpose.
Advantages: Strong adaptability; high recognition accuracy; can handle complex and varied image tasks.
Disadvantages: Requires a large amount of data; training is complex and computationally intensive.
Robotphoenix has developed its own VisionPower Universal Intelligent Vision Platform. In complex vision systems, hardware and software require significant data interaction, and there are complex process control interactions. External hardware data such as PLCs, encoders, photoelectric sensors, etc. also need to participate in visual processing.
With dozens of independently developed image processing algorithms encapsulated, Robotphoenix has built a powerful vision analysis tool library. No programming is required—machine vision applications can be quickly developed through simple and flexible configuration.
VisionPower software opens a vision algorithm interface, supporting concurrent processing of 6 different types and brands of cameras.
2D vision algorithms: Background modeling algorithm, color extraction algorithm, Blob analysis algorithm, template matching algorithm, etc.
3D vision algorithms: 3D modeling algorithms, 3D object extraction algorithms, target 3D size detection algorithms, etc.
2D-3D Composite Algorithm: Composite background modeling algorithm, composite object extraction algorithm, etc.
Supports classification, positioning, segmentation and abnormal area algorithms, and the hardware configuration requirements of the operating platform are low, and the cost is easy to control.
These technological advantages enable Robotphoenix to provide leading vision solutions across various industry applications, better serving clients’ automation upgrade needs.
Vision systems help Delta robots, SCARA robots, and other industrial robots “see” targets, enabling them to perceive and respond to dynamic environments. Just like the collaboration between human “eyes,” “hands,” and “brain,” robotic vision (eyes) combines with the control system (brain) and robot arms (hands) to form a complete automation system, playing a crucial role in production line inspection, accurate positioning, and quality control.
In modern manufacturing, vision systems have become an indispensable part. Whether for high-precision electronic product inspection or automatic picking of scattered materials, robot vision is continuously pushing the boundaries of intelligent manufacturing.
With the advancements in artificial intelligence and deep learning, robot vision is continually evolving, empowering industrial robots with enhanced perception and decision-making capabilities. Robotphoenix, with its powerful vision platform and algorithm capabilities, injects new energy into industrial automation. In the future, vision systems will further integrate with industrial robots to build more efficient, intelligent, and flexible production systems.
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