Collaborative robots change the interaction mode between human and machine, and the unique value between people and machines can be effectively played. The market is more optimistic. When it comes to the era of "Industry 5.0", it will be the world of collaborative robots. . In order to speed up the popularization of collaborative robots, today's market is also working to lower its operating threshold, allowing more operators to put into use, and through the added value of artificial intelligence (AI), the value of robots in the factory continues to catch up with the development level of human beings. .
In general, industrial robots will be customized according to the operation content of the robots, and the basic parameters such as operation methods and design will be set by the manufacturers. Therefore, if the general factory operators do not have the professional background of robots, It is difficult to import and use by itself. Therefore, most of the factories rely on the talents with this background to assist the robots in tasks such as tuning and training.
In the past, there was almost no unified operation method among robot manufacturers because of the standard relationship. However, one of the selling points of collaborative robots in the future is that they are easy to operate and more flexible. For small and medium-sized enterprises that lack related resources, if it is difficult to operate. High, it is difficult to popularize in this field.
In order to reduce the threshold of robot use, Kawasaki Heavy Industries (KHI) and ABB have also cooperated in the future. The operation interface will be simplified by the unification of the operation interface, for example, the operation screen can be generalized, making it easier for the operator to use the robot. ABB said that it is hoped that the future collaborative robots will operate like a 3-year-old child. With a simple operation, no matter who can operate the robot.
When collaborative robots become popular, how to make the users of related backgrounds use them quickly is a very important issue. At present, the operation of the arm is guided through the teaching box or the human step, and the action track is recorded, so that the arm can repeatedly perform the fixed action, but the detailed action still needs to be controlled by the process.
Taiwan's Daming robots combine AR technology to develop space-to-air teaching, which is easier and more flexible than traditional robotic arm teaching methods. For example, the operator can directly combine the robot in the real situation with the "virtual" training track through the mobile phone lens. By sliding the path in the mobile phone screen, the operation direction of the arm can be controlled in real time.
Daming robots said that this way of airspace teaching is very suitable for large-area application scenarios, such as logistics and warehousing. If the goods are picked up in a high cargo rack, the operator can operate on the flat ground through the mobile device.
Machine learning accelerates the "smart" of collaborative robots
AI is reaching every industry, and manufacturing is no exception. However, if the scope is reduced to robot applications, the development of AI has not yet reached a mature stage. At present, the production line has been able to use the deep learning technology skillfully, and find out the fake products through image recognition, instead of the human eye for yield detection.
The development of AI combined with robots is expected in the future. German robot manufacturer Kuka believes that AI can help robots adapt to the uncertainties and changes brought about by the environment. Machine learning will come in handy for future changes in manufacturing trends, helping robots in the plant become more familiar with the professional skills required in the application field and quickly adapt to the manufacturing environment.
For example, in Kuka's research and development, robots can improve their ability to optimize during the downtime, deal with what happens with humans, and learn from experience, such as context-dependent optimization and configuration for algorithms. Through machine learning, the robot can know whether each action is successful or failed, or where to move to a location with a higher success rate.
According to experiments, the robot has a success rate of 90% after 8 hours of study, which is quite comparable to a skilled worker. Even by collecting sensors such as waveforms and currents during normal or abnormal operation, it is possible to detect subtle changes that are difficult for humans to perceive through machine learning, and to provide an early warning before the robot completely fails.
Building a new state of light-off factory with collaborative robots
The idea of ​​using a collaborative robot in a light-off factory seems to be unlikely, because in the usual impression, the lights-off factory takes no one to automate production, because no one needs to be illuminated. But collaborative machines can work with humans, and it seems impossible for both to exist at the same time.
However, Danish collaborative robot manufacturer Universal Robot (UR) believes that the collaborative value of collaborative robots should not be limited to human operations. Collaborative robots are also affordable, flexible and easy to use. These can also be applied to new ones. Type of light-off factory.
UR believes that most of the Taiwanese manufacturing industries are still small and medium-sized enterprises, but their automation budget is limited, and the scale of the factory is small. Although there is a need for upgrading automation or necessary, it is hard to consider the investment cost.
Today's market is moving towards a variety of manufacturing or application-changing manufacturing trends, and many SMEs are also facing difficulties in achieving the expected quality and delivery due to lack of work, not to mention the double manpower due to seasonal orders. Therefore, UR recommends that small and medium-sized manufacturers or start-ups can introduce turn-off automation for specific processes that include environments that are not suitable for humans or that are not safe enough for humans, such as extremely hot or toxic gases.
For example, UR, such as 3D printing company, allows collaborative robots to work at night, responsible for high-repetition, fast and accurate 3D printer loading and unloading tasks, and let the device use rate when the lights are still running at night. Increase by 3 times.
In this way, the collaborative robot can not only work with humans during the day, but also keep running during the night after the employees go home, plus its flexibility and ease of use, even small companies can use the lights Factory concept to create more efficient output value.
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