In a general-purpose car production workshop, every 60 seconds or 90 seconds, a car body is placed on the assembly line. If a robot stops during this process, the manufacturer will suffer a loss of $20,000 per minute. The pipeline's stop line will still evaporate millions of dollars. If the robot failure is caused by the entire workshop, the loss of the sky will be passed on to the supply chain, car dealers, car fleets and even individual car consumers. Like the fallen dominoes, these adverse effects are huge and far-reaching. In addition to software control capabilities, multi-machine collaboration also needs to face the hardware adaptation problem. In order to solve the adaptability problem, the Nabbot NRC control system supports one robot to configure different brand servos. Support for setting the servo brand model for each axis of each robot makes the expansion impossible. Whether it is Panasonic, Tamagawa, or the servos of brands such as Qingneng Dechuang, Delta, TECO, and Tuco, they can be adapted. Machined Aluminum Parts,Aluminum Parts Machining,Custom Aluminum Parts,Aluminum Cnc Machining Parts Wuqiang Xingdou International Trade Co., Ltd , https://www.wqxingdou.com
In the automated production line, the reason for the whole line of the robot strike was that there was no collaboration between the robot and the robot, and the concept of “multi-machine collaboration†came into being. In fact, the intensive assembly line will definitely bring the requirement of “multi-machine collaborationâ€, and there is a need for information sharing between the robot and the robot.
Undoubtedly, robots from stand-alone automation to production line automation are an irreversible trend, and "multi-machine collaboration" is the "inevitable product" of this trend.
Embrace trend
"Don't be an enemy of the trend!" At the press conference last year, Yang Li, director of intelligent application technology of Leisai, said that under the trend of single-machine automation to production line automation, the era of motion control "bus" has arrived.
From pulse control to bus control, from one controller to only one robot to one controller can control multiple robots, and changes in motion control make "multi-machine collaboration" possible. In a certain sense, the advent of the "bus era" of motion control means that the robot has already bid farewell to the era of "single-handedly fighting" and entered the "multi-machine cooperation" level.
From a conceptual point of view, the concept of "multi-machine collaboration" is different from the concept of "multi-process": "multi-process" is the agreement between us. You are the first one, I am the second, and I should not do it if I don't. "Multi-machine collaboration" is what you do when I can't finish it. The assignment and coordination of tasks between such robots has more advantages besides avoiding the full line of smashing caused by a robot strike.
Just like a dialogue between people, you can do a lot of things, let the machine and the machine create a dialogue, and you can do more things, which is the meaning of achieving "multi-machine collaboration."
A robot has a limit no matter how fast it is. For example, a parallel robot can currently grab 80 pieces in one minute*, while SCARA robots can grab 60 pieces, and six-axis robots can only grab 20-30 pieces. However, if more than one robot cooperates to complete the task, it will inevitably bring about an increase in efficiency.
In addition, in the construction process of the smart factory, the hard part is to integrate the core technology equipment such as robots into the production line, because the robot is not very convenient to arrange into the production line like a human. As long as the robot can communicate like a human, the production line arrangement can become more flexible.
According to industry insiders, traditional industrial robots need to be equipped with a motor cabinet. After multi-machine cooperation, a controller can integrate many robots, which reduces costs and saves space. Compared with the single robot, the multi-robot system has high efficiency in completing tasks, high task complexity, fast information transmission, and more accurate positioning information.
Smell the wind
In the past two years, the trend of multi-machine collaboration has become more and more obvious. Wang Yuechao, chairman of Birkent, said that there are very few stand-alone operations, and basically all need multi-model and multi-task cooperation.
In recent times, for the application of parallel robots, Birken has developed a vision-based multi-robot task assignment algorithm, using the vision system to obtain environmental information through local observation, and selecting task execution from state transition equations to achieve multi-robots from local to global. Coordinated allocation of systems.
Zhong Xing believes that "multi-machine collaboration" mainly solves three problems on the production line: information communication between robots, intelligent judgment of robots on work results, and task assignment of the entire production line.
Previously, Zhongxing Xing developed a “multi-machine collaboration†workstation based on the application of SCARA. By implementing SCARA robots with AVS vision system to achieve synchronous follow-sorting, the vision system can realize dynamic recognition and *positioning, thus enabling robots to achieve multi-machine cooperation.
As a leading robot company in the country, Eston is also one of the first companies to realize the wind direction. In fact, the Eston Automation core component product line has undergone a strategic transformation from AC servo system to motion control system solution, and the business model is achieving full sublimation from single-axis-single-unit.
At last year's trade fair, Eston's sports control brand Trio also brought intelligent control unit solutions based on small workstations. According to Zhu Lei, general manager of Eston Automation Intelligent Control Unit Division, ESTUN intelligent control unit solution is based on ESTUN's Trio controller platform, which combines ESTUN robot, AC servo system and vision system to realize a controller. Collaborative control of multiple robots, servo axes, vision systems, logic control, to provide customers with a comprehensive automation solution.
Under the trend of robot multi-machine collaboration, Nabbit independently developed the control system solution NRC controller. According to Zhang Xiaolong, general manager of Nabate, one controller of Nabbot supports more than eight robots at the same time, and can deploy multiple morphing robots in the same system to truly achieve multi-machine collaboration. The controller can also easily switch between robots and teach operations, making multi-machine collaboration as simple as stand-alone control, and can be switched freely with one-button operation.
The application advantages of multi-machine collaboration are mainly as follows: 1. Effectively reduce costs and reduce initial investment, 4 robots can reduce control system costs by 75%; 2. Group control cooperation process, improve production tempo and production efficiency by more than 30%; Third, save volume: save more than 50% of the floor space.
Opportunities and challenges coexist
Although the bus motion control system brings "multi-machine collaboration" possible, it also needs to face new problems and challenges.
As the general manager of Nabate Zhang Xiaolong said, there are still many difficulties to overcome in multi-machine cooperation. One control system of pulse control can only control one axis and cannot expand. However, after the bus, it can expand multi-axis, but expand more. After the axis, the complexity of the software also rose sharply.
In fact, multi-machine collaboration has always been a pain point in the industry, because it involves many levels, the specific process is divided into the following parts: multi-machine interconnection - machine vision perception environment - access task information - multi-machine strategy - strategy Perform feedback on results. The task allocation is the basis for the smooth operation of the entire multi-robot system.
Different from "human-machine collaboration", which emphasizes robot-human collaboration, "multi-machine collaboration" emphasizes the collaboration between robots and robots. "Multi-machine collaboration" is a "one-to-many" relationship, which means The relationship of "multi-machine collaboration" is much more difficult to deal with than "human-machine collaboration."
In Zhang Xiaolong's view, multi-machine collaboration requires multiple machines to operate independently. One machine's error alarm does not affect the operation of other machines. This mainly reflects the ability of software control.
The software capability is exactly what Burkent is breaking. The vision-based multi-robot task assignment algorithm developed by Birken is equipped with the self-developed BeMotion motion controller, which beats the material density acquired by the visual real-time and the grab speed of multiple robots. The actual factors such as the real-time speed of the conveyor belt are used as the model input factors. The convolutional neural network + decision tree is used as the algorithm model. Unsupervised learning is carried out through a large number of training samples, and the accuracy of the algorithm model is continuously improved. Assigned to multiple robots, the material is completely and orderly captured and multiple robots can be used reasonably and efficiently.
When a robot in the task pooling system fails, the task assignment algorithm automatically assigns the robot's tasks to the grab tasks of other normal robots to ensure normal grab efficiency. When the faulty robot returns to normal, the other The robot automatically assigns the task back to the original failed robot to continue the crawling task.
The Birkent multi-machine collaborative task assignment algorithm can be embedded in the controller or embedded in the vision system. "We use this function as an executable file, and it can be used everywhere. The advantage of this is that this function does not pick up the hardware, and does not limit the brand." Wang Yuechao said.
Judging from the entire smart factory, Zhu Lei believes that the real difficulty in building a smart factory is how to refine so many equipment, data and even purely digital things in the factory into information. The intelligent control unit solution realizes multi-functions such as motion control, robot control, logic control, visual control and motion simulation. Motion simulation improves efficiency, multi-machine synergy reduces costs, free wiring saves space, and soft and hard are easier to use. To effectively enhance the user experience.