Before the formal popularization of robot palletizing, factories will package and distribute products manually, which will increase the risk of injury to workers. However, with the continuous progress of technology, factories will use more flexible robot palletizing for product packaging and distribution.
In the past, workers would cause additional injuries by repeatedly lifting large or heavy objects, and the daily work area would also cause injuries to workers. Now the factory will use robot palletizing for daily stacking work and place protective devices in the work area outside the feed conveyor belt, which can reduce the possibility of injury to workers.
At the same time, the robot palletizing has a good accuracy, and they will complete the conveying work of products according to the program written by workers every time. At the same time, the factory is equipped with arm end tools for robot palletizing, which can effectively handle bags, pails, cardboard boxes and heavy handbags; and in the process, the shell of products will not be damaged.
The beginning of control design is to present an abstraction of the real world, a model, to interpret sensor readings and make decisions. As long as the reality acts according to the assumptions of the model, it can make a good guess and play a controlling role. However, once the real world deviates from these assumptions, it will no longer be able to make correct guesses and control will be lost. Usually, once control is lost, control cannot be regained. (unless it is restored by some kind external force.)
This is one of the main reasons why robot palletizing programming is so difficult. I often see videos of new research robots in the laboratory, which show excellent agility, navigation or teamwork spirit, and I would like to ask, "why not use it in the real world?" Well, the next time you see a video like this, take a look at how tightly controlled the lab environment is.
In most cases, these robots can only perform these impressive tasks as long as the environmental conditions remain within the narrow limits of their internal models. Therefore, a key to the development of robotics is to develop more complex, flexible and robust models, which are limited by available computing resources.