In the real world, there is a system in which a dog called a sheepdog stimulates part of a flock of sheep that are freely moving to guide them to a goal position. If we consider this system from the perspective of a control problem, it is an interesting control system: one or more sheepdogs, who act as a small number of controllers, are used to indirectly control many sheep that cannot be directly controlled. For this reason, there have been many studies conducted regarding this system; however, these studies have been limited to building numerical models or performing simulation analyses. Very little research has been done on building a working system. The point we wish to emphasise here is that we attempted to build the sheepdog system in as simple a way as possible. For the purpose, we introduce minimal settings for the sheep model and the sheepdog controller. In the process of building and testing an actual system, we noticed “an emergence of blind zone” because the robots possess size, or so-called cases where the objects in the blind zone cannot be observed because the object is in front. Using the existing method, as the number of sheep increases, it becomes impossible to perceive the goal position, i.e., emerge the goal-lost-situation. This results in the guidance task becoming impossible. As clear identification of the goal position is vital for guidance, we propose a method for cases in which the goal position is invisible. Using our method, the robot appropriately selects another object, and sets this object as the new target. We have confirmed through simulations that the proposed method can maintain guidance regardless of the number of sheep.
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