Abstract
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.
Highlights
On grazing land, there is a system in which a flock of several thousands of sheep that run about freely are controlled by only a few dogs called sheepdogs
The impact of blind zones during measurement of relative positions by light was small, so guidance could be achieved by ignoring this robot, but as the number of sheep robots increased, cases where the goal is invisible occurred with increasing frequency, leading us to conclude that their impact becomes impossible to ignore
This paper tries to demonstrate the sheepdog-type navigation, as as possible, which is based on the concept of a sheepdog guiding a large flock of sheep
Summary
There is a system in which a flock of several thousands of sheep that run about freely are controlled by only a few dogs called sheepdogs. Other research has proposed and verified many sheep and sheepdog models from the perspectives of degree of similarity to behavior of an actual sheepdog system, and guidance performance in guiding a flock of sheep [9,10,11,12,13,14]. The point we wish to emphasise here is that we intend to build the sheepdog system in as simple a way as possible For this purpose, we examine the blind-zone effect in the experiment after introducing minimal settings for the sheep model and sheepdog algorithm from. On the basis that the guidance impact of a dog robot is unable to perceive the goal results from the object having size rather than being modelled as a point, we proposed the following method: “Using another object that replaces the goal as a substitute goal”.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
