International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Industry 4.0 which creates “smart factories” present a recent trend in development. The area represents a merge of cyberphysical systems and Internet of Things, which aims to improve manufacturing technologies. Industry 4.0 strives to boost the algorithms and technologies used in industrial processes during the production processes, process preparations, and products delivery. Our intention is to improve the robotics transport system in factory floor. There are a lot of different research approaches in this area for further improvement. Our approach is to deal with multi-agent systems control, because of the great potential it has in practical applications in industrial robotics. The strive for minimizing the work time and maximizing the efficiency can be satisfied through the usage of multiple coordinated agents to achieve the end goal. The use of Automated Guided Vehicles (AGVs), combined with concepts for task planning of multiple agents broadened during the late 20th century. In this paper, the multi-agent system consists of several mobile robots, in other words platforms, which need to transport materials in a workhouse. The goal of each mobile platform is to carry the specified object to a set position. These appointed goals are not predefined and can be changed according to the needs of the user. Working in a dynamic environment, numerous agents with different tasks to complete can be exposed to many obstacles which may be the cause of accidents. For this reason, a careful path planning is required in such environments. The suggested path planning algorithm for this system is A*. A* is a fast path finder, which can navigate quite well in a planar environment, but it is not favorable for dynamical settings. Therefore, a combination of the A* algorithm with a collision avoidance method is proposed for overcoming these difficulties. By doing this, the A* algorithm is expanded to work in dynamical situations and can assure the convergence of any agent towards their goal. This fusion of both, the path finding algorithm and the collision avoidance method, can aid the cooperation of the agents and improve the efficiency of the system as a whole.

In control literature, one can easily find a variety of different examples for industrial control, where contemporary control algorithms are implemented. Surprisingly, there are not many known examples where the state-of-the-art control algorithms have been implemented in real-time control systems. Instead, researchers usually implement algorithms that are proven to be reliable, fast and easy to implement. One control solution that has been proven to satisfy all previously mentioned attributes is PID. However, despite all its good assets it has two major deficiencies. One of them is that it can’t adapt on the diversities caused by the variation occurring in the model parameters and still it can’t control nonlinear systems due to multiple operating points present there. Therefore, to deal with those weaknesses an improvement in PID control structure has been introduced in the form of supervisory mechanism (SM) which as a main constitute part has a quadratic polynomial model. Thus, the control value of the newly proposed PID algorithm is formed of two terms, the first one is the value calculated by standard PID and the second one is the value calculated by the SM. The quadratic model forming part of the SM is obtained based on the past value of the error. Nevertheless, the use of quadratic model introduces additional complexity into the PID controller. Furthermore, the quadratic model should be updated fast enough and also it has to describe the data adequately. These aspects are analyzed and discussed in details in this paper. Moreover, an algorithm is introduced which will guarantee that the data used for calculation of the quadratic model is suitable.