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

Estimating the size of a multi-agent system (MAS) is a fundamental task with applications in routing, resource management, distributed coordination, etc. Achieving accurate network size estimation in MASs is challenging due to the absence of a central authority, lack of global identifiers, dynamic network changes, communication constraints, etc. This paper investigates the applicability of distributed consensus algorithms with asymptotic convergence for estimating MAS size. Specifically, we examine the average consensus algorithm using four different weighting schemes: Maximum Degree (MD), Metropolis-Hastings (MH), Best Constant (BC), and Convex Optimized (OW) weights. Experimental results on a random geometric graph demonstrate that all weighting schemes enable agents’ internal states to asymptotically converge to the value 1/n, where n is the total number of agents. Differences among the schemes are observed in convergence speed and early-stage oscillations, with BC providing the fastest convergence and OW exhibiting more initial fluctuations. Overall, the study confirms that distributed average consensus algorithms with the examined weights can effectively estimate network size in MASs.

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.

Nowadays the world is surviving the fourth industrial revolution named Industry 4.0, which combines physical world of real things with their “virtual twins”. The man with his intellect, creativity and will lies beyond this ideology. Now the image of a new paradigm of Industry 5.0 could be seen. It involves the penetration of Artificial Intelligence in man’s common life, their “cooperation” with the aim of enhancing the man capacity and the return of the man at the “Centre of the Universe”. The paper outlines modern technologies – from IoT up to emergent intelligence, being developed in organizations where authors work. The convergence of these technologies, according to our minds, will provide the transformation from Industry 4.0 to Industry 5.0.