Interacting with a multi AGV system

This paper introduces a novel Human Machine Interface (HMI) that allows users to interact with a fleet of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The interface is developed for providing operators with information regarding the fleet of AGVs, and the status of the industrial environment. Information is provided in an intuitive manner, utilizing a three-dimensional representation of the elements in the environment. The HMI also allows operators to influence the behavior of the fleet of AGVs, manually inserting missions to be accomplished

Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Due to increasing demand for unique products, large variety in product portfolios and the associated rise in individualization, the efficient use of resources in traditional line production dwindles. One answer to these new challenges is the application of matrix-shaped layouts with multiple production cells, called Matrix Production Systems. The cycle time independence and redundancy of production cell capabilities within a Matrix Production System enable individual production paths per job for Flexible Mass Customisation. However, the increased degrees of freedom strengthen the need for reliable production control systems compared to traditional production systems such as line production. Beyond reliability a need for intelligent production within a smart factory in order to ensure goal-oriented production control under ever-changing manufacturing conditions can be ascertained. Learning-based methods can leverage condition-based reactions for goal-oriented production control. While centralized control performs well in single-objective situations, it is hard to achieve contradictory targets for individual products or resources. Hence, in order to master these challenges, a production control concept based on a decentralized multi-agent bidding system is presented. In this price-based model, individual production agents - jobs, production cells and transport system - interact based on an economic model and attempt to maximize monetary revenues. Evaluating the application of learning and priority-based control policies shows that decentralized multi-agent production control can outperform traditional approaches for certain control objectives. The introduction of decentralized multi-agent reinforcement learning systems is a starting point for further research in this area of intelligent production control within smart manufacturing

Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles

Internationalization of markets and climate change introduce multifaceted challenges for modern supply chain (SC) management in the today's digitalisation era. On the other hand, Automated Guided Vehicle (AGV) systems have reached an age of maturity that allows for their utilization towards tackling dynamic market conditions and aligning SC management focus with sustainability considerations. However, extant research only myopically tackles the sustainability potential of AGVs, focusing more on addressing network optimization problems and less on developing integrated and systematic methodological approaches for promoting economic, environmental and social sustainability. To that end, the present study provides a critical taxonomy of key decisions for facilitating the adoption of AGV systems into SC design and planning, as these are mapped on the relevant strategic, tactical and operational levels of the natural hierarchy. We then propose the Sustainable Supply Chain Cube (S2C2), a conceptual tool that integrates sustainable SC management with the proposed hierarchical decision-making framework for AGVs. Market opportunities and the potential of integrating AGVs into a SC context with the use of the S2C2 tool are further discussed

New Solution of Abstract Architecture for Control and Coordination Decentralized Systems

This paper contains a new approach that combines the advantages and disadvantages of suppressing hierarchical and heterarchical control architectures, creating a semi-heterarchical (holonic) control architecture. The degree of subordinate unit autonomy changes dynamically, depending on the presence of a system disruption, and its scope allows for a smooth transition from hierarchical to heterarchic control architecture in subordinate units. We have proposed a representation of the dynamic degree of autonomy and its possible application to subordinate units, which are, in our case, one-directional Automated Guided Vehicles (AGVs) and are guided by magnetic tape. In order to achieve such a semi-heterarchic management architecture with a dynamic degree of autonomy, approaches such as smart product, stymergic (indirect) communication, or basic principles of holon approach have been implemented

Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda

Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehousing, cross-docks, terminals, and hospitals. Their advanced hardware and control software allow autonomous operations in dynamic environments. Compared to an automated guided vehicle (AGV) system in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs, AMRs can communicate and negotiate independently with other resources like machines and systems and thus decentralize the decision-making process. Decentralized decision-making allows the system to react dynamically to changes in the system state and environment. These developments have influenced the traditional methods and decision-making processes for planning and control. This study identifies and classifies research related to the planning and control of AMRs in intralogistics. We provide an extended literature review that highlights how AMR technological advances affect planning and control decisions. We contribute to the literature by introducing an AMR planning and control framework t

Research and development of an intelligent AGV-based material handling system for industrial applications

The use of autonomous robots in industrial applications is growing in popularity and possesses the following advantages: cost effectiveness, job efficiency and safety aspects. Despite the advantages, the major drawback to using autonomous robots is the cost involved to acquire such robots. It is the aim of GMSA to develop a low cost AGV capable of performing material handling in an industrial environment. Collective autonomous robots are often used to perform tasks, that is, more than one working together to achieve a common goal. The intelligent controller, responsible for establishing coordination between the individual robots, plays a key role in managing the tasks of each robot to achieve the common goal. This dissertation addresses the development of an AGV capable of such functionality. Key research areas include: the development of an autonomous coupling system, integration of key safety devices and the development of an intelligent control strategy that can be used to govern the operation of multiple AGVs in an area