In this paper we address the problem of assigning a set of tasks to a set of Automated Guided Vehicles (AGVs), in a conflict-free manner. Specifically, we consider a system of multiple AGVs, moving along a predefined roadmap, and utilized for transportation of goods in automated warehouses. Sequential application of task assignment and path planning often gives rise to pathological situations, such as deadlocks, in which AGVs block each other, thus preventing tasks completion. In this paper we propose a method for assigning tasks while taking into account the subsequent path planning, encoding possible conflicts into a conflict graph, that is subsequently utilized for defining constraints of an optimization problem. Simulations are performed on maps of real industrial environments, to compare the proposed method with traditional task assignment.

In this paper we consider the problem of coordinating the motion of a group of Automated Guided Vehicles (AGVs) utilized in industrial environments for logistics operations. In particular, we consider a hierarchical coordination strategy, where the environment is partitioned into sectors: coordination on the top layer defines the sequence of sectors to be traveled, while coordination on the bottom layer deals with traffic management inside each sector. In this paper we introduce a novel partitioning algorithm, that defines the sectors in a dynamic manner, taking into account both the shape of the (generally non-convex) environment, and the current distribution of the AGVs. This is achieved exploiting a clustering algorithm, and subsequently defining the sectors based on the geodesic distance.

This paper describes innovative sensing technologies and control techniques, that aim at improving the performance of groups of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. We explicitly consider the situation where the environment is shared among AGVs, manually driven vehicles, and human operators. In this situation, safety is a major issue, that needs always to be guaranteed, while still maximizing the efficiency of the system. This paper describes some of the main achievements of the PAN-Robots European project.

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. Note to Practitioners-The motivation of this work grows from the need to increase the flexibility and efficiency of current multi-AGV systems. In particular, in the current state-of-the-art the AGVs are guided through a manually defined roadmap with ad-hoc strategies for coordination. This is translated into high setup time requested for installation and the impossibility to easily respond to dynamic changes of the environment. The proposed method aims at managing all the design, setup and control process in an automatic manner, decreasing the time for setup and installation. The flexibility is also increased by considering a coordination strategy for the fleet of AGVs not based on manual ad-hoc rules. Simulations are performed in order to compare the proposed approach to the current industrial one. In the future, industrial aspects, as the warehouse management system, will be integrated in order to achieve a real and complete industrial functionality.

This paper deals with decentralized coordination of Automated Guided Vehicles (AGVs). We propose a hierarchical traffic control algorithm, that implements path planning on a two layer architecture. The high-level layer describes the topological relationships among different areas of the environment. In the low-level layer, each area includes a set of fixed routes, along which the AGVs have to move. An algorithm is also introduced for the automatic definition of the route map itself. The coordination among the AGVs is obtained exploiting shared resources (i.e. centralized information) and local negotiation (i.e. decentralized coordination). The proposed strategy is validated by means of simulations using real plant.

This paper deals with the automatic generation of a roadmap. We propose an approach to build a roadmap for Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The algorithm computes a roadmap in such a way that the coverage, the connectivity and the redundancy of the paths are maximized. In this way the flexibility and the efficiency of the AGV system can be increased. The proposed approach is validated by means of comparison with different roadmaps manually built in real plants.