Hierarchical Traffic Management of Multi-AGV Systems with Deadlock Prevention Applied to Industrial Environments

This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the presented control archi- tecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections,. . . ), a prede- fined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors. Note to Practitioners—This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to Manuscript received 31 January 2023; accepted 2 May 2023. This work was supported by the COLLABORATION Project through the Italian Ministry of Foreign Affairs and International Cooperation. This article was recommended for publication by Associate Editor L. Liu and Editor P. Rocco upon evaluation of the reviewers’ comments. (Corresponding author: Federico Pratissoli.) Federico Pratissoli and Lorenzo Sabattini are with the Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, 41121 Modena, Italy (e-mail: federico.pratissoli@unimore.it; lorenzo.sabattini@unimore.it). Riccardo Brugioni was is with RSEngineering S.r.l., 41053 Maranello, Italy (e-mail: brugioniriccardo@gmail.com). Nicola Battilani is with Industria Tecnologica Italiana S.r.l. (IT-I), 42122 Reggio Emilia, Italy (e-mail: nicola.battilani@it-i.it). This article has supplementary material provided by the authors and color versions of one or more figures available at https://doi.org/10.1109/ TASE.2023.3276233. Digital Object Identifier 10.1109/TASE.2023.3276233 be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the pre- sented control architecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections, . . . ), a predefined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors.