Distributed multi-robot teams are increasingly used for optimal coverage of domains with unknown density distributions, often modeled with Gaussian Processes (GPs). However, current methods rely on data sharing, raising privacy concerns and computational issues. We propose a Federated Learning (FL) approach that enables collaborative training of GP models without sharing raw data. To enhance scalability and efficiency, we introduce a filtering strategy that selects relevant data samples, minimizing computational load. Realistic simulations emulating real world scenarios demonstrate the effectiveness of our method in achieving robust environmental estimates with minimal data sharing and reduced complexity.

Multirobot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multirobot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian processes (GPs) to model the spatial field and balance the tradeoff between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where theexploration-exploitationtradeoff is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.

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 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.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 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 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.

This paper presents a coverage based control algorithm to coordinate a group of autonomous robots. Most of the solutions presented in the literature rely on an exact Voronoi partitioning, whose computation requires complete knowledge of the environment to be covered. This can be achieved only by robots with unlimited sensing capabilities, or through communication among robots in a limited sensing scenario. To overcome these limitations, we present a distributed control strategy to cover an unknown environment with a group of robots with limited sensing capabilities and in the absence of reliable communication. The control law is based on a limited Voronoi partitioning of the sensing area, and we demonstrate that the group of robots can optimally cover the environment using only information that is locally detected (without communication). The proposed method is validated by means of simulations and experiments carried out on a group of mobile robots.

This paper deals with the traffic management of multiple Automated Guided Vehicles (AGVs) in an automatic factory or warehouse. We propose innovative methods, evolved from the studies previously conducted in [1], to coordinate a fleet of AGVs in an industrial environment, and we describe the methodologies developed to build a complete traffic manager software. The software is based on a multi-layer control architecture: a higher-level layer useful to model the traffic of vehicles among the different areas of the warehouse, a middle layer which acts as a bridge between the traffic model and the path planner, and a lower-level layer which represents the roadmap itself and, hence, defines the optimal path to be followed by each vehicle. Finally, the AGVs movement coordination is managed separately through a centralized control in order to avoid conflicts and deadlocks.The aim is to make theoretical methods applicable to a real environment facing the usual problems related to the industrial applications, which have been overlooked in [1]. Indeed, the roadmap is usually constrained by the plant layout, especially in medium size factories, and paths can not be arbitrarily defined. Hence, the aim is to realize a reliable and robust software able to manage real scenarios, allowing the traffic management of multiple AGVs. The proposed method aims at flexibility by considering a coordination strategy not based on assumptions and ad-hoc rules.