Effective path planning for multi-vehicle autonomous systems is essential to guarantee safe and efficient operation in industrial environments composed of both structured and unstructured settings. In the proposed framework, the overall environment is explicitly modeled as the integration of static and dynamic areas, each exhibiting distinct spatial and temporal properties. Static areas correspond to structured zones, such as corridors or production lines, and are represented by roadmaps with fixed topology. Dynamic areas, instead, represent unstructured portions of the environment and are modeled as gridmaps to enable adaptive navigation under changing occupancy conditions. Building upon this representation, a hybrid roadmap unifying static and dynamic areas is proposed, with a hierarchical control architecture for coordinated multi-vehicle navigation. The high-layer planner determines the optimal sequence of areas to traverse by solving a multi-objective optimization (MOO) problem that balances efficiency, safety, and traffic-related factors. On the other hand, the low-layer planner generates feasible paths within each area according to its structural constraints. The MOO formulation is embedded in a bi-level optimization framework, where the outer-level employs a quasi-adaptive self-tuning process for objective weights, addressing the limitations of subjective or suboptimal weight selection. Experimental validation across two realistic industrial layouts demonstrates improved mission efficiency, reduced travel-time variability, and scalability with fleet size, confirming the effectiveness of the proposed method for Industry 4.0 applications.

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.

Information exchange is crucial for optimal coordination of robots, but a link may not always be available among agents to share data. For this reason, this paper presents a decentralized solution for flocking control, leveraging state and uncertainty estimation of undetected robots. A neural network is trained to mimic a state estimator, also providing information about the uncertainty of the estimate. This uncertainty is used to weigh the contribution of the estimate in taking actions for coordination. Using Control Barrier Functions and Control Lyapunov Functions, we define an optimization problem to find an optimal control input to reproduce collective motion observed in nature. We evaluate both the learned estimator and the control strategy with extensive simulations.

The global population is growing at an unprecedented pace, resulting in an increase in the number of people affected by cognitive decline. This paper presents a MR application for older adults with cognitive decline, aiming to stimulate cognitive and motor functioning and promote active ageing. Developed using Unity and deployed to HoloLens 2, the system allows users to interact with holograms through a series of engaging games. Before having elderly people with cognitive decline test the MR approach, preliminary studies were conducted to assess both its feasibility and usability. The results are encouraging, with participants reporting the approach is easy to learn and perform. They also felt confident and successful in accomplishing what they were asked to do. The immersive nature of MR has the potential to transform ageing into an experience filled with opportunities rather than limitations.

The integration of robots into human-shared environments has driven advancements in social robotics and natural Human-Robot Interaction. A key challenge in this field is to enable robots to interpret and respond to social cues, ensuring fluid, context-aware, and socially acceptable interactions. To address this, we propose a two-layer architecture for social navigation. The high-level reasoning layer utilizes Large Language Models to interpret contextual and environmental cues, generating constraints for navigation. These constraints are then enforced by the low-level layer, which employs Control Barrier Functions to ensure smooth and socially compliant robot movements. We validate our approach in a dynamic simulation environment, demonstrating effective constraint enforcement and socially acceptable navigation behavior.

In recent decades, the detection of fatigue has been largely studied to improve safety and performance in various domains such as healthcare, transportation, and manufacturing. In fact, fatigue significantly affects cognitive and physical performance. There are numerous studies on the detection of fatigue, but most of them focus on binary classification (i.e., fatigue vs. resting state) or different levels of fatigue intensity, without distinguishing between specific fatigue types.This work aims to discriminate between cognitive and physical fatigue, and proposes the use of physiological signals to classify four distinct conditions: rest, cognitive fatigue, physical fatigue, and combined cognitive and physical fatigue. We analyze data from cardiac, eye, electrodermal, and electromyographic activity. We consider different feature selection methods, including correlation analysis, principal component analysis and sequential forward floating search method. Ultimately, we classify them using state-of-the-art machine learning methods.The highest classification accuracy (86.823%) is obtained from a support vector machine method using a selection of the features extracted from cardiac and electromyographic sensors.This study highlights the potential for real-time fatigue classification, which can enhance the adaptability of automated systems to human needs, particularly in high risk environments like transportation and healthcare. Furthermore, the findings suggest that fatigue monitoring can be effectively conducted with minimal sensor requirements, contributing to the design of more efficient wearable sensor systems.

Industry 5.0 embodies the vision for the future of factories, emphasizing the importance of sustainable industrialization and the role of industry in society, through the key concept of placing the well-being of workers at the center of the production process. Building upon this vision, we propose a new paradigm to design human-centric industrial applications. To this end, we exploit Digital Twin (DT) technology to build a digital replica for each entity on the shop floor and support and augment interaction among workers and machines. While so far DTs in automation have been proposed for machine digitalization, the core element of the proposed approach is the Operator Digital Twin (ODT). In this scenario, biometrics allows to build a reliable model of those operator’s characteristics that are relevant in working contexts. Biometric traits are measured and processed to detect physical, emotional, and mental conditions, which are used to define the operator’s state. Perspectively, this allows to manage and monitor production and processes in an operator-in-the-loop manner, where not only is the operator aware of the state of the plant, but also any technological agent in the plant acts and reacts according to the operator’s needs and conditions. In this paper, we define the modeling of the envisioned ecosystem, present the designed DT’s blue-print architecture, discuss its implementation in relevant application scenarios, and report an example of implementation in a collaborative robotics scenario.Note to Practitioners—This paper was motivated by the problem of designing human-cyber-physical systems, where production processes are managed by concurrently taking into account operators, machines and plant status. This answers the needs of the novel Industry 5.0 paradigm, which aims to enhance social sustainability of modern factories. To this end, we propose an architecture based on digital twins that allows to develop a digital layer, detached from the physical one, where the plant can be monitored and managed. This allows the creation of a digital ecosystem where machines, operators, and the interactions among them are represented, augmented, and managed. We discuss how the proposed architecture can be applied to three relevant scenarios: remote training and maintenance, line operation and line supervision. Moreover, the implementation in a collaborative robotics scenario is presented, to provide an example of the proposed architecture can be implemented in industrial scenarios.

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.

In the era of Industry 4.0, the importance of human-robot collaboration (HRC) in the advancement of modern manufacturing and automation is paramount. Understanding the intricate physiological responses of the operator when they interact with a cobot is essential, especially during programming tasks. To this aim, wearable sensors have become vital for real-time monitoring of worker well-being, stress, and cognitive load. This article presents an innovative dataset (SenseCobot) of physiological signals recorded during several collaborative robotics programming tasks. This dataset includes various measures like ElectroCardioGram (ECG), Galvanic Skin Response (GSR), ElectroDermal Activity (EDA), body temperature, accelerometer, ElectroEncephaloGram (EEG), Blood Volume Pulse (BVP), emotions and subjective responses from NASA-TLX questionnaires for a total of 21 participants. By sharing dataset details, collection methods, and task designs, this article aims to drive research in HRC advancing understanding of the User eXperience (UX) and fostering efficient, intuitive robotic systems. This could promote safer and more productive HRC amid technological shifts and help decipher intricate physiological signals in different scenarios.

Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being. To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced. Though the analysis of these biosignals, researchers can determine whether the person is feeling mental fatigue, physical fatigue or a combination of both. This paper introduces MePhy, a comprehensive dataset containing various biosignals, gathered while inducing different fatigue conditions, in particular mental and physical fatigue. Among the biosignals closely associated with stress situations, we chose: eye activity, cardiac activity, electrodermal activity (EDA) and electromyography (EMG). Data were collected using different devices, including a camera, a chest strap and different sensors from the BITalino kit.

Efficiently navigating and manipulating objects in complex environments is a fundamental challenge in robotics. This paper presents a novel approach to streamline object-pushing tasks by integrating Behavior Trees (BT) to coordinate a control and planning framework. The proposed system optimizes the execution of tasks involving the pushing of objects while ensuring adaptability to varying scenarios.Our approach employs BTs to encapsulate high-level task specifications and decision-making processes, facilitating a flexible and intuitive representation of robot behavior. By seamlessly integrating BT technology with a coordinated control and planning system, we enable the robot to make real-time decisions and adapt to dynamic environments.We present experimental results demonstrating the effectiveness of our approach, highlighting its ability to improve task execution efficiency and adaptability.

Recently, electroencephalographic (EEG) signals have been used to enhance Human-Robot Interaction (HRI). In particular, Error-Related Potentials (ErrPs) have been exploited since very few years. These potentials are evoked when there is a mismatch between the command given by the subject and the movements of the robot, or if the user’s expectation is different from the robot or other human behavior. These signals can be used to improve and customize the robot system, as feedback to better adapt the robot to human needs. This work aims to investigate and detect the ErrPs during different interaction tasks. We set up an experiment divided into five different tasks, where every task has 120 events with a 25%-35% probability of error. The robot used in the experiment is a Baxter robot and the commands from the subject to the robot are sent in two different ways: with a keyboard or with a motion capture device. This work aims to reproduce a simplified teleoperated pick and place task. However, the achieved results do not allow to correctly identify the ErrPs, but exhibit only some minor differences between trials with and without errors. Hence, we here analyze the reasons behind such negative results, focusing on the challenges of the structure and the setup of the experiment. We analyze the possible problems and provide some recommendations to overcome them in similar use cases.

Recently, electroencephalographic (EEG) signals have been used to design and enhance human-robot interaction (HRI). In particular, error-related potentials (ErrPs) have been leveraged since very few years. These potentials can be used to provide feedback to the robot about any mismatch between the user’s expectations and the robot’s behavior, during interaction tasks. In this process, the correct classification of ErrPs is crucial, which, in turn, relies on the reliability of the process for extraction and selection of signal features. In this work, we consider an extensive list of possible features and perform a statistical analysis to assess their discriminative power for ErrP analysis. The aim is to reduce the number of features used for classification while retaining the most relevant ones only. Overall, the outcome of our study shows that some parameters have relevant importance compared to others, (i.e. temporal features, frequency features, signal processing features, and some wavelet transform coefficients), while some of the features used in existing works are not useful since they have low discriminative power.

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.

Communication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This article investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system’s spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective.

In this article, we develop an algorithm for resilient path planning, where a team of robots must navigate in a resilient formation such that they achieve$F$-resilience, meaning they can coordinate in the presence of up to$F$adversaries. Resilient formations are those having high connectivity often achieved by driving robots close together. Unfortunately, the objective of maintaining resilience can often times conflict with achieving collision and obstacle avoidance. We seek to provide safe navigation while maintaining resilience by employing a local controller that uses control barrier functions (CBFs). CBF-based formulations are amenable to satisfying multiple objectives, but can be prone to deadlock if any of the objectives conflict with each other. Furthermore, it is difficult to know a priori where this may occur in a given environment. To this end, we 1) characterize when the environment will force a tradeoff between safe navigation and resilience, and 2) develop an algorithm that derives a new representation of the environment in which areas where resilience cannot be provably guaranteed are blocked off. This algorithm can be used to plan a path through an environment that always provably admits a resilient formation. If the algorithm cannot find such a path, an alternative CBF is proposed where resilience can be treated as asoft constraint. For this case, a nested form of the CBF is executed and a critical gain is derived that provably prioritizes navigation over resilience while resilience is not attainable. Finally, in addition to simulation results, we run hardware experiments with six GoPiGo differential-drive robots that achieve$F$-resilient consensus while navigating through a cluttered environment, to showcase the applicability of our methods in the presence of adversaries.

The topology of a robotic swarm affects the convergence speed of consensus and the mobility of the robots. In this paper, we prove the existence of a complete set of local topology manipulation operations that allow the transformation of a swarm topology. The set is complete in the sense that any other possible set of manipulation operations can be performed by a sequence of operations from our set. The operations are local as they depend only on the first and second hop neighbors' information to transform any initial spanning tree of the network's graph to any other connected tree with the same number of nodes. The flexibility provided by our method is similar to global methods that require full knowledge of the swarm network. We prove the existence of a sequence of transformations for any tree-to-tree transformation, and derive sequences of operations to form a line or star from any initial spanning tree. Our work provides a theoretical and practical framework for topological control of a swarm, establishing global properties using only local information.

The growing spread of robots for service and industrial purposes calls for versatile, intuitive and portable interaction approaches. In particular, in industrial environments, operators should be able to interact with robots in a fast, effective, and possibly effortless manner. To this end, reality enhancement techniques have been used to achieve efficient management and simplify interactions, in particular in manufacturing and logistics processes. Building upon this, in this paper we propose a system based on mixed reality that allows a ubiquitous interface for heterogeneous robotic systems in dynamic scenarios, where users are involved in different tasks and need to interact with different robots. By means of mixed reality, users can interact with a robot through manipulation of its virtual replica, which is always colocated with the user and is extracted when interaction is needed. The system has been tested in a simulated intralogistics setting, where different robots are present and require sporadic intervention by human operators, who are involved in other tasks. In our setting we consider the presence of drones and AGVs with different levels of autonomy, calling for different user interventions. The proposed approach has been validated in virtual reality, considering quantitative and qualitative assessment of performance and user's feedback.

Recent advances in robotics have allowed the introduction of robots assisting and working together with human subjects. To promote their use and diffusion, intuitive and user-friendly interaction means should be adopted. In particular, gestures have become an established way to interact with robots since they allow to command them in an intuitive manner. In this article, we focus on the problem of gesture recognition in human–robot interaction (HRI). While this problem has been largely studied in the literature, it poses specific constraints when applied to HRI. We propose a framework consisting in a pipeline devised to take into account these specific constraints. We implement the proposed pipeline considering, as an example, an evaluation use case. To this end, we consider standard machine learning algorithms for the classification stage and evaluate their performance considering different performance metrics for a thorough assessment.

This work addresses the problem of transporting an object along a desired planar trajectory by pushing with mobile robots. More specifically, we concentrate on establishing optimal contacts between the object and the robots to execute the given task with minimum effort. We present a task-oriented contact placement optimization strategy for object pushing that allows calculating optimal contact points minimizing the amplitude of forces required to execute the task. Exploiting the optimized contact configuration, a motion controller uses the computed contact forces in feed-forward and position error feedback terms to realize the desired trajectory tracking task. Simulations and real experiments results confirm the validity of our approach.

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.

Building on the benefits of Industry 4.0, Industry 5.0 promotes human-centricity of factories, placing operator’s wellbeing at the center of production. Production processes are expected to be designed around operator’s needs, for a more sustainable contribution of industry to society. In this broad context, in this paper we consider the problem of monitoring operator’s condition and, specifically, detecting any mental or physical fatigue they might be experiencing in workplaces. Indeed, if the onset of any source of fatigue is monitored, it is possible to introduce assistive strategies that preserve productivity, on the one side, and operator’s wellbeing, on the other side. To achieve this goal, we detect mental and physical fatigue conditions via physiological monitoring, by means of a wearable device that measures cardiac activity. We design an experimental protocol such that test participants are exposed to mental and physical fatigue. Their heart rate variability is then extracted and analysed to discriminate among rest, mental fatigue, physical fatigue and joint mental and physical fatigue. The achieved results show that statistically significant differences can be found in time-domain metrics. Moreover, the analysis of the empirical distribution functions shows, for each metrics, the conditions that exhibit the greatest differences and, hence, that can be distinguished more accurately. However, results show also that, in the presence of physical fatigue, it is difficult to detect the presence of additional mental fatigue.

The key concept of collaborative robotics is represented by the presence of a strict interaction between a human user and the robotic system. As such, the study of the interaction is of paramount importance for a successful implementation of the system. In this article, we propose a novel approach to address the problem of designing a collaborative robotic system for industrial applications, focusing on the characteristics of the interaction. In particular, we will propose a set of methodologies focused on interaction design, inspired by those used for the design of user interfaces. These methodologies will allow the design of collaborative robotic systems following a user-centered approach, thus putting emphasis not only on safety and adaptability of the robotic systems (which have been widely addressed in the literature), but also on the interaction experience. While the proposed methodology was developed considering general collaborative robotics applications, two real industrial case studies were considered, to instantiate the considered framework and showcase its applicability to the real-world domain. Note to Practitioners—This article aims at bridging the gap between interaction design and collaborative robotics. In particular, the proposed methodology will represent a toolset for robotic experts (researchers and system integrators), for understanding the user experience and designing the robotic system ensuring an effective interaction. In fact, while robotics experts are typically well aware of issues and methodologies related to technological and application aspects, they often tend to ignore the principles of interaction. Such principles are commonly adopted in the design of computer-based human–machine interfaces or web applications, but, to the best of the authors’ knowledge, have never been applied to the design of collaborative robotic systems for industrial applications. Hence, this article will serve as a fundamental step to bring interaction design principles into the robot integration domain.

In recent years, production systems have become highly sophisticated and complex. As a result, while on the one hand, the least-skilled labor has been partially displaced by machines, high-skilled labor is more required to supervise and control advanced automation systems. In many cases, the complexity of machines implies an increased complexity of human–machine interfaces (HMIs), which are the main point of contact between the operator and the machine. To enable effective use of HMIs and to enable their usage by workers with different knowledge and capabilities, novel design approaches have been proposed. In particular, in this article, we consider the approach developed in the framework of the European research project INCLUSIVE, which aimed at designing industrial HMIs that adapt to the skills and capabilities of human operators. As a case study, we consider an adaptive interaction system for the woodworking industry and present an extensive evaluation carried out in real production environment with shopfloor workers. The effectiveness of the INCLUSIVE approach has been assessed with subjective and objective measurements and compared to that of interaction systems customarily used in industry. Results have shown that users appreciated the INCLUSIVE system and largely preferred it over the customary system. Moreover, with regard to objective performance-related measurements, they performed better when using the INCLUSIVE system since they received tailored guidance during the considered working tasks. Note to Practitioners— This article was motivated by the fact that advanced automation systems are often highly complex for human operators. To address this problem, we focus on the importance of designing the automation system around users and discuss an approach for adaptive automation, called INCLUSIVE. Its main feature is that it adapts the human–machine interface (HMI) according to operator’s skills, capabilities, and current mental fatigue. In this article, we provide an extensive evaluation of the INCLUSIVE system, considering a company producing woodworking machines as a use case. Assessment was carried out in the company shopfloor, considering real workers, and the INCLUSIVE system was compared with the customary HMI running on the company machines. The results of our study suggest that adapting the interaction to operator’s needs allows better working performance while letting workers more satisfied with the use of the system.

Connectivity maintenance is crucial for the real world deployment of multi-robot systems, as it ultimately allows the robots to communicate, coordinate and perform tasks in a collaborative way. A connectivity maintenance controller must keep the multi-robot system connected independently from the system’s mission and in the presence of undesired real world effects such as communication delays, model errors, and computational time delays, among others. In this paper we present the implementation, on a real robotic setup, of a connectivity maintenance control strategy based on Control Barrier Functions. During experimentation, we found that the presence of communication delays has a significant impact on the performance of the controlled system, with respect to the ideal case. We propose a heuristic to counteract the effects of communication delays, and we verify its efficacy both in simulation and with physical robot experiments.

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.

While modern production systems are becoming increasingly technologically advanced, the presence of human operators remains fundamental in industrial workplaces. To complement and enhance the capabilities of human workers, approaches based on adaptive automation have been introduced. They consist of adapting the behavior of the system according to the user’s capabilities and effort. In this article, we present a general holistic framework for adaptive automation, called INCLUSIVE, that assists the operator during working tasks. The system consists of three modules. First, a thorough characterization of the operator’s constitutional and situational condition is provided; based on this, properly tailored adaptation is given, and if necessary, further training and support are provided. The framework has been implemented and tested considering three industrial use cases, selected as representative of a wide area of interest for the industry in Europe, in terms of both production requirements and involved operators. Tests have been carried out in real production environments, considering real production tasks carried out by 53 shop-floor workers. Results have shown that workers’ satisfaction when using the INCLUSIVE system and their performances was increased with respect to customary interaction systems currently used in industries. Moreover, the achieved results were used to formulate a set of recommendations for the design and implementation of an adaptive interaction system in relation to ensuring worker satisfaction and system usability in an industrial environment, as well as performance requirements.Note to Practitioners—This article was motivated by the fact that, despite modern advanced automation, human operators are still central in the manufacturing process. However, technological progress often causes challenging interaction with complex industrial systems. The goal of this article is to introduce a complete framework for adaptive automation, with the ultimate goal of facilitating the interaction of human operators with complex industrial systems. The framework relies on three modules: measurement of human capabilities, the adaption of the interaction system, and additional teaching and support. The three modules are discussed at a high level, independently of the target application. Moreover, to facilitate their application in specific working contexts, examples are provided with respect to three different industrial applications. Results of tests carried out with shop-floor operators show that implementing the proposed framework allows better working performance and increases worker satisfaction with the use of automation.

Modern production systems are becoming more and more complex to comply with diversified market needs, flexible production, and competitiveness. Despite technological progress, the presence of human operators is still fundamental in production plants, since they have the important role of supervising and monitoring processes, by interacting with such complex machines. The complexity of machines implies an increased complexity of human-machine interfaces (HMIs), which are the main point of contact between the operator and the machine. Thus, HMIs cannot be considered anymore an accessory to the machine and their improvement has become an important part of the design of the whole machines, to enable a nonstressful interaction and make them easy to also use less skilled operators. In this article, we present a general framework for the design of HMIs that adapt to the skills and capabilities of the operator, with the ultimate aim of enabling a smooth and efficient interaction and improving user's situation awareness. Adaptation is achieved by considering three different levels: perception (i.e., how information is presented), cognition (i.e., what information is presented), and interaction (i.e., how interaction is enabled). For each level, general guidelines for adaptation are provided, thus defining a meta-HMI independent of the application. Finally, some examples of how the proposed adaptation patterns can be applied to the case of procedural and extraordinary maintenance tasks are presented.

This paper proposes a technique to manipulate an object with a nonholonomic mobile robot by pushing, which is a nonprehensile manipulation motion primitive. Such a primitive involves unilateral constraints associated with the friction between the robot and the manipulated object. Violating this constraint produces the slippage of the object during the manipulation, preventing the correct achievement of the task. A linear time-varying model predictive control is designed to include the unilateral constraint within the control action properly. The approach is verified in a dynamic simulation environment through a Pioneer 3-DX wheeled robot executing the pushing manipulation of a package.

Connectivity maintenance is an essential aspect to consider while controlling a multi-robot system. In general, a multi-robot system should be connected to obtain a certain common objective. Connectivity must be kept regardless of the control strategy or the objective of the multi-robot system. Two main methods exist for connectivity maintenance: keep the initial connections (local connectivity) or allow modifications to the initial connections, but always keeping the overall system connected (global connectivity). In this paper we present a method that allows, at the same time, to maintain global connectivity and to implement the desired control strategy (e.g., consensus, formation control, coverage), all in an optimized fashion. For this purpose, we defined and implemented a Control Barrier Function that can incorporate constraints and objectives. We provide a mathematical proof of the method, and we demonstrate its versatility with simulations of different applications.

Multi-robot systems are able to achieve common objectives exchanging information among each other. This is possible exploiting a communication structure, usually modeled as a graph, whose topological properties (such as connectivity) are very relevant in the overall performance of the multirobot system. When considering mobile robots, such properties can change over time: robots are then controlled to preserve them, thus guaranteeing the possibility, for the overall system, to achieve its goals. This, however, implies limitations on the possible motion patterns of the robots, thus reducing the flexibility of the overall multi-robot system. In this paper we introduce teleoperation as a means to reduce these limitations, allowing temporary violations of topological properties, with the aim of increasing the flexibility of the multi-robot system.

Robot swarms herald the ability to solve complex tasks using a large collection of simple devices. However, engineering a robotic swarm is far from trivial, with a major hurdle being the definition of the control laws leading to the desired globally coordinated behavior. Communication is a key element for coordination and it is considered one of the current most important challenges for swarm robotics. In this paper, we study the problem of maintaining robust swarm connectivity while performing a coverage task based on the Voronoi tessellation of an area of interest. We implement our methodology in a team of eight Khepera IV robots. With the assumptions that robots have a limited sensing and communication range-and cannot rely on centralized processing-we propose a tri-objective control law that outperforms other simpler strategies (e.g. a potential-based coverage) in terms of network connectivity, robustness to failure, and area coverage.

Legibility can be defined as the ability of a robot to communicate its intent to the user. Legibility is relatively little investigated in multi-robot systems, but, in the literature, studies exist where the trajectory of manipulators is analyzed as a factor to improve the collaboration between robots and users. In this paper, we focus on the legibility of a group of mobile robots. To this end, we consider a set of motion-variables: trajectory, dispersion and stiffness. They are typical parameters that determine the motion of a group of robots. To analyze the effect of the motion-variables over legibility, Fisher's exact test and ANOVA (analysis of variance) were carried out. The data for the statistical analysis cover a full factorial plan and they were collected in a virtual reality set-up, where the users shared the environment with a group of robots. We investigate two aspects of legibility: the correctness and the rapidity of communication, namely if the communication happens correctly and how fast it happens. Trajectory was found to be relevant to correctly communicate the intention of the robots, while stiffness and dispersion were relevant for the rapidity of legibility.

In this paper we consider a multi-robot system that shares the environment with external entities, and we propose a methodology for controlling the interaction with them. In particular, we consider the problem of achieving a desired dynamic interaction model, in such a way that the multi-robot system exchanges desired forces with external entities. This is obtained by introducing local deformations of the coupling actions among the robots. The proposed method ensures preservation of the passivity property, which provides safety guarantees in the interaction with the (possibly poorly known) external entities.

Human-robot interaction has gained a lot of attention in recent years, since the use of robots can complement and improve human capabilities. To make such interaction smooth, proper interaction approaches are needed. Customarily these are tested in simplified scenarios and tame laboratory environment, since reproducing complex real use cases is often difficult. Achieved results are then not representative of actual interaction in reality and do not scale to complex scenarios. To overcome this issue, in this paper we consider the use of virtual reality as an alternative tool to assess HRI in those scenarios that are difficult to reproduce in reality. To this end, we compare the interaction experience for the same task, which is carried out in both virtual reality and real environment. To assess user's interaction in the two scenarios, we consider quantitative task related metrics, mental workload sustained, and subjective reporting. Results show that virtual reality allows to reproduce a faithful interaction experience and, thus, can be used to reliably validate human-robot interaction approaches in complex scenarios.

In this paper we present a general framework for affective human-robot interaction that allows users to intuitively interact with a robot and takes into account their mental fatigue, thus simplifying the task or providing assistance when the user feels stressed. Interaction with the robot is achieved by naturally mapping user's forearm motion, detected with a smartwatch, into robot's motion. High-level commands can be provided by means of gestures. An approach based on affective robotics is used to adapt the level of robot's autonomy to the cognitive workload of the user. User's mental fatigue is detected from the analysis of heart rate, also measured by the smartwatch. The framework is general and can be applied to different robotic systems. In this paper, we consider its experimental validation on a wheeled mobile robot.

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally implies an unsustainable cognitive workload for the human operators, in addition to a non-negligible training effort. To overcome this issue, in this paper we present a methodology for the design of adaptive human-centred HMIs for industrial machines and robots. The proposed approach relies on three pillars: measurement of user's capabilities, adaptation of the information presented in the HMI, and training of the user. The results expected from the application of the proposed methodology are investigated in terms of increased customization and productivity of manufacturing processes, and wider acceptance of automation technologies. The proposed approach has been devised in the framework of the European project INCLUSIVE.

In physical human-robot interaction, the coexistence of robots and humans in the same workspace requires the guarantee of a stable interaction, trying to minimize the effort for the operator. To this aim, the admittance control is widely used and the appropriate selection of the its parameters is crucial, since they affect both the stability and the ability of the robot to interact with the user. In this paper, we present a strategy for detecting deviations from the nominal behavior of an admittance-controlled robot and for adapting the parameters of the controller while guaranteeing the passivity. The proposed methodology is validated on a KUKA LWR 4+.

In this paper we consider the problem of controlling the dynamic behavior of a multi-robot system while interacting with the environment. In particular, we propose a general methodology that, by means of locally scaling inter-robot coupling relationships, leads to achieving a desired interactive behavior. The proposed method is shown to guarantee passivity preservation, which ensures a safe interaction. The performance of the proposed methodology is evaluated in simulation, over large-scale multi-robot systems.

Admittance control is a widely used approach for guaranteeing a compliant behavior of the robot in physical human-robot interaction. When an admittance-controlled robot is coupled with a human, the dynamics of the human can cause deviations from the desired behavior of the robot, mainly due to a stiffening of the human arm, and thus generate high-frequency unsafe oscillations of the robot. In this paper we present a novel methodology for detecting the rising oscillations in the human-robot interaction. Furthermore, we propose a passivity-preserving strategy to adapt the parameter of the admittance control in order to get rid of the high-frequency oscillations and, when possible, to restore the desired interaction model. A thorough experimental validation of the proposed strategy is performed on a group of 26 users performing a cooperative task.

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.

Applications based on groups of self‐organized mobile robots are becoming pervasive in communication networks, monitoring, traffic, and transportation systems. Their advantage is the possibility of providing services without the existence of a previously defined infrastructure. However, physical agents are prone to failures that add uncertainty and unpredictability in the environments in which they operate. Therefore, a robust topology regarding failures is an imperative requirement. In this article, we show that mechanisms based solely on connectivity maintenance are not enough to obtain a sufficiently resilient network, and a robustness‐oriented approach is necessary. Thus, we propose a local combined control law that aims at maintaining the overall network connectivity while improving the network robustness via actions that reduce vulnerability to failures that might lead to network disconnection. We demonstrate, from a theoretical point of view, that the combined control law maintains connectivity, and experimentally validate it under diverse failure distributions, from two perspectives: as a reactive and as a proactive mechanism. As a reactive mechanism, it was able to accommodate ongoing failures and postpone or avoid network fragmentation, including cases where failures are concentrated over short time spans. As a proactive mechanism, the network topology was able to evolve from potentially vulnerable with respect to failures to a more robust one. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(4), 388–400 2017

In this paper, we propose a novel methodology for achieving complex dynamic behaviors in multirobot systems. In particular, we consider a multirobot system partitioned into two subgroups: 1) dependent and 2) independent robots. Independent robots are utilized as a control input, and their motion is controlled in such a way that the dependent robots solve a tracking problem, that is following arbitrarily defined setpoint trajectories, in a coordinated manner. The control strategy proposed in this paper explicitly addresses the collision avoidance problem, utilizing a null space-based behavioral approach: this leads to combining, in a non conflicting manner, the tracking control law with a collision avoidance strategy. The combination of these control actions allows the robots to execute their task in a safe way. Avoidance of collisions is formally proven in this paper, and the proposed methodology is validated by means of simulations and experiments on real robots.

In this paper, we address the connectivity maintenance problem for a multirobot system that moves according to a given bounded collective control objective. We assume that the interaction among the robotic units is limited by a given visibility radius both in terms of sensing and communication capabilities. For this scenario, we propose a decentralized bounded control law that can provably preserve the connectivity of the multirobot system over time. We characterize the effect of the connectivity control term on the achievement of the collective control objective by resorting to an input-to-state stability-like analysis. We provide numerical and experimental results to corroborate the theoretical findings and assess the effectiveness of the proposed bounded connectivity maintenance control law.

This paper introduces a control strategy for obtaining cooperative tracking of periodic setpoint trajectories in multi-robot systems. A heterogeneous group of robots is considered: a few independent robots are used as a control input for the system, with the aim of controlling the position of the remaining robots, namely the dependent ones. The control strategy presented in this paper explicitly considers changes in the communication topology among the robots. These changes happen as the system evolves, since robots are equipped with finite range communication devices.

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.

Autonomous vehicles require advances sensing technologies, in order to be able to safely share the environment with human operators. Those sensing technologies are in fact necessary for identifying the presence of unforeseen objects, and measuring their position and velocity. Furthermore, classification is necessary for effectively predicting their behavior. In this paper we consider the presence of sensing systems both on-board each vehicle, and installed on infrastructural elements. While the simultaneous presence of multiple sources of information heavily improves the amount (and quality) of available data, it generates the need for effective data fusion and storage systems. Hence, we introduce a centralized cloud service, that is in charge of receiving and merging data acquired by different sensing systems. Those data are then distributed to the autonomous vehicles, that exploit them for implementing advanced navigation strategies. The proposed methodology is validated in a real industrial environment to safely perform obstacle avoidance with an autonomously driven forklift.

When teleoperating a multi-robot system it is useful to control the kind of behavior of the fleet depending on the environment it is moving in. In this paper, a novel bilateral control architecture for teleoperating a group of mobile robots is proposed. The user can command the robots both as a flexible and amorphous group and as a set of agents executing different trajectories for achieving a desired task, mimicking a conductor-orchestra paradigm. Exploiting passivity based control, we ensure a stable and safe behavior for the user. The proposed teleoperation strategy is validated by means of experiments.

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.

The papers in this special section focus on the technology and applications supported by networked cooperative autonomous systems.

In order to accomplish cooperative tasks, decentralized systems are required to communicate among each other. Thus, maintaining the connectivity of the communication graph is a fundamental issue. Connectivity maintenance has been extensively studied in the last few years, but generally considering undirected communication graphs. In this paper, we introduce a decentralized control and estimation strategy to maintain the strong connectivity property of directed communication graphs. In particular, we introduce a hierarchical estimation procedure that implements power iteration in a decentralized manner, exploiting an algorithm for balancing strongly connected directed graphs. The output of the estimation system is then utilized for guaranteeing preservation of the strong connectivity property. The control strategy is validated by means of analytical proofs and simulation results.

Decentralized control strategies for multirobot systems have been extensively studied over the past few years. Typically, these strategies aim at exploiting local interaction rules to regulate the overall state of the multirobot system toward a desired configuration, thus generating some desired coordinated behaviors, such as synchronization, swarming, deployment, or formation control. However, when considering the real-world application of multirobot systems, more complex cooperative dynamic behaviors are desirable. Along these lines, in this paper, we propose a methodology to control a multirobot system for cooperatively tracking arbitrarily defined periodic setpoint trajectories. This objective is fulfilled partitioning the multirobot system into independent robots (that can provide control inputs) and dependent robots (that are controlled through local interaction). The motion of the independent robots is then defined in such a way that, exploiting local interactions, the dependent robots are controlled to track the desired trajectories. The proposed control strategy is validated by means of simulations and experiments on real robots.

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.

Decentralized control of networked systems has been widely investigated in the literature, with the aim of regulating the overall state of the system to some desired configuration, thus obtaining coordinated emerging behaviors (e.g. synchronization, swarming, coverage, formation control) by means of local interaction. In this paper we introduce a methodology to solve a tracking problem, that is defining a decentralized control strategy for making a networked system follow an arbitrarily defined periodic setpoint function. The most suitable interconnection topology is defined together with the control law as the solution of a constrained optimization problem, in order to ensure asymptotic tracking. Simulations are provided for validating the proposed control strategy.

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.

Networked systems and decentralized control strategies have been widely investigated in the literature, with the objective of obtaining coordinated emerging behaviors by means of local interaction. While typical approaches aim at solving regulation problems (e.g. synchronization, swarming, coverage, formation control) a few works have recently appeared that move towards the solution of more complex problems, such as tracking of arbitrary setpoint functions. Based on the formulation introduced in [1], this objective is obtained in this paper partitioning the networked systems into leaders (that can provide control inputs) and followers (that are controlled through local interaction). In this paper we provide a methodology for letting the leaders estimate the state of the followers in a decentralized manner: this estimate is then used for control purposes. Simulations are provided for validating the proposed control strategy.

In this paper we introduce a collision avoidance control strategy for groups of mobile robots moving in a three-dimensional environment, whose dynamics are described according to the Lagrangian model. The proposed strategy is based on the use of gyroscopic forces, that ensure obstacle avoidance without interfering with the convergence properties of the multi-robot system's desired control law. Moreover, we introduce a method to define the direction of the force in an optimal way, in order to introduce the smallest possible perturbation with respect to the desired behavior of the system. Collision avoidance and convergence properties are analytically demonstrated, and simulation results are provided for validation purpose.

Decentralized control of networked systems has been widely investigated in the literature, with the aim of obtaining coordinated emerging behaviors (e.g. synchronization, swarming, coverage, formation control) by means of local interaction. In this paper we consider the possibility of injecting external inputs into the networked system, in order to obtain more complex cooperative behaviors. Specifically, we introduce a strategy that makes it possible to control the overall state of the networked system by directly controlling only a subset of the networked agents, namely the leaders. Exploiting local interaction rules, it is possible to define the inputs for the leaders in such a way that each follower is forced to track a desired periodic setpoint.

This study introduces a control algorithm that, exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, ensures the connectivity maintenance property for multi robot systems, in the presence of a generic (bounded) additional control term. This result is obtained by driving the robots along the negative gradient of an appropriately defined function of the algebraic connectivity. The proposed strategy is then enhanced with the introduction of the concept of critical robots, that is robots for which the loss of a single communication link might cause the disconnection of the communication graph. Limiting the control action to critical robots will be shown to reduce the control effort that is introduced by the proposed connectivity maintenance control law and to mitigate its effect on the additional (desired) control term.

While RGB-D sensors are becoming more and more popular in mobile robotics laboratories, they are usually not yet adopted for industrial applications. In fact, in this field, depth measurements are generally acquired by means of laser scanners and, when visual information is needed, by means of stereo-cameras. The aim of this paper is to perform an experimental validation, to compare the performance of a stereo-camera and an RGB-D sensor, in a specific application: mobile robot localization for industrial applications. Experiments are performed exploiting artificial landmarks (defined by a self-similar pattern), placed in known positions in the environment.

In order to accomplish cooperative tasks, multi-robot systems are required to communicate among each other. Thus, maintaining the connectivity of the communication graph is a fundamental issue. Connectivity maintenance has been extensively studied in the last few years, but generally considering only kinematic agents. In this paper we will introduce a control strategy that, exploiting a decentralized procedure for the estimation of the algebraic connectivity of the graph, ensures the connectivity maintenance for groups of Lagrangian systems. The control strategy is validated by means of analytical proofs and simulation results.

In this paper we present a decentralized control strategy for the connectivity maintenance for groups of mobile robots performing some desired task. Exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, we prove the connectivity maintenance property in the general case, i.e. in presence of a generic (bounded) additional control term. Then, we address two specific decentralized control applications: rendezvous and formation control. We analytically prove that our control strategy ensures the global connectivity maintenance, while preserving the convergence properties of the rendezvous and formation controllers respectively. Simulations and experimental results are presented as well, in order to show the effectiveness of the proposed control law.

In this paper we address the trajectory tracking problem for groups of mobile robots. We consider trajectories described by completely arbitrary shaped closed curves. The proposed control strategy is a completely decentralized algorithm, and does not require any global synchronization. The desired behavior is obtained by means of some properly designed artificial potential functions.

In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe a methodology to obtain a formation with the shape of a regular polygon. Then, applying a bijective coordinates transformation, we show how to obtain a formation with an arbitrary shape. Our control strategy is based on the interaction of some artificial potential fields, but it is not affected by the problem of local minima.