The field of multi-robot systems is one of the main research topics in robotics, as robot networks offer great advantages in terms of reliability and efficiency in many application domains. This paper focuses on the problem of mutual localization and 3D cooperative environment mapping using a heterogeneous multi-robot team. The proposed algorithm relies on the exchange of local maps and is totally distributed; no assumption on a common reference frame is done. The developed strategy is robust to failures, scalable with the number of the robots in the network, and has been validated through an experimental campaign.

Distributed networks of sensors have been recognized to be a powerful tool for developing fully automated systems that monitor environments and human activities. Nevertheless, problems such as active control of heterogeneous sensors for high-level scene interpretation and mission execution are open. This paper presents the authors' ongoing research about design and implementation of a distributed heterogeneous sensor network that includes static cameras and multi-sensor mobile robots. The system is intended to provide robot-assisted monitoring and surveillance of large environments. The proposed solution exploits a distributed control architecture to enable the network to autonomously accomplish general-purpose and complex monitoring tasks. The nodes can both act with some degree of autonomy and cooperate with each other. The paper describes the concepts underlying the designed system architecture and presents the results obtained working on its components, including some simulations performed in a realistic scenario to validate the distributed target tracking algorithm.

In the last decades, sensor networks have received significant attention in the field of Ambient Intelligence (AmI)for surveillance and assisted living applications, as they provide a powerful tool to capture relevant information aboutenvironments and people activities. Mobile robots hold the promise to enhance the potential of sensor networks, towards thedevelopment of intelligent systems that are able not only to detect events, but also to actively intervene on the environmentaccordingly. This paper presents a Distributed Ambient Intelligence Architecture (DAmIA) aiming at integrating multi-sensorrobotic platforms with Wireless Sensor Networks (WSNs). It is based on the Robot Operating System (ROS), and providesa flexible and scalable software infrastructure extendible to different AmI scenarios. The paper describes the proposedarchitecture and presents experimental tests, showing the feasibility of the system in the context of Ambient Assisted Living(AAL).

In this paper we present an on-board Computer Vision System for the pose estimation of an Unmanned Aerial Vehicle (UAV)with respect to a human-made landing target. The proposed methodology is based on a coarse-to-fine approach to searchthe target marks starting from the recognition of the characteristics visible from long distances, up to the inner details whenshort distances require high precisions for the final landing phase. A sequence of steps, based on a Point-to-Line Distancemethod, analyzes the contour information and allows the recognition of the target also in cluttered scenarios. The proposedapproach enables to fully assist the UAV during its take-off and landing on the target, as it is able to detect anomaloussituations, such as the loss of the target from the image field of view, and the precise evaluation of the drone attitude whenonly a part of the target remains visible in the image plane. Several indoor and outdoor experiments have been carriedout to demonstrate the effectiveness, robustness and accuracy of developed algorithm. The outcomes have proven that ourmethodology outperforms the current state of art, providing high accuracies in estimating the position and the orientation oflanding target with respect to the UAV.

This paper deals with the analysis of the convergence properties of the max-consensus protocol in presence of asynchronous updates and bounded time delays on directed static networks. The work is motivated by real-world applications in distributed decision-making systems, for which max-consensus is an effective paradigm. The main result of this paper is that the strongly connectedness of the directed communication network is a sufficient condition for the asynchronous max-consensus protocol to let a distributed system converge in finite time. Implementation issues are also taken into account, by complementing the theoretical analysis with the definition of a mechanism to detect convergence in a distributed fashion. Finally, a numerical example is given, highlighting both the issues related to the failure of synchronous protocols applied to asynchronous settings and the effectiveness of the proposed asynchronous framework.

This paper addresses the problem of distributed target tracking, performed by a network of agents which update their local estimates asynchronously. The proposed solution extends and improves an existing consensus-based distributed target tracking framework to cope with real-world settings in which each agent is driven by a different clock. In the consensus-based target tracking framework, it is assumed that only a few agents can actually measure the target state at a given time, whereas the remainder is able to perform a model-based prediction. Subsequently, an algorithm based on max-consensus makes all the agents agree, in finite time, on the best available estimate in the network. The limitations imposed by the assumption of synchronous updates of the network nodes are here overcome by the introduction of the concept of asynchronous iteration. Moreover, an event-based approach makes for the lack of a common time scale at the network level. Furthermore, the synchronous scenario can be derived as a special case of the asynchronous setting. Finally, numerical simulations confirm the validity of the approach. © 2013 IEEE.

In this paper the problem of distributed target tracking is considered. A network of heterogeneous sensing agents is used to observe a maneuvering target and, at each iteration, all the agents are able to agree about the estimate of the target position, despite the fact that only a small percentage of agents can sense the target at each time instant. Our Consensus-based Distributed Target Tracking (CDTT) is a fully distributed iterative tracking algorithm, in which each iteration is based on two phases: an estimation phase and a consensus one. As a result, the estimated trajectories are identical for all the agents at each time instant. Numerical simulations and comparison with another target tracking algorithm are carried out to show the effectiveness and feasibility of our approach. © 2011 IEEE.

In this paper we consider the cooperative control of the manipulation of a load on a plane by a team of mobile robots. We propose two different novel solutions. The first is a controller which ensures exact tracking of the load twist. This controller is partially decentralized since, locally, it does not rely on the state of all the robots but needs only to know the system parameters and load twist. Then we propose a fully decentralized controller that differs from the first one for the use of i) a decentralized estimation of the parameters and twist of the load based only on local measurements of the velocity of the contact points and ii) a discontinuous robustification term in the control law. The second controller ensures a practical stabilization of the twist in presence of estimation errors. The theoretical results are finally corroborated with a simulation campaign evaluating different manipulation settings.

In this paper, a distributed approach for the estimation of kinematic and inertial parameters of an unknown rigid body is presented. The body is manipulated by a pool of ground mobile manipulators. Each robot retrieves a noisy measurement of its velocity and the contact forces applied to the body. Kinematics and dynamics arguments are used to distributively estimate the relative positions of the contact points. Subsequently, distributed estimation filters and nonlinear observers are used to estimate the body mass, the relative position between its geometric center and its center of mass, and its moment of inertia. The manipulation strategy is functional to the estimation process, and is suitably designed to satisfy nonlinear observability conditions that are necessary for the success of the estimation. Numerical results corroborate our theoretical findings.

In this paper, we propose a distributed strategy for the estimation of the kinematic and inertial parameters of an unknown body manipulated by a team of mobile robots. We assume that each robot can measure its own velocity, as well as the contact forces exerted during the body manipulation, but neither the accelerations nor the positions of the contact points are directly accessible. Through kinematics and dynamics arguments, the relative positions of the contact points are estimated in a distributed fashion, and an observability condition is defined. Then, the inertial parameters (i.e., mass, relative position of the center of mass and moment of inertia) are estimated using distributed estimation filters and a nonlinear observer in cooperation with suitable control actions that ensure the observability of the parameters. Finally, we provide numerical simulations that corroborate our theoretical analysis.

We present two distributed methods for the estimation of the kinematic parameters, the dynamic parameters, and the kinematic state of an unknown planar body manipulated by a decentralized multi-agent system. The proposed approaches rely on the rigid body kinematics and dynamics, on nonlinear observation theory, and on consensus algorithms. The only three requirements are that each agent can exert a 2D wrench on the load, it can measure the velocity of its contact point, and that the communication graph is connected. Both theoretical nonlinear observability analysis and convergence proofs are provided. The first method assumes constant parameters while the second one can deal with time-varying parameters and can be applied in parallel to any task-oriented control law. For the cases in which a control law is not provided, we propose a distributed and safe control strategy satisfying the observability condition. The effectiveness and robustness of the estimation strategy is showcased by means of realistic MonteCarlo simulations.

In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on node selection, rather than on sensor fusion. The presented approach is particularly suitable when sensors with limited sensing capability are considered. In this case, strategies based on sensor fusion may exhibit poor results, as several unreliable measurements may be included in the fusion process. On the other hand, our approach implements a distributed strategy able to select only the node with the most accurate estimate and to propagate it through the whole network in finite time. The algorithm is based on the definition of a metric of the estimate accuracy, and on the application of an agreement protocol based on max-consensus. We prove the convergence, in finite time, of all the local estimates to the most accurate one at each discrete iteration, as well as the equivalence with a centralised Kalman filter with multiple measurements, evolving according to a state-dependent switching dynamics. An application of the algorithm to the problem of distributed target tracking over a network of heterogeneous range-bearing sensors is shown. Simulation results and a comparison with two distributed Kalman filtering strategies based on sensor fusion confirm the suitability of the approach. © 2014 © 2014 Taylor & Francis.

In this paper the problem of distributed target tracking is considered. A network of agents is used to observe a mobile target and, at each iteration, all the agents agree about the estimate of the target position, despite the fact that they only have local interactions and only a small percentage of them can sense the target. The proposed approach, named Consensus-based Distributed Target Tracking (CDTT), is a fully distributed iterative tracking algorithm. At each iteration our method applies two phases. During the perception phase the target position is obtained either as a measure or as a prediction; subsequently, in the consensus phase a consensus algorithm is applied in order to let all the agents agree on the target position. As a result, the estimated trajectories are identical for all the agents. Numerical simulations are carried out to show the effectiveness and feasibility of our approach. © 2011 IEEE.

Nowadays, Internet of Things (IoT) and robotic systems are key drivers of technological innovation trends. Leveragingthe advantages of both technologies, IoT-aided robotic systems can disclose a disruptive potential of opportunities The presentcontribution provides an experimental analysis of an IoT-aided robotic system for environmental monitoring. To this end, an experimentaltestbed has been developed. It is composed of: (i) an IoT device connected to (ii) a Unmanned Aerial Vehicle (UAV) whichexecutes a patrolling mission within a specified area where (iii) an IoT network has been deployed to sense environmental data.An extensive experimental campaign has been carried out to scavenge pros and cons of adopted technologies. The key resultsof our analysis show that: (i) the UAV does not incur any significant overhead due to on board IoT equipment and (ii) the overallQuality of Service (QoS) expressed in terms of network joining time, data retrieval delay and Packet Loss Ratio (PLR) satisfies themission requirements. These results enable further development in larger scale environment.

Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted on-board an agricultural vehicle. First, a dense 3D map of the grapevine row, augmented with its color appearance, is generated, based on infrared stereo reconstruction. Then, different computational geometry methods are applied and evaluated for plant per plant volume estimation. The proposed methods are validated through field tests performed in a commercial vineyard in Switzerland. It is shown that different automatic methods lead to different canopy volume estimates meaning that new standard methods and procedures need to be defined and established. Four deep learning frameworks, namely the AlexNet, the VGG16, the VGG19 and the GoogLeNet, are also implemented and compared to segment visual images acquired by the RGB-D sensor into multiple classes and recognize grape bunches. Field tests are presented showing that, despite the poor quality of the input images, the proposed methods are able to correctly detect fruits, with a maximum accuracy of 91.52%, obtained by the VGG19 deep neural network.

This report summarizes the design of the MINOAS heavy climbing robot, able to support marking and thickness measure devices and displace them over the ship hold walls, according to technical specifics defined in the MINOAS Project. In particular, a Magnetic Autonomous Robotic Crawler (MARC) with track based locomotion and electrical propulsion has been designed, paying specific attention to the capability of operating on vertical walls, going beyond discontinuities between consecutive surfaces,and carrying a significant payload. This paper reports details about the electro-mechanical design, development of the automatic control systems, integration with the overall MINOAS architecture.

In recent years, the study of sensor and robot networks for Ambient Assisted Living applications has gained a growing attention. In this work, a multimodal user interface for a distributed ambient intelligence architecture is presented. The user interface has been designed with in mind the specific characteristics of each user and the easiness of use of functions even for people without familiarity with technology. The system has been tested with elderly users: the acquired experience allows the analysis of problems like user acceptance, usability and suitability of these systems. The tests have shown a good users' feeling towards the interface, especially in relation to the voice interface.

In this paper, we present new theoretical results on the convergence of max-consensus protocols for asynchronous networks. The analysis is carried out exploiting well-established concepts in the field of partially asynchronous iterative algorithms and of analytic synchronization. As a main result, we propose a theoretical setting to prove the convergence of the asynchronous max-consensus protocol. Moreover, we provide an upper bound on the convergence time of the max-consensus protocol in asynchronous networks.

Robotic networks are increasingly used to under- take complex missions and are often composed of heterogeneous agents executing well-defined tasks. To react against external disturbances and hardware failures, a typical technique is to change at runtime how tasks are assigned to robots. In this perspective, the paper presents an approach based on predictive control for the on-line selection of the optimal time instants when perform reassignments. To evaluate the effectiveness of the proposed approach, simulation results are showcased in comparison with reactive and proactive strategies.

A poster about the use of marsupial robots and vehicles for next-genaration of missions in polar regions

Future cyber-physical systems are expected to exploit autonomous robots to accomplish dangerous or complex missions composed of several tasks. A critical aspect is the availability of suitable mission planning strategies to react against external disturbances or hardware outages. Unfortunately, classic planning approaches may not take advantage of the ability of cyber-physical systems to collect a variety of information from sensors or IoT nodes, which can be used to forecast future events. Therefore, this paper proposes the adoption of predictive control for mission planning. Specifically, predictive control is used to compute online the best time instants when to change the assignment of tasks to robots by solving finite- horizon optimal control problems. Simulation results performed in comparison with "legacy" reactive and proactive strategies showcase the superiority of the proposed approach, especially in scenarios characterized by large disturbances.