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.

To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst-case efficiency of (1-(1/e)) relative to the optimal node embedding, or VNET embedding if virtual links are mapped to exactly one physical link. This bound is optimal, that is, no better polynomial-time approximation algorithm exists, unless P=NP. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared to existing distributed VNET embedding solutions, and we show how by appropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.

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 this paper a novel decentralized approach for task sequencing within a multiple missions control framework is presented. The main contribution of this work concerns the decentralization of a control framework for multiple mission execution in order to enhance the robustness of the system, and the application of the latter to a heterogeneous robotic network. The proposed approach is based on the Matrix-based Discrete Event Framework (MDEF). This formalism is adapted to networks of heterogeneous robots, i.e., robots with different capabilities, and to the decentralized control of mission execution using a consensus-based approach which guarantees the agreement among robots on executed actions and their consequences.

Passive Radio Frequency Identification (RFID) is being increasingly used in mobile robotics applications, as it provides inexpensive and effective solutions to data association issues in basic navigation tasks. Nonetheless, problems related to sensitivity of the signal to interference and reflections, and missing tag range and bearing information are open. In this paper, we propose a novel approach to passive RFID, which tackles those issues using fuzzy reasoning. Specifically, first, we present a fuzzy antenna model. Then, based on this model, we describe two fuzzy logic methods for tag localization. One allows us to accurately localize passive tags in the environment and to generate what we call an RFID-augmented map; the other is suited for estimating the bearing of a tag relative to the robot. The general use of both methods is in object localization, map building, environment monitoring, and robot pose estimation. Results of experimental tests demonstrate that fuzzy logic is appropriate to operate under uncertainty in RFID systems, and allows for accurate tag localization.

In this paper, we present a mobile robot global positioning system which combines passive RFID and monocular vision. The method is intended to solve the so-called kidnapped robot problem, that is, the problem of estimating the robot pose when it is first booted up or it has been kidnapped and taken to some unknown location. The main novelty of the approach is the use of fuzzy logic to cope with both uncertainty in RFID data and missing tag position information. Fuzzy logic allows us to manage inaccurate sensor data using rules easily understandable by humans. An additional advantage of the proposed method is that it does not require the robot to move around to solve ambiguities, nor does it need several tags to be properly arranged. Conversely, only one passive RFID tag and a single visual landmark are sufficient to univocally estimate the pose of the robot in the environment.