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The flow network balancing problem, i.e., the problem of balancing the incoming and outgoing flows for each vertex of a directed graph, has been widely investigated with several distributed solutions being proposed in recent years. Flow balancing is crucial in several application domains, ranging from water and traffic networks to complex network synchronization and distributed adaptive networked control. In this paper, we focus on finding the solution for the flow network balancing problem that is optimal in a minimal effort sense. More specifically, we aim at modifying a given set of (unbalanced) flows so that we obtain a balanced solution. We assume that there is a (possibly heterogenous) cost associated to the unit variation of each flow, as well as lower and upper bounds on the peredge flows. More in detail, we first establish a necessary and sufficient optimality condition for network balancing and then propose a distributed protocol, demonstrating its convergence toward the global optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed distributed algorithm. 64 9 3529 3543

The vision of the project PANTHEON is to develop the agricultural equivalent of an industrial Supervisory Control And Data Acquisition (SCADA) system to be used for the precision farming of orchards. By taking advantage of the technological advancements in the fields of control, robotics, remote sensing, and big-data management, the objective of the project is to design an integrated system where a relatively limited number of heterogeneous unmanned robotic components (including terrestrial and aerial robots) move within the orchard to collect data and perform typical farming operations, and an Internet-of-Things (IoT) agro-meteorological solar- powered network is deployed to continuously monitor the environmental conditions of the orchard. The information is going to be collected and stored in a central operative unit that integrates the data coming from the different robotic vehicles to perform automatic feedback actions (e.g. to regulate the irrigation system) and to support the decisions of the agronomists and farmers in charge of the orchard. The PANTHEON SCADA system will acquire information at the resolution of the single plant. This will allow to drastically increase the detection of possible limiting factors for each individual plant, such as lack of water or pests and diseases affecting the plant health, and to react accordingly. Thus, the ongoing implementing architecture has the potential to increase the production of the orchard while, at the same time, being more cost-effective and environmentally-friendly. The PANTHEON SCADA system will be validated in a real-world (1:1 scale) orchard, thus leading to a working prototype., https://youtu.be/GwMDxBwhu28 https://youtu.be/ZBGkKNEj0LU

Cloud-aided robots are increasingly adopted in realizing Cyber-Physical-Social System (CPSS) to reduce human efforts and enhance the system performance with diverse robotic applications. However, the multi-hop distance from robot to cloud data centre elevates the data transfer delay that often inhibits the deadline-satisfied task execution of robotic applications. Thanks to edge computing, this issue can be well addressed by harnessing computing resources in proximity of data sources. As edge resources are constrained in energy and processing capacity, sharing resources among the tasks of multiple robotic applications is critical to ensure. Therefore, in this paper, we develop a congestion game-theoretic robotic edge resource allocation mechanism for CPSS, which not only maintains the Quality-of-Service (QoS) by meeting task completion deadlines but also satisfies the energy constraints of resources. Here, Agriculture 4.0 is considered as a use case for the proposed mechanism which can be extended for other domains. Nevertheless, the performance evaluation of the proposed mechanism is conducted in an iFogSim simulated edge computing environment. In comparison with existing greedy, heuristic, and evolutionary benchmarks, our mechanism is proven to offer overall 20\% improvement in deadline satisfaction, energy consumption, and resource utilization.

Identifying pests and treating them in a timely manner is a crucial aspect of the Precision Agriculture (PA) paradigm. Driven by the needs of the H2020 European Project Pantheon, focused on precision farming in hazelnut orchards, we propose a pest management system for gall-mites, which cause severe symptoms on generative and vegetative buds. We develop a data-driven monitoring system based on You Only Look Once (YOLO) framework enabling the early detection of the pest infestation with mean average precision of 86.7% on a holdout dataset. We perform a thorough analysis on its performance using several data augmentation methods as well as validate its real-time computation capability on a NVIDIA Jetson Xavier, which can be easily integrated into any robotic platform. Finally, we contextualize the role of the proposed detection framework in a comprehensive pest management system.

In this chapter, we introduce the vision of the H2020 project ``Precision Farming of Hazelnut Orchards'' (PANTHEON), which is to develop the agricultural equivalent of an industrial Supervisory Control and Data Acquisition (SCADA) system to be used for precision farming of orchards. PANTHEON's objective is to design an integrated system where a relatively limited number of heterogeneous unmanned robotic components (including terrestrial and aerial robots) move within the orchard to collect data and perform typical farming operations. In addition, an Internet-of-Things (IoT) agrometeorological solar-powered network is deployed to continuously monitor the environmental conditions of the orchard. The information so collected is then stored in a central operative unit that integrates the data to perform automatic feedback actions (e.g., to regulate the irrigation system) and to support the decisions of the agronomists and farmers in charge of the orchard. The proposed SCADA system will acquire information at the resolution of the individual plant, to drastically increase, compared to current best practices, the detection of possible limiting factors at the level of the individual plant and to react accordingly. Differently from the current state of the art in precision farming for large-scale orchards, the capability of monitoring the state and the evolution of each single tree will be the enabling technology to allow more focused interventions. This will lead to a better average health of the orchard and to an increased effectiveness of integrated pest management (IPM) activities. In conclusion, the ongoing implemented architecture has the potential to increase production while, at the same time, being more cost-effective and environmentally friendly. To summarize, we believe that the proposed SCADA paradigm for Precision Agriculture may represent an attractive opportunity for the design of a novel real-time software architecture. In other words, by allowing the processing of massive amounts of datasets derived from the SCADA architecture, it will be possible to step up the current effectiveness of Precision Agriculture (PA) methodologies by providing real-time answers to the questions posed by farm managers, when in need of timely decisions.

In this paper, we consider the problem of general topology control in multirobot systems with nonholonomic kinematics. Our contribution is twofold: We first demonstrate the correctness of topology control under the assumption that the network topology can switch arbitrarily and that potential-based mobility is discontinuous with respect to topology changes; we then demonstrate that a multirobot team under the above listed conditions continues to achieve topology control when actuator saturation is applied and in the presence of arbitrary discontinuous (and possibly nonpairwise) exogenous objectives. Simulation results are given to corroborate our theoretical findings.

Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problems of distributed estimation and control of node centrality in undirected graphs with asymmetric weight values. In particular, we focus our attention on $\alpha$-centrality, which can be seen as a generalization of eigenvector centrality. In this setting, we first consider a distributed protocol where agents compute their $\alpha$-centrality, focusing on the convergence properties of the method; then, we combine the estimation method with a consensus algorithm to achieve a consensus value weighted by the influence of each node in the network. Finally, we formulate an $\alpha$-centrality control problem which is naturally decoupled and, thus, suitable for a distributed setting and we apply this formulation to protect the most valuable nodes in a network against a targeted attack, by making every node in the network equally important in terms of {\alpha}-centrality. Simulations results are provided to corroborate the theoretical findings., Comment: published on IEEE Transactions on Automatic Control https://ieeexplore.ieee.org/abstract/document/9126182

In this paper, we address the clock synchronization problem for wireless sensor networks. In particular, we consider a wireless sensor network where nodes are equipped with a local clock and communicate in order to achieve a common sense of time. The proposed approach consists of two asynchronous consensus algorithms, the first of which synchronizes the clocks frequency and the second of which synchronizes the clocks offset. This work advances the state of the art by providing robustness against bounded communication delays. A theoretical characterization of the algorithm properties is provided. Simulations and experimental results are presented to corroborate the theoretical findings and show the effectiveness of the proposed algorithm.

This technical communique represents a generalization of the convergence analysis for the consensus algorithm proposed in Priolo et al. (2014). Although the consensus was reached for any strongly connected weighted digraphs (SCWD), the convergence analysis provided in Priolo et al. (2014) was only valid for diagonalizable matrices encoding a SCWD. The result we present here generalizes the previous one to all possible matrices encoding a SCWD that can be used in the algorithm.

In this work, we incorporate trust into the interaction dynamics for multi-agent systems in order to analyze the effects of trust on human-robot teams. We extend previous work by moving away from a 'self-centered' trust model and introduce a so-called 'team-oriented' trust model in which each agent increases its trust for its neighbors only if they are collectively contributing to the team's overall goal. The coupling of the trust dynamics and the agents' state dynamics is shown to give rise to an intricate relationship that has the potential to make the team's performance deteriorate under certain circumstances. We derive conditions under which the multi-agent system is guaranteed to achieve a collective objective and provide simulations to corroborate the theoretical findings.

In this paper, we propose a decentralized model and control framework for the assignment and execution of tasks, i.e. the dynamic task planning, for a network of heterogeneous robots. The proposed modeling framework allows the design of missions, defined as sets of tasks, in order to achieve global objectives regardless of the actual characteristics of the robotic network. The concept of skills, defined by the mission designer and considered as constraints for the mission execution, is exploited to distribute tasks across the robotic network. In addition, we develop a decentralized control algorithm, based on the concept of skills for decoupling the mission design from its deployment, which combines task assignment and execution through a consensus-based approach. Finally, conditions upon which the proposed decentralized formulation is equivalent to a centralized one are discussed. Experimental results are provided to validate the effectiveness of the proposed framework in a real-world scenario.

In this work we propose a distributed algorithm to solve the discrete-time average consensus problem on strongly connected weighted digraphs (SCWDs). The key idea is to couple the computation of the average with the estimation of the left eigenvector associated with the zero eigenvalue of the Laplacian matrix according to the protocol described in Qu et al. (2012). The major contribution is the removal of the requirement of the knowledge of the out-neighborhood of an agent, thus paving the way for a simple implementation based on a pure broadcast-based communication scheme.

This article proposes a swarm-based path-following guidance system for an autonomous marine multi-vehicle system. In particular, a team of unmanned surface vehicles is required to converge to and navigate along a desired reference path, while at the same time aggregating and maintaining a range-based formation configuration. First, a separate description is given for a swarm methodology, initially developed for small ground mobile robots and exploited to aggregate the robot team, and a virtual target–based path-following guidance system developed for unmanned surface vehicles, exploited to drive not the single vehicles but the robot formation as a whole. Then, the integration of the two proposed methodologies is reported and proven, in order to guarantee the feasibility and stability of the overall guidance framework. Simulative results are proposed to validate the effectiveness of the proposed methodology and to evaluate the system performances.

In this paper, we consider the problem of identifying a bipartition of a planar multi-robot network, such that the resulting two sub-teams are rigid networks. As opposed to approaching the network splitting problem constructively, we instead determine the existence conditions for rigidity-preserving bipartitions, and provide an iterative algorithm that identifies such partitions in polynomial time. In particular, the relationship between rigid graph partitions and the previously identified Z-link edge structure is given, yielding a direction towards which a graph search is applied. Adapting a supergraph search mechanism from the set generation literature, we then provide a methodology for discerning graphs cuts that represent valid rigid bipartitions. Finally, full algorithm details and pseudocode are provided, together with simulation results that verify correctness and demonstrate complexity.

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.

This paper focuses on the Wireless Sensor Network (WSN) synchronization problem for event-driven measurement applications. The objective is twofold: i) to provide high accuracy in the area where an event is detected, ii) to ensure a long network life time. These two objectives are conflicting. In fact, to increase the synchronization accuracy packets must be exchanged at higher rate, thus affecting the network lifetime. In several interesting applications a trade-off can be achieved based on the observation that, if the goal is to observe a localized event, only the portion of the network surrounding the detected event requires a higher accuracy. The proposed algorithm represents a formalization of this idea. Simulations are done to show the effectiveness of the proposed algorithm.

This paper focuses on the improvement of a swarm-based path-following guidance system for an autonomous marine multi-vehicle system. In the seminal idea a team of Unmanned Surface Vehicles (USVs) is required to join into a formation by means of a potential-based swarm aggregation methodology, while a virtual-target based guidance module drives the whole formation towards a desired reference path. Such a framework is then enhanced by taking into account the vehicles' positive surge speed constraint and maximum limit, typical of marine systems. The analysis of the results is reported to prove the feasibility and reliability of the proposed methodology, as well as the performance evaluation of the overall system.

In this paper we propose a distributed and cooperative approach to address the problem of coordinating the electric power demand of electric thermal systems in an urban environment. We model the network of electric thermal systems as a multi-agent system where each agent has only access to local information and ignores any global data such as the state of the whole network or its topology. Furthermore, no central coordinator or aggregator of information is assumed to exist. The proposed method is based on the design of a randomized and asynchronous gossip-based local interaction policy among heterogeneous devices interconnected by a communication peer-to-peer network over the internet. We provide preliminary formal results on the convergence properties of the approach and numerical simulations to validate the performance of the proposed method.

In this paper, we present an algorithm for the exploration of an unknown graph by multiple robots, which is never worse than depth-first search with a single robot. On trees, we prove that the algorithm is optimal for two robots. For k robots, the algorithm has an optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any tree, and this is substantiated by simulations. For trees with e edges and radius r, the exploration time is less than 2e/k + (1 + (k/r))k-1 (2/k!)rk-1 = (2e/k) + O((k + r)k-1) (for r >; k

Area coverage is a well-known problem in robotics. Extensive research has been conducted for the single robot coverage problem in the past decades. More recently, the research community has focused its attention on formulations where multiple robots are considered. In this paper, a new formulation of the multi-robot coverage problem is proposed. The novelty of this work is the introduction of a sensor network, which cooperates with the team of robots in order to provide coordination. The sensor network, taking advantage of its distributed nature, is responsible for both the construction of the path and for guiding the robots. The coverage of the environment is achieved by guaranteeing the reachability of the sensor nodes by the robots. Two distributed algorithms for path construction are discussed. The first aims to speed up the construction process exploiting a concurrent approach. The second aims to provide an underlying structure for the paths by building a Hamiltonian path and then partitioning it. A statistical analysis has been performed to show the effectiveness of the proposed algorithms. In particular, three different indexes of quality, namely completeness, fairness, and robustness, have been studied.

In this paper, we consider the problem of connectivity maintenance in multi-robot systems with unicycle kinematics. While previous work has approached this problem through local control techniques, we propose a solution which achieves global connectivity maintenance under nonholonomic constraints. In addition, our formulation only requires intermittent estimation of algebraic connectivity, and accommodates discontinuous spatial interactions among robots. Specifically, we extend a decision-based link maintenance framework to unicycle kinematics and discontinuous potential-based interaction, by exploiting techniques from nonsmooth analysis. Then, we couple this extension with an existing connectivity estimation technique which yields an estimate with tunable precision in finite time, achieving our result. To illustrate the correctness of our methods, we provide a brief simulation result that closes the paper.

Part 5: INFRASTRUCTURE MODELING AND SIMULATION; International audience; Data fusion provides a means for combining pieces of information from various sources and sensors. This chapter presents a data fusion methodology for interdependent critical infrastructures that leverages a distributed algorithm that allows the sharing of the possible causes of faults or threats affecting the infrastructures, thereby enhancing situational awareness. Depending on the degree of coupling, the algorithm modulates the information content provided by each infrastructure using a data fusion technique called evidence discounting. The methodology is applied to a case study involving a group of dependent critical infrastructures. Simulation results demonstrate that the methodology is resilient to temporary faults in the critical infrastructure communications layer.

Localization, i.e., estimating a robot pose relative to a map of an environment, is one of the most relevant problems in mobile robotics. The research community has devoted a big effort to provide solutions for the localization problem. Several methodologies have been proposed, among them the Kalman filter and Monte Carlo Localization filters. In this paper, the Clustered Evolutionary Monte Carlo filter (CE-MCL) is presented. This algorithm, taking advantage of an evolutionary approach along with a clusterization method, is able to overcome classical MCL filter drawbacks. Exhaustive experiments, carried on the robot ATRV-Jr manufactured by iRobot, are shown to prove the effectiveness of the proposed CE-MCL filter.

In this paper, we consider the problem of evaluating the rigidity of a planar network, while satisfying common objectives of real-world systems: decentralization, asynchronicity, and parallelization. The implications that rigidity has in fundamental multirobot problems, e.g., guaranteed formation stability and relative localizability, motivates this study. We propose the decentralization of the pebble game algorithm of Jacobs et al. , which is an O(n 2 ) method that determines the generic rigidity of a planar network. Our decentralization is based on asynchronous messaging and distributed memory, coupled with auctions for electing leaders to arbitrate rigidity evaluation. Further, we provide a parallelization that takes inspiration from gossip algorithms to yield significantly reduced execution time and messaging. An analysis of the correctness, finite termination, and complexity is given, along with a simulated application in decentralized rigidity control. Finally, we provide Monte Carlo analysis in a Contiki networking environment, illustrating the real-world applicability of our methods, and yielding a bridge between rigidity theory and realistic interacting systems.

In this paper, we consider the problem of evaluating the generic rigidity of an interconnected system in the plane, without a priori knowledge of the network's topological properties. We propose the decentralization of the pebble game algorithm of Jacobs et. al., an O(n2) method that determines the generic rigidity of a planar network. Our decentralization is based on asynchronous inter-agent message-passing and a distributed memory architecture, coupled with consensus-based auctions for electing leaders in the system. We provide analysis of the asynchronous messaging structure and its interaction with leader election, and Monte Carlo simulations demonstrating complexity and correctness. Finally, a novel rigidity evaluation and control scenario in the accompanying media illustrates the applicability of our proposed algorithm.

"In this work the Network Localization Problem with noisy measurements and mobility is considered. In particular, we first focus on the discovery of the localizable subnetwork by introducing an iterative approach to detect and merge small localizable components. Furthermore, we propose a novel check to mitigate the risk of flips ambiguities in order to enlarge the aforementioned subnetwork. Successively, we introduce the concept of critical node, and adopt an iterative localization scheme to retrieve information about the relevance of a node. This allows us to investigate how the accuracy of the localization process can be improved by the aid of mobility, i.e., critical nodes which are relevant for the localization of the network can be localized by means of mobile nodes. Simulations are provided to show the effectiveness of the proposed approach."

In this work, the clock synchronization problem for wireless sensor networks is addressed. In our scenario, we consider a wireless sensor network where nodes are equipped with a local clock hardware and communicate in order to achieve a common sense of time. This work introduces a novel synchronization algorithm able to ensure a good level of synchronization even in the presence of random bounded communication delays. A theoretical analysis of the robustness of the proposed algorithm is proposed along with simulations to corroborate the theoretical finding.

"In this paper, a novel decentralized swarm aggregation algorithm for multi-robot systems with an integrated obstacle avoidance is proposed. In this framework, the interaction among robots is limited to their visibility neighborhood, i.e., robots that are within the visibility range of each other. Furthermore, to better comply with the hardware\/software limitations of mobile robotic platforms, robots actuators are assumed to be saturated. A theoretical characterization of the main properties of the proposed swarm aggregation algorithm is provided. Simulations have been carried out to validate the theoretical results and experiments have been performed with a team of low-cost mobile robots to demonstrate the effectiveness of the proposed approach in real scenario."

In this work, a swarm aggregation algorithm for multi-robot systems based on local interaction is proposed. In the considered framework, a swarm of robots is moving within an outdoor environment according to the guidance commands issued by a remote control station. Robots, which are responsible for the execution of the guidance commands, are required to show a cohesive behavior through local interactions. A theoretical validation of the swarm interaction modeling is given. Simulations along with experimental results carried out by exploiting a Kinect® -based remote control system are provided to corroborate the theoretical results.

In this work, by starting from the theoretical framework proposed in [1], the problem of decentralized supervision of a Petri net through collaboration among supervisors is addressed. Communication is assumed to be available but limited to one-hop neighbors, i.e., supervisors reachable from one another with respect to a certain communication radius r. A sufficient condition to achieve decentralized admissibility by focusing the attention on the communication topology of the network of supervisors is provided. Furthermore, under the assumption of control under concurrent firing, a protocol to reach an agreement on the control input among the supervisors is proposed. Finally, a feasibility analysis for the implementation of the proposed decentralized control framework is discussed.

In this work the data aggregation problem for a multi-agent system within the framework of Theory of Evidence is investigated. In the proposed scenario, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, each agent is supposed to provide a set of observations which does not change over time. A protocol for distributed data aggregation for graph-like network topologies is designed. Experimental results with a sensor network have been carried out to corroborate the theoretical results and the feasibility of the proposed approach.

This paper investigates the data aggregation problem for a multi-agent system. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, agents are assumed to dynamically gather data over time, i.e., a dynamic scenario. A protocol for distributed data aggregation which is proved to converge to the basic belief assignment (BBA) given by a centralized aggregation based on the Transferable Belief Model (TBM) is provided.

In this paper the problem of multi-robot collaborative topological map-building is addressed. In this framework, a team of robots is supposed to move in an indoor office-like environment. Each robot, after building a local map by using infrared range-finders, achieves a topological representation of the environment by extracting the most significant features via the Hough transform and comparing them with a set of predefined environmental patterns. The local view of each robot which is significantly constrained by its limited sensing capabilities is then strengthened by a collaborative aggregation schema based on the Transferable Belief Model (TBM). In this way, a better representation of the environment is achieved by each robot with a minimal exchange of information. A preliminary experimental validation carried out by exploiting data collected from a self-made team of robots is proposed.

Multi-Robot Systems, i.e., collections of robots which cooperate to achieve a common goal, have become more and more appealing over the years to the robotics community. Research interests come from the several advantages that a multi-robot approach offers over traditional single complex robotic system approaches, such as a larger range of task domains or a higher robustness and flexibility. A primary concern is how to effectively achieve a cooperative behavior in multi-robot systems. Indeed, formation control turned out to be an important technique to achieve this goal. However, the majority of the approaches available in the literature assumes robots to be able to measure inter-distances among them. For this reason, in this paper we focus our attention on providing a reliable technique to compute inter-distances among robots. Experimental results, carried out by exploiting Xbee-Pro radio transceiver, are given to show the effectiveness of the proposed technique.

In this paper a novel approach to the problem of decentralized agreement toward a common point in space in a multi-agent system is proposed. Our method allows the agents to agree on the relative location of the network centroid respect to themselves, on a common reference frame and therefore on a common heading. Using this information a global positioning system for the agents using only local measurements can be achieved. In the proposed scenario, an agent is able to sense the distance between itself and its neighbors and the direction in which it sees its neighbors with respect to its local reference frame. Furthermore only point-to-point asynchronous communications between neighboring agents are allowed thus achieving robustness against random communication failures. The proposed algorithms can be thought as general tools to locally retrieve global information usually not available to the agents.

In this paper we present an algorithm for the exploration of an unknown graph with k robots, which is guaranteed to succeed on any graph, and which on trees we prove to be near-optimal for two robots, having optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any graph, and this is substantiated by simulations. For trees with n edges and radius r, the exploration time is equation, improving a recent method with equation [1], and almost reaching the lower bound equation. The algorithm is meant to be used in indoor navigation or cave search scenarios where the environment can be modeled as a graph. In this scenario, communication is realized by the devices being dropped by the robots at explored vertices, and the states of which are read and changed by further visiting robots. Simulations on Player/Stage platform have been performed in both tree and graph exploration which corroborate the mathematical results.

In this paper, a novel distributed algorithm to deal with the problem of estimating the network centroid in a multi-agent system is proposed. In this scenario, agents are assumed to be lacking any global reference frame or absolute position information. The proposed algorithm can be thought as a general tool to retrieve information about the centroid of a network of agents. Indeed, this allows to release several simplifying assumptions for a significant family of algorithms dealing with decentralized motion coordination. The convergence properties of the algorithm are carefully investigated in the case of a fully connected network for which a proof of convergence is provided. Successively, simulations to show the effectiveness of the algorithm also for arbitrary undirected connected graphs are given.

One of the most important problems in mobile robotics is to realize the complete robot's autonomy. In order to achieve this goal several tasks have to be accomplished. Among them, the robot's ability to localise itself turns out to be critical. The research community has provided, through the years, different methodologies to face the localisation problem, such as the Kalman filter or the Monte Carlo Integrations methods. In this paper a different approach relying on a specialisation of the genetic algorithms is proposed. The novelty of this approach is to take advantage of the complex networks theory for the spatial deployment of the population to more quickly find out the optimal solutions. In fact, modelling the search space with complex networks and exploiting their typical connectivity properties, results in a more effective exploration of such space.

A multirobot system composed by a five DoF manipulator (SCORTEC-ER 1) and a mobile base (ATRV-Jr by iRobot) is considered. The system is equipped with a uniform software architecture able to control the motion of both components. Some exteroceptive sensors like sonars and laser rangefinders are available on the mobile platform, some others are mounted on the end-effector of the manipulator: a colour camera, able to locate objects to operate with; an ultrasound range finder, near the camera, that will be used to get distance measures. In this paper we consider the problem of relocalising the mobile manipulator in a known environment supposing that no base movement are allowed to avoid hazard operations. As only the arm can be safely panned to explore the surroundings, the proposed procedure is based exclusively on the ultrasound rangefinder. Using such measures, and making the hypothesis that the arm configuration in known, a Monte Carlo filter is designed to estimate the base pose. Such multiple hypotheses filter is appropriate in this kind of problems where, in the best case, it is possible to retrieve a finite number of possible poses compatible with available measures.

In this work we propose a decentralized algorithm for balancing a strongly connected weighted digraph. This algorithm relies on the decentralized estimation of the left eigenvector associated to the zero structural eigenvalue of the Laplacian matrix. The estimation is performed through the distributed computation of the powers of the Laplacian matrix itself. This information can be locally used by each agent to modify the weights of its incoming edges so that their sum is equal to the sum of the weights outgoing this agent, i.e., the weighted digraph is balanced. Simulation results are proposed to corroborate the theoretical results.

Localization has been recognized as one of the most significant problems in mobile robotics. A robot must have a knowledge of its position in an environment in order to correctly perform almost any task. Moreover, having accurate information about the nodes positions turns out to be substantial in a sensor network context. In fact, several middle-ware services often rely on location knowledge to properly operate. In this paper, an overall scenario in which a robot is moving within a collaborative sensor network is considered. The aim is to provide an unified framework in which the robot can localize itself gathering information coming from the network, while nodes simultaneously try to find out their location. Simulation are provided in order to validate the effectiveness of the proposed framework.