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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 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 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.

We formulate the problem of robots ferrying messages between statically-placed source and sink pairs that they can communicate with wirelessly. We first analyze the capacity region for this problem under both ideal (arbitrarily high velocity, long scheduling periods) and realistic conditions. We indicate how robots could be scheduled optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival rates. We find that if the number of robots allocated grows proportionally with the number of source-sink pairs, then the capacity of the network scales as $\Theta(1)$, similar to what was shown previously by Grossglauser and Tse for uncontrolled mobility; however, in contrast to that prior result, we also find that with controlled mobility this constant capacity scaling can be obtained while ensuring finite delay. We then consider the setting where the arrival rates are unknown and present a coarse-grained backpressure message ferrying algorithm (CBMF) for it. In CBMF, the robots are matched to sources and sinks once every epoch to maximize a queue-differential-based weight. The matching controls both motion and transmission for each robot: if a robot is matched to a source, it moves towards that source and collects data from it; and if it is matched to a sink, it moves towards that sink and transmits data to it. We show through analysis and simulations the conditions under which CBMF can stabilize the network. We show that the maximum achievable stable throughput with this policy tends to the ideal capacity as the schedule duration and robot velocity increase.

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.

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 investigate set Input to State Stability (set-ISS) in the context of multi-agent systems, specifically when agent interaction is spatial in nature. As a case study we consider an aggregative objective with collision avoidance, where it is assumed that interacting agents are symmetrically disturbed. By considering constraints on physical occupancy in the workspace, we obtain insights into the tradeoffs of disturbance, collective behaviors, and the parameters of interaction. Finally, a feedback loop is generated using the disturbance information yielded by set-ISS analysis to strengthen cohesion against disturbance.

We consider the problem of controlling the motion of a set of robots to ferry messages between a given set of statically-placed nodes. The design and analysis of an arrivalrate unaware throughput-optimal policy for this problem is challenging because of the coupling between position and link rate. We propose a fine-grained backpressure message ferrying algorithm (FBMF) for joint motion and transmission control of robots. Unlike traditional backpressure settings, because the controlled motion of the relay nodes changes the channel rates, it turns out that the conventional approach to prove throughput optimality does not work in this problem setting. We prove for the simplest setting (single-flow, single-robot, constant arrival) that this policy indeed achieves throughput optimality. The analysis reveals that under feasible traffic, even when queues are highly over-loaded, the change in the total queue size can be positive over a time step, nevertheless the system exhibits a limit-cycle behavior and stability holds because the change in the total queue size is negative over the cycle for sufficiently large queues. We pose the design and analysis of a throughput optimal policy for the general case as a challenging open problem for network theory.

In this paper, a novel wireless sensor network synchronization protocol for event-driven measurement applications is proposed. The objective is twofold: 1) to provide high accuracy in the area where an event is detected and 2) to ensure a long network lifetime. The complexity of the problem arises from the fact that these two properties are usually in conflict. To increase the synchronization accuracy, nodes are required to exchange synchronization packets at higher rate, thus impacting the network lifetime. Vice versa to ensure a long network life time, the number of packets to be exchanged should be minimized thus impacting the synchronization accuracy. A tradeoff can be achieved by observing that the packet rate should be increased only for the portion of the network surrounding the detected events as only these nodes require a higher accuracy to collect data. The proposed algorithm represents a formalization of this idea. Numerical and experimental results are provided to validate the effectiveness of the proposed algorithm. 0018-9456 © 2014 IEEE.

In this work we consider a multi-robot system composed of three robotic units running the seminal swarm aggregation control law proposed in [1] for which a pairwise interaction is considered among each couple of units. In particular, we provide a characterization of all the possible equilibria for such a system while varying the parameters of interaction and investigate their stability properties. Simulations along with experimental results are given to corroborate the theoretical analysis.

"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 paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime., 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 2014

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.

This paper focuses on the integration of a cooperative swarm methodology initially developed for small ground mobile robots, with a virtual target based path-following guidance system developed for marine surface vehicle systems. After separately describing the approaches, for swarm aggregation and path-following guidance respectively, the paper reports the effortless procedure to perform the integration of the proposed techniques. Simulative and experimental results are then proposed to prove the effectiveness of the proposed methodology.

The problem of reaching an agreement in a multiagent system has been widely investigated. Opinion Dynamics is a similar problem where agents are assumed to interact in order to reach an agreement on their opinion. The key aspect of this formulation is that the time-varying network topology describing the interaction among agents is defined according the closeness of their opinion. This model aims to describe the process which takes place when human beings work together to reach an agreement on their standpoints. A well-established approach is to consider the opinion of each human being through a numeric value. However, this model does not embody the inherent uncertainty of the human reasoning process. Indeed, the complex facets of human opinions, often expressed in linguistic ways, are heavily affected by vagueness and ambiguity. In this paper, resorting to the fuzzy theory, a framework to model the agreement of agents with vague opinions is provided. This represents a generalization of the original opinion dynamics modeling. Similarities with both the consensus and the original opinion dynamics problems are presented.

This work presents the design of a cooperative control policy for a group of heterogeneous vehicles given by a set of ground robots able to physically interact with an aerial robot by applying forces trough tethered cables governed by actuated winches. It is shown how different geometric configurations of the group may lead to possibly different dynamical properties of the overall system which can be exploited to improve the achievable closed-loop performances in certain situations. Suitable control allocation problems are then formulated in which the ground robots are considered as real mobile actuators able to improve the controllability of the aerial vehicle and to help it counteracting external disturbances such as wind.

This paper describes a decentralized approach to control the execution of a set of tasks for a network of heterogeneous robotic agents, i.e., agents with different sets of sensors and actuators. The proposed approach is based on a discrete-event modeling and control framework. The novelty is the full decoupling between the tasks modeling and their deployment. This implies an higher robustness to faults in the network of agents and a more systematic way for the task modeling regardless of the actual capability of the agents. A consensus-based mechanism is exploited in the control system to achieve coherence among the agents state and synchronize the agents action.

We propose a swarm aggregation algorithm based on local interactions in the presence of saturations on the robot actuators. This assumption allows to better model the physical limitations of actual mobile robotic platforms. In our framework, robot-to-robot interactions are limited to the visibility neighborhood, i.e., to robots that are within the range of visibility of each other. A theoretical analysis of the convergence properties is presented for the proposed swarm aggregation algorithm. Extensive simulations have been performed to corroborate the theoretical results. In addition, experiments with a team of low-cost mobile robots have been carried out to show the effectiveness of the proposed approach.

In this work, the decentralized control law proposed in [Sabattini et al., 2011b] for multi-robot connectivity maintenance is enhanced by means of a selective action. The idea is to identify critical agents, i.e. agents for which a disconnection might cause the split of the communication graph, and limit the control action to those agents. The objective is twofold: to reduce the control effort introduced by the connectivity maintenance control action as well as to avoid unnecessary action of the connectivity maintenance controller, thus reducing its effect on the overall performances of the system. A theoretical analysis of the proposed control law is discussed. Simulations along with experimental results are given to corroborate the theoretical results.

In this paper the simultaneous roll reduction and course keeping problem for a surface vessel by means of active fins and rudder is addressed. This work extends the results proposed in [Carletti et al. (2010)] by considering an alternative nonlinear multivariable ship dynamics where the wave effects are modeled as forces and moments affecting the system dynamics rather than as disturbances affecting the control inputs. A theoretical analysis of the boundedness of the zero dynamics along with the design of a sliding mode control are proposed. Simulations results are provided to corroborate the theoretical analysis.

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.

Wireless Sensor Networks (WSNs) are at the forefront of emerging technologies due to the recent advances in Microelectromechanical Systems (MEMSs). The inherent multidisciplinary nature of WSN attracted scientists coming from different areas stemming from networking to robotics. WSNs are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking. Most of these applications require basic services such as self-localization or time synchronization. The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. In this paper, the self-localization problem for sensor networks is addressed. A distributed formulation based on the Information version of the Kalman Filter is provided. Distribution is achieved by neglecting any coupling factor in the system and assuming an independent reduced-order filter running onboard each node. The formulation is extended by an interlacement technique. It aims to alleviate the error introduced by neglecting the cross-correlation terms by "suitably” increasing the noise covariance matrices. Real experiments involving MICAz Mote platforms produced by Crossbows along with simulations have been carried out to validate the effectiveness of the proposed self-localization technique.

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.

Motion planning is an important step in any complex robotic motion task. Many algorithms deal with this problem and a lot of effective approaches makes use of random generation of roadmaps or motion commands. In this paper, a novel algorithm for random roadmap generation is proposed. This approach, which addresses the planning problem with a resilience philosophy, relies on a network model with some particular topological properties. These properties of robustness against random faults and intentional attacks are functional to devising a suitable solution for the planning problem. Comparative simulations against several algorithms have been performed to show the effectiveness of the proposed approach.

Wireless Sensor Networks (WSN) are at the forefront of emerging technologies due to the recent advances in Micro-Electro-Mechanical Systems (MEMS) technology. WSN are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking.The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. The strength of a sensor network, which turns out to be also its weakness, is the capability to perform inter-node processing while sharing data across the network. However, the limited reliability of a node, due to the low-cost nature of the hardware components, drastically constrains this aspect. For this reason, the availability of a mechanism to build distributed robust connectivity topologies, where robustness is meant against random failures of nodes and intentional attacks of nodes, is crucial. The complex network theory along with the percolation theory provides a suitable framework to achieve that. Indeed, topologies such as multi-modal and scale free ones, show interesting properties which might be embedded into a sensor network to significantly increase its robustness. In this work, a mechanisms to build robust topologies in a distributed fashion is proposed, its effectiveness is analytically investigated and results are validated through simulations.

In the last few years Critical Infrastructures have become more and more tightly interconnected and their protection is one of the major issues for the national and international security. In order to achieve that, many modeling techniques for the interdependencies existing among them have been proposed. Now, a crucial issue is how to use such models to develop tools able to estimate the status of key elements of CIs, quantify the possible threats and suggest adequate countermeasures to human operators and actors. Due to security, commercial and technological aspects, the only feasible approach is to provide distributed and interconnected state/interdependency estimators. In this paper the general problem of the state estimation of interconnected systems sharing the same model is introduced. A first step in the solution of such a challenging problem is then provided in the case of linear systems. The final objective of this research is to define an effective framework for the problem at hand, and then implement and validate an on-line distributed state/interdependency estimator within the EU IST MICIE project.

In this paper, a novel genetic algorithm based on a “collaborative” fitness-sharing technique to deal with the multi-robot localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment.

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons. The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

In this paper the multi-robot localization problem is addressed. A new biology-inspired approach is proposed and implemented: the bacterial colony growth framework (BCGF). It takes advantage of the models of species reproduction to provide a suitable framework for carrying on the multi-hypothesis, along with proper policies for both autonomous and collaborative contexts. Collaboration among robots is obtained by exchanging sensory data and their relative distance and orientation. This information is integrated into the framework in such a way that the convergence aptitude is enhanced. Several simulations in different environments have been performed, comparing autonomous and collaborative localization, along with proper statistical analysis for performance assessment.

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.

Localisation is one of the most important tasks to be accomplished in order to realize the complete autonomy of a mobile robot. In this paper, a new strategy for global localisation is proposed. Applying this method a robot is able to safely initialise its position or relocalise itself in case of recovery of pose tracking failure. The algorithm presented adopts a hybrid approach. First a particle filter is used to generate hypotheses on the possible pose supposing that no movements are allowed to avoid collisions. Thereafter safe trajectories are planned and executed to reduce the remaining ambiguities while the hypotheses are monitored and validated by a set of parallel extended Kalman filters. The novelty of this approach stands on the ability to generate the pose hypotheses without any feature-based knowledge. As a consequence, a landmark-based description of the environment is no longer required for the algorithm execution.

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.

Localisation, i.e., estimating a robot pose relative to a map of an environment, is one of the most important problems in mobile robotics. The literature offers several possible approaches to deal with such task, and optimal algorithms can be devised relying on particular constraints (linear state equations, Gaussian posterior density, etc.). Here, we propose a preliminary study for an algorithm able to approximate a large number of probability distributions when a map of the environment is available. This work improves the particle filters strategies presented in literature, reducing the number of particles needed to solve the localisation problem. The algorithm relies upon a suitable clustering of the particle set and a genetic approach for the resampling step.

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.