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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, 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 study, the decentralized common reference frame estimation problem for multiagent systems in the absence of any common coordinate system is investigated. Each agent is deployed in a 2-D space and can only measure the relative distance of neighboring agents and the angle of their line of sight in its local reference frame; no relative attitude measurement is available. Only asynchronous and random pairwise communications are allowed between neighboring agents. The convergence properties of the proposed algorithm are characterized, and its sensitiveness against additive noise on the relative distance measurements is investigated. An experimental validation of the effectiveness of the proposed algorithm is provided.

We present two novel distributed algorithms for hole detection in a wireless sensor network (WSN) based on the distributed Delaunay triangulation of the underlying communication graph. The first, which we refer to as the distance-vector hole determination (DVHD) algorithm, is based on traditional distance vector routing for multi-hop networks and shortest path lengths between node pairs. The second, which we refer to as the Gaussian curvature-based hole determination (GCHD) algorithm, applies the Gauss-Bonnet theorem on the Delaunay graph to calculate the number of holes based on the graph's Gaussian curvature. We present a detailed comparative performance analysis of both methods based on simulations, showing that while DVHD is conceptually simpler, the GCHD algorithm shows better performance with respect to run-time and message count per node.

In this work, we address the connectivity maintenance problem for a team of mobile robots which move according to a given collective control objective. In our framework, the interaction among the robots is limited by a given visibility radius both in terms of sensing and communication. For this scenario, we propose a bounded control law which can provably preserve the connectivity of the multi-robot system over time even in the presence of any desired bounded control objective. Furthermore, we characterize the effects of the connectivity control term on the collective control objective, in terms of robustness of the desired control objective to the disturbance of the connectivity, by resorting to the set Input-to-State Stability framework (set-ISS). For the validation of the proposed bounded connectivity control law we consider the encirclement problem as an example of collective control objective. Simulations are provided to corroborate the theoretical results.

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 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 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 paper, we present a decentralized algorithm to estimate the eigenvalues of the Laplacian matrix that encodes the network topology of a multi-agent system. We consider network topologies modeled by undirected graphs. The basic idea is to provide a local interaction rule among agents so that their state trajectory is a linear combination of sinusoids oscillating only at frequencies function of the eigenvalues of the Laplacian matrix. In this way, the problem of decentralized estimation of the eigenvalues is mapped into a standard signal processing problem in which the unknowns are the finite number of frequencies at which the signal oscillates.

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 paper a novel algorithm for the decentralized estimation of the network centroid for a multi-agent system deployed in a d-dimensional space is presented. It is assumed that the agents are static, do not share a common coordinate system and are only able to estimate relative distance and angle of sight with respect to themselves during pairwise asynchronous communications. It is shown that if the graph representing the network topology is rigid, then the proposed algorithm solves the network centroid estimation problem.

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.

Preserving connectivity of a network topology is a crucial aspect for multi-robot systems in order to perform almost any collaborative task. This problem turns out to be significantly challenging in the case of a heterogeneous multirobot system equipped with different sensors with limited field of view. Such an interaction scheme, in fact, is described by directed graphs (digraphs), for which, a few approaches have still been presented in literature. This paper addresses the problem of estimating an approximated minimum strongly connected digraph contained in a given digraph. A novel decentralized approach is proposed and its suitability is confirmed by simulations.

This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.

In this work we focus on the topology control problem for robotic networks. In particular, we assume agents to be equipped with limited field of view sensors. As a consequence, directed graphs are required to model the robot-to-robot interaction. This significantly limits the applicability of algorithms developed for undirected graphs. In that view, we propose an auction-based solution for the decentralized estimation of an approximated minimum (in terms of number of links and in terms of a global cost function) strongly connected directed graph. This represents the first step towards the development of a connectivity maintenance framework for directed graphs. A theoretical analysis along with numerical simulations are provided 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 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.

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 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.

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.

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 the formation stabilization problem for a system of heterogeneous agents is considered. Agents are characterized by different linear dynamics, and assumed to be able to collaborate by exchanging information if they are within their range of communication. A sufficient algebraic condition for the stability of the formation based on a generalization of the Gerschgorin circle theorem for block matrices is proposed. Furthermore, conditions under which the formation remains stable under switching topology are investigated. Simulation results are given to corroborate the theoretical results.

Infrastructures are becoming more and more interoperable, while stakeholders are not aware of the overall behavior. In order achieve a global awareness, in this paper the need for cooperation is stressed; however, due to security and commercial issues, only few, very abstract data can be shared.In this paper a distributed interdependency estimation framework is provided, able to grant a minimal disclosure of data among the infrastructures, while letting operators make decisions with a wider perspective.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 the data aggregation problem for a multi-agent system is investigated. In this framework, 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 an observation which does not change over time, i.e., static 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 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.

In this paper the multi-robot localization problem is addressed. A new framework based on a spatially structured genetic algorithm is proposed. Collaboration among robots is considered and is limited to the exchange of sensor data. Additionally, the relative distance and orientation among robots are assumed to be available. The proposed framework (MR-SSGA) takes advantage of the cooperation so that the perceptual capability of each robot is extended. Cooperation can be set-up at any time when robots meet, it is fully decoupled and does not require robots to stop. Several simulations have been performed, either considering cooperation activated or not, in order to emphasize the effectiveness of the collaboration strategy.

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.

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.

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 self-localization problem for sensor networks is addressed. Given a set of nodes deployed in an environment, self-localization consists of finding out the location of all nodes in regard to any topology or metric of interest. Nodes are assumed to be equipped with a sensor board able to provide these inter-node distances. In addition, a few nodes are assumed to be equipped with some absolute position devices. According to this scenario, a new distributed algorithm based on an extended information filter is proposed. This algorithm provides an accurate estimation of node positions with a reasonable computational complexity, even when in presence of noisy measurements. Real experiments, carried out by exploiting Micaz Motes platforms, have been performed to show the effectiveness of the proposed technique.

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.