Human-multi-robot teaming in precision agriculture presents a promising approach to addressing labor shortages and managing the complexities of agricultural practices. An effective coordination of these teams, including task allocation and scheduling strategies while accounting for the inherent unpredictability of human behavior, is crucial for maximizing system productivity and ensuring user comfort. In this study, we introduce a Mixed-Integer Linear Programming (MILP) approach that aims to minimize workers’ waiting times, robots’ energy consumption during the different phases of the robots’ motions, and the overall makespan. To enhance the robustness of our framework and consider human preferences, a user interface is designed to capture real-time human feedback; then, an adaptive online updating strategy that dynamically adjusts plans responding to variations in human operators’ parameters is devised. To handle large-scale problems, we extend the solution approach by leveraging Constraint Programming (CP) combined with a batch decomposition strategy. The approach is validated through extensive simulations in a Unity-based realistic virtual reality environment and laboratory experiments using two TurtleBot2 robots and two human operators performing grape harvesting tasks.

Deploying a team of autonomous robots, operating collaboratively towards a common objective within dynamic environments, has the potential to improve the system efficiency across several fields. This paper proposes a distributed comprehensive framework enabling a networked multi-robot system to serve time-varying requests arising from different locations within the environment in a distributed and safe manner, i.e., by guaranteeing no collisions with possible obstacles and preserving connectivity among the robots. To this aim, a two-layer architecture is proposed where the top layer is in charge of distributively assigning new service requests to the robots by resorting to an auction-based algorithm, while the bottom layer is in charge of safely navigating the environment to serve the assigned requests by relying on Control Barrier Functions. However, the presence of connectivity constraints might affect the number of service requests that the multi-robot system can handle simultaneously and might lead to deadlock situations where robots cannot reach the designated locations due to loss of network connectivity. Hence, a distributed strategy based on consensus algorithms to detect and solve deadlocks in a distributed fashion is proposed. The completeness of the approach is proved. Simulation results in an agricultural setting and real-world laboratory experiments are provided to validate the effectiveness of the proposed approach.Note to Practitioners—This paper was inspired by the necessity to coordinate a team of robots to perform tasks within an unstructured agricultural field, including both the decision-making and navigation strategies, with no central control unit as envisioned by the European project CANOPIES. To this aim, a distributed approach is designed where robots only rely on local data and information from neighboring robots to assign and execute tasks effectively in a coordinated manner. In addition, as working under local communication constraints may prevent parallel execution of all tasks, potentially leading to deadlock situations, a distributed strategy is developed to enable each robot to detect and solve such situations. The proposed approach can be employed in several domains where the cooperation of multiple autonomous robots might be beneficial, ranging from logistics settings to search and rescue scenarios up to agricultural environments. Laboratory experiments with three robots demonstrate the effectiveness of the approach.

Weeds management is a repetitive and crucial task for agricultural settings. This paper considers a four-wheeled robot, called Vitirover, designed for grass-cutting and weed management tasks in vineyards. The robot steering mechanism employs differential rotation of rear wheels, mounted on a universal joint. First, the kinematic model of the robot is derived. Next, based on the kinematic model, a Model Predictive Control (MPC) formulation is designed to encourage the robot to follow a desired path while targeting weeds in the environment using dynamic weights. Simulation results in Gazebo simulator are provided to validate the overall system.

Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of latent planning systems. We rely on our Latent Space Roadmap (LSR) framework, which builds a graph in a learned structured latent space to perform planning. Given multiple LSR framework instances, that differ either on their latent spaces or on the parameters for constructing the graph, we use the action information as well as the embedded nodes of the produced plans to define similarity measures. These are then utilized to select the most promising plans. We validate the performance of our Ensemble LSR (ENS-LSR) on simulated box stacking and grape harvesting tasks as well as on a real-world robotic T-shirt folding experiment.

Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple heterogeneous agents which are characterized by different capabilities and/or embodiment. In this work, we propose a method to realize visual action planning in multi-agent settings by exploiting a roadmap built in a low-dimensional structured latent space and used for planning. To enable multi-agent settings, we infer possible parallel actions from a dataset composed of tuples associated with individual actions. Next, we evaluate feasibility and cost of them based on the capabilities of the multi-agent system and endow the roadmap with this information, building a capability latent space roadmap (C-LSR). Additionally, a capability suggestion strategy is designed to inform the human operator about possible missing capabilities when no paths are found. The approach is validated in a simulated burger cooking task and a real-world box packing task.

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware.

The federated learning (FL) paradigm aims to distribute the computational burden of the training process among several computation units, usually called agents or workers, while preserving private local training datasets. This is generally achieved by resorting to a server-worker architecture where agents iteratively update local models and communicate local parameters to a server that aggregates and returns them to the agents. However, the presence of adversarial agents, which may intentionally exchange malicious parameters or may have corrupted local datasets, can jeopardize the FL process. Therefore, we propose selective trimmed average (SETA), which is a resilient algorithm to cope with the undesirable effects of a number of misbehaving agents in the global model. SETA is based on properly filtering and combining the exchanged parameters. We mathematically prove that the proposed algorithm is resilient against data and local model poisoning attacks. Most resilient methods presented so far in the literature assume that a trusted server is in hand. In contrast, our algorithm works both in server-worker and shared memory architectures, where the latter excludes the necessity of a trusted server. The theoretical findings are corroborated through numerical results on MNIST dataset and on multiclass weather dataset (MWD).

This work addresses the problem of estimating soil moisture by requiring only limited information about the field and the surrounding environment. The measurements are assumed to be disturbed by the action of an external exogenous linear system that models changing weather conditions. All measurements are not provided by on-site sensors but by unmanned devices (UAVs) that plan their motion to cover the entire area. Since each UAV can only overview a portion of the field at any given time a data-driven intermittent observer is proposed to achieve the goal. In addition some considerations are introduced about the minimum number of devices that are required to obtain a data-driven description of the system. Numerical simulations are provided to corroborate the theoretical results

The use of heterogeneous human-multi-robot teams enables the combination of complementary skills of these two different types of agents. To have an effective collaboration, it is necessary to define a strategy for allocating and scheduling tasks among them. In this work, we distinguish robots in working robots and service ones: working robots and human operators can perform similar tasks in the environment and both are assisted by service robots. We propose a Mixed-Integer Linear Programming approach that aims to minimize the waiting times of the working agents, the energy consumption of the service robots, and the makespan while ensuring that the velocity constraints of the robots are met and the task ordering is correct. Furthermore, we propose an online updating strategy that tackles changes in the parameters of working agents and adapts the plan accordingly based on a heuristic algorithm. To validate our framework, we analyze a precision agriculture harvesting application with two human operators, two working robots, and two service robots.

In this work, we propose a route planning strategy for heterogeneous mobile robots in Precision Agriculture (PA) settings. Given a set of agricultural tasks to be performed at specific locations, we formulate a multi-Steiner Traveling Salesman Problem (TSP) to define the optimal assignment of these tasks to the robots as well as the respective optimal paths to be followed. The optimality criterion aims to minimize the total time required to execute all the tasks, as well as the cumulative execution times of the robots. Costs for travelling from one location to another, for maneuvering and for executing the task as well as limited energy capacity of the robots are considered. In addition, we propose a sub-optimal formulation to mitigate the computational complexity by leveraging the fact that generally in PA settings only a few locations require agricultural tasks in a certain period of interest compared to all possible locations in the field. A formal analysis of the optimality gap between the optimal and the sub-optimal formulations is provided. The effectiveness of the approach is validated in a simulated orchard where three heterogeneous aerial vehicles perform inspection tasks.Note to Practitioners—This paper aims at providing an efficient solution to PA needs by deploying a team of robots able to perform agricultural tasks at given locations in large-scale orchards. In particular, a novel general optimization problem is proposed that, given a set of mobile and possibly heterogeneous robots and a set of agricultural tasks to carry out, defines the assignment of these tasks to the robots as well as the routes to follow, while minimizing the total and the cumulative execution times of the robots. Existing approaches for route optimization in PA generally involves complete coverage of the field by one or multiple robots and do not account for maneuvering costs with general layouts of the field. We consider costs for travelling from one location to another, for executing the task and for maneuvering without any restriction on the layout of the plants as well as we take into account the limited energy capacity of the robots. We also provide a sub-optimal formulation which reduces the computational burden by relaxing the optimization of the maneuvering costs at the locations where agricultural tasks are carried out and formally derive the optimality gap. The proposed approach is flexible and can be easily adapted to any PA setting involving multiple mobile robots that are required to accomplish given tasks in an area of interest. We validate its effectiveness in a realistic simulated setup composed of three heterogeneous aerial vehicles performing inspection tasks. In future research, we aim to design algorithms to solve the proposed optimization problems in an efficient manner as well as to validate the formulations on real-world robotic platforms.

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient.Note to Practitioners—This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications.

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of(r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings.

In this article, we address the problem of coordinating the motion of a team of robots with limited field of view (FOV), which inducesasymmetryin their interactions. In this context, we first propose a general coordinated motion framework for multirobot systems with triangular FOV capable of guaranteeing stability under asymmetric (directed) interactions. In deriving this framework, we illustrate that asymmetry in multirobot interactions can lead to degenerate configurations for which a fully decentralized controller may be insufficient to achieve coordination. Thus, we introduce a switching control mechanism that achievesadaptive decentralization, enabling collaborative behaviors that seek support of a centralized planner for situations that are inherently unstable (degenerate). To demonstrate the generality of our framework we provide a case study involving varying team objectives, such as topology control, that the robots can achieve with limited FOV, while remaining stable. Experimental and numerical validations based on the previously discussed case study are provided to corroborate the theoretical findings

In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a novel Multi-Platform Steiner Traveling Salesman Problem (MP-STSP) where, in order to guarantee the exploitation of multiple moving platforms and the minimization of the overall operational time, the proposed formulation explicitly takes into account the time required to perform each task. By doing so, the computed itineraries attempt to balance the workload among the deployed platforms. Comparative simulations, inspired by the needs of the EU H2020 Project PANTHEON1, are provided to numerically demonstrate the effectiveness of the proposed planning strategy for an orchard precision agriculture setting.

In this paper, we address the problem of optimal topology selection for stable coordination of multi-robot systems with asymmetric interactions. This problem arises naturally for multi-robot systems that interact based on sensing, e.g., with limited field of view (FOV) cameras. From our previous efforts on motion control in such settings, we have shown that not all interaction topologies yield stable coordinated motion when asymmetry exists. At the same time, not all robot-to-robot interactions are of equal quality, and thus we seek to optimize asymmetric interaction topologies subject to the constraint that the topology yields stable multi-robot motion. In this context, we formulate an optimal topology selection problem (OTSP) as a mixed integer semidefinite programming (MISDP) problem to compute optimal topologies that yield stable coordinated motion. Simulation results are provided to corroborate the effectiveness of the proposed OTSP formulation.

In this work we consider a swarm of agents shaped as bars with a certain orientation in the state space. Members of the swarm have to reach an aggregate state, while guaranteeing the collision avoidance and possibly achieving an angular consensus. By relying on a segment-to-segment distance definition, we propose a control law, which guides the agents towards this goal. A theoretical analysis of the proposed control scheme along with simulations and experimental results is provided. The proposed framework can be used to model several application scenarios ranging from collaborative transportation to precision farming, where each agent may represent either a large robot or a group of robots intent to carry bar-like shaped loads. Representative examples include: a fleet of robot-teams performing a collaborative object transportation task in an automated logistic setting, or a fleet of autonomous tractors each carrying a large atomizer to spray chemical products for pest and disease control in a precision farming setting.

In this paper, we demonstrate how topological constraints, as well as other abstract constraints, can be integrated into task allocation by applying the combinatorial theory of matroids. By modeling problems as an intersection of matroid constraints, arbitrary combinatorial relationships can be achieved in the task allocation space. To illustrate the expressiveness of the framework, we model a novel task allocation problem that couples abstract per-robot constraints with a communication spanning tree constraint. As our problem is cast as a matroid intersection, provable optimality bounds with simple greedy algorithms follows immediately from theory. Next, we present a decentralized algorithm that applies auction methods to task allocation with matroid intersections. Simulations of task allocation for surveillance in urban environments demonstrate our results. Finally, Monte Carlo results are provided that indicate greedy task allocations can be highly competitive even with near-optimal solutions in practice.

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 ideal conditions. We indicate how robots could be scheduled optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival rate. We then consider the setting where the arrival rate is 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. We show through analysis and simulations the conditions under which CBMF can stabilize the network, and its corresponding delay performance. From a practical point of view, we propose a heuristic approach to adapt the epoch duration according to network conditions that can improve the end-to-end delay while guaranteeing the network stability at the same time. We also study the structural properties with its explicit delay performance of the CBMF algorithm in a homogeneous network.

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.

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.

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.

Motivated by the strong influence network rigidity has on collaborative systems, in this paper, we consider the problem of partitioning a multiagent network into two sub-teams, a bipartition, such that the resulting sub-teams are topologically rigid. In this direction, we 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 feasible direction for graph search. Adapting a supergraph search mechanism, we then detail a methodology for discerning graphs cuts that represent valid rigid bipartitions. Next, we extend our methods to a decentralized context by exploiting leader election and an improved graph search to evaluate feasible cuts using only local agent-to-agent communication. Finally, full algorithm details and pseudocode are provided, together with simulation results that verify correctness and demonstrate complexity.

In this paper, we address the decentralized and parallel construction of rigid graphs in the plane that optimize an edge-weighted objective function under cardinality constraints. Two auction-based algorithms to solve this problem in a decentralized fashion are first proposed. Centered around the notion of leader election, the first approach finds an optimal solution through a greedy bidding, while the second approach provides a sub-optimal solution which reduces complexity according to a sliding mode parameter. Then, by exploiting certain local structural properties of graph rigidity, a parallelization to build a portion of the optimal solution in constant time is derived. A theoretical characterization of algorithm performance is provided together with complexity analysis. Finally, simulation results are presented to corroborate the theoretical findings.

In this paper, we address the construction of optimally rigid networks that minimize an edge-weighted objective function over a planar graph. We propose two auction-based algorithms to solve this problem in a fully decentralized way. The first approach finds an optimal solution at the cost of high communication complexity; the second approach provides a sub-optimal solution while reducing the computational burden according to a sliding mode parameter ζ, yielding a tradeoff between complexity and optimality. A theoretical characterization of the optimality of the first algorithm is provided, and a closed form for the maximum gap between the optimal solution and the sub-optimal solution is also given. Simulation results are presented to corroborate the theoretical findings.

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. We demonstrate our propositions through simulation under a realistic wireless network regime.

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.

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 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.

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(n2) 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 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, 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 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.

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.

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.

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 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 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.

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,k-1), thereby improving a recent method with time O((e/logk) + r) [2], and almost reaching the lower bound max((2e/k), 2r). The model underlying undirected-graph exploration is a set of rooms connected by opaque passages; thus, the algorithm is appropriate for scenarios like indoor navigation or cave exploration. In this framework, communication can be realized by bookkeeping devices being dropped by the robots at explored vertices, the states of which are read and changed by further visiting robots. Simulations have been performed in both tree and graph explorations to corroborate the mathematical results.

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 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.

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.

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 2n/k + O(rk-1), improving a recent method with O(n/log k + r) [1], and almost reaching the lower bound max (2n/k, 2r). 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 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 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.

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.

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.

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.