This article deals with the challenge of a fair and efficient reallocation of resources in a multiagent system (MAS). A novel dynamic control framework is introduced in which the state of each agent evolves according to a distributed control law. This law ensures the allocation of resources according to predefined weights and promotes a fair distribution among the agents. The control strategy is thoroughly analyzed to show that resource allocation remains well-defined, avoids singularities, and ensures consistent performance. Moreover, it is proved that the total resource allocated remains constant, showing that the system can preserve a crucial invariant throughout its evolution. Theoretical results validate the approach and show its adaptability to changing agent states while maintaining the overall resource constraints. A key advantage of the proposed strategy is that the total amount of resources matching the initial distribution remains unknown to the agents. Instead, through emergent behavior resulting from interaction with other agents, each agent uses an appropriate amount of the resource based on its own requirements. This work contributes to an effective control strategy to achieve stable and fair resource allocation in multiagent networks.

This paper introduces an error positioning analysis making use of a proposed cooperative multi-technology localization technique that leveraging WiFi, Ultra WideBand (UWB), LIght Detection And Ranging (LIDAR) signals and an Extended Kalman Filter (EKF) is able to guarantee a more precise positioning estimation and a signal map reconstruction. The analysis of the positioning error aims to show how the quality of the used sensors can affect the values estimated by the filter. In particular, we have considered three different classes of WiFi receiver sensitivity for evaluating how the receiver quality can affect the error positioning estimation. Moreover, we have also varied the number of WiFi emitters giving an indication of the goodness of the proposed approach in respect with other techniques. The numerical simulations are carried out considering a system in which sensors with different quality of sensing are taken into account. The results obtained have shown the greater robustness of the proposed strategy compared to others based on multilateration, especially when dealing with low-quality sensors, that is receiver with bad sensitivity, and with different number of WiFi emitters inside the considered area. It emerges that the adopted approach leads to acceptable results even in the presence of unreliable sensor measurements.

This paper presents a new software framework for HRI experimentation with the sixth version of the common NAO robot produced by the United Robotics Group. Embracing the common demand of researchers for better performance and new features for NAO, the authors took advantage of the ability to run ROS2 onboard on the NAO to develop a framework independent of the APIs provided by the manufacturer. Such a system provides NAO with not only the basic skills of a humanoid robot such as walking and reproducing movements of interest but also features often used in HRI such as: speech recognition/synthesis, face and object detention, and the use of Generative Pre-trained Transformer (GPT) models for conversation. The developed code is therefore configured as a ready-to-use but also highly expandable and improvable tool thanks to the possibilities provided by the ROS community. The code repository is: https://github.com/antbono/OAN

In this article, a novel approach to generating kinematic state trajectories for single-integrator multiagent systems is developed with the aim of addressing cooperative surveillance tasks of rectangular areas. In particular, the key idea consists in driving the involved agents within a so-called containment region while simultaneously reaming outside a forbidden area around the prescribed target. As one of its main features, the proposed kinematics allows the whole multiagent configuration to rotate safely along the perimeter under both full and partial connectivity properties of the underlying topology graph. Steady-state conditions are analyzed and sufficient conditions are derived in terms of kinematic model parameters. Finally, a set of simulations is aimed at showing the capability of the kinematic descriptions to quickly buttonhole the containment region and to keep a rotating behavior around the target.

In this paper, the resilient control problem for constrained cyber-physical systems subject to stealthy data intrusions on the communication channels is considered. The key idea consists in designing a neural network to act as the anomaly detector during the on-line operations. Accordingly the controller unit, based on model predictive control arguments, is developed to take advantage of the resulting detection capabilities. As its main merits are concerned, the overall control architecture has a two-fold merit with respect to the existing literature: it is avoided the need of modifying the detector structure whenever a different class of attacks is considered, and the occurrence of false positive events is significantly mitigated. Finally, a numerical example is provided to show the effectiveness and peculiarities of the proposed approach.

This paper presents a methodology to generate proper state trajectories of a swarm of agents, described as a single integrator model. The main goal of the proposed methodology is to route the agents into planar polygons like areas, so that to accomplish for cooperative perimeter surveillance or target capturing tasks. In particular, the agents are driven into a polygonal strip defined by a given containment external convex polygonal region and an internal forbidden convex polygonal region around a prescribed target. The control law is designed looking at a circular strip, then a conversion from circular to polygonal areas is applied to achieve the prescribed goal. Steady-state conditions are analyzed and sufficient conditions derived in terms of the control law parameters. Simulation results put in light the main properties of the resulting kinematic state description.

Abstract In this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or P n P Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the P n P problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.

This paper proposes a kinematic model for a swarm of agents able to exhibit the formation of vortices around a given reference trajectory and to deal with uncertainty in the reference information. One of the main novelties of this model is the use of two coordinates coupling matrices that weight the attraction to a given reference trajectory and the interaction among agents. Each agent may exchange information with all the other agents in its neighbourhood and this neighbourhood is described by a detection range related to the proximity sensor that each agent is equipped with. The communication topology graph evolution is studied too and sufficient conditions to ensure both the persistence of connections between the agents and the achievement of a complete graph are provided. Moreover the swarm aggregation and steady-state properties are described. Numerical simulations illustrate the obtained results.

In this paper a second-order model for a swarm of agents is proposed to face the team reference tracking problem. The described model is a double integrator where each agent dynamic is ruled by a control input formed by attractive parts to the reference position and speed, and by a repulsive part that rules the interactions among agents. It will be proved that following the proposed model, the swarm centroid will asymptotically reach the reference trajectory and the agents' speeds will reach a consensus on the reference speed while their positions will be fixed in a reference frame that moves according to the reference trajectory. The main novelty of the proposed work is the interaction term among agents, the effect of which can be easily tuned by modifying an interaction matrix that modulates the effect of agents interactions on their various components. Properly choosing this matrix, aggregation properties can be modified to ensure that all the agents enter and remain in a defined zone around the reference trajectory, moving according to it.

This paper focuses on the trajectory tracking problem for a swarm of mobile agents. A kinematic model describing the interactions and evolutions of the swarm members is proposed and its main properties are analyzed emphasizing that the agents centroid is ensured to track in finite-time a given reference trajectory and that the agents reach an aggregation in finite-time in a hyper-ball moving around the centroid path. One of the main characteristics of the model is the presence of an interaction matrix, between agents coordinates, which allows to define some properties of the swarm allowing the creation of different forms of agents aggregations, i.e., spheres, ellipsoids, straight lines, etc. Indeed swarm properties related to the agents configuration around the performed path along with agents interactions and absence of collisions are analyzed depending on the chosen interaction matrix.

In this paper, a novel path planning and obstacles avoidance method for a mobile robot is proposed.This method makes use of a switching strategy between the attractive potential of the target and a new helicoidal potential field which allows to bypass an obstacle by driving the robot around it.The new technique aims at overcoming the local minima problems of the well known artificial potentials method, caused by the summation of two (or more) potential fields.In fact, in the proposed approach, only a single potential is used at a time.The resulting proposed technique uses only local information and ensures high robustness, in terms of achieved performance and computational complexity, w.r.t. the number of obstacles.Numerical simulations and comparisons with traditional artificial potential field technique confirm a robust behavior of the method, also in the case of a framework with multiple obstacles.

In this paper the problem of estimating the relative orientation and position between a camera and an object is investigated. It is assumed that both the camera and the object are provided with an Inertial Measurement Unit (IMU) capable of measuring their attitude with respect to the gravity and the earth magnetic vectors. Furthermore, the object is assumed to contain a feature of n points, the position of which is known in the object coordinate frame. An algorithm is proposed, which uses the image provided by the camera and the information provided by the IMUs, to solve the PnP problem, i.e., to estimate the relative pose of the object in the camera reference frame. Two special cases will be studied. The first is the case where all the attitude information given by the IMU is used. In the second case only the measurements provided by inclinometers are used, neglecting those coming from the magnetometers, because they are usually quite noisy. The effectiveness of the proposed algorithms is tested either by numerical simulations and by experimental tests with cameras.