Publications Lists     [ all BibTeX ]  

The Multi–Rate Support feature has been introduced by the IEEE 802.11 standard to improve system performance, and has been widely exploited by means of Rate Adaptation (RA) strategies within general purpose Wireless LANs. These strategies revealed ineffective for real–time industrial communications, and alternative solutions, better tailored for such a specific field of application, were investigated. The preliminary outcomes of the analyses carried out were promising, even if they clearly indicated that further efforts were necessary. In this direction, this paper firstly proposes Rate Selection for Industrial Networks (RSIN), an innovative RA algorithm specifically conceived for the real–time industrial scenario with
the goal of minimizing the transmission error probability, while taking into account the deadline imposed to packet delivery. Then, it describes the practical implementation of RSIN on commercial devices, along with that of other formerly introduced RA techniques. Finally, the paper presents a thorough performance
analysis, carried out to investigate the behavior of the addressed RA schemes. Such an assessment was performed via both experimental campaigns and simulations. The obtained results, on the one hand, confirm the effectiveness of the RA techniques purposely designed for real–time industrial communication. On the other hand, they clearly indicate that RSIN outperforms all the other strategies.

This paper aims to delineate a comprehensive method that integrates machine vision and deep learning for quality control within an industrial setting. The innovative approach proposed in this solution leverages a microservice architecture that ensures adaptability and flexibility to different scenarios while focusing on the employment of affordable, compact hardware, and achieves exceptionally high accuracy in performing the quality control task keeping a minimal computational time. Consequently, the developed system operates entirely on a portable smart camera, eliminating the need for additional sensors like photocells and external computation, which simplifies setup and commissioning phases and reduces the overall impact on the production line. By leveraging the integration of the embedded system with the machinery, this approach offers real-time monitoring and analysis capabilities, facilitating swift detection of defects and deviations from desired standards. Moreover, the low-cost nature of the solution makes it accessible to a wider range of manufacturing enterprises, democratizing quality processes in Industry 5.0. The system has been successfully implemented and is fully operational in a real industrial environment and the experimental results obtained from this implementation are also presented in the work.

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.

In fast nonlinear model predictive control the sensitivitycomputation is one of the key aspects to reducecomputational burden, in fact specific automated andefficient procedures for that have been developed. Howeverthe number of sensitivity computations required toadequately approximate the nonlinear dynamics is typicallyhigh and fixed a priori. In this paper, we developa sensitivity updating scheme capable of reducing thenumber of sensitivity computations exploiting an onlinecurvature-based measure of nonlinearity of the system.The proposed strategy is applied to the sequentialquadratic programming framework with specific attentionto the Real-Time Iteration implementation. Simulationson the inverted pendulum benchmark show asignificant reduction of the number of the sensitivity updates,hence a reduction of the overall computationaltime.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their eectiveness for a wide range of real– time industrial communication applications. Nonetheless, the introduction of the important IEEE 802.11n amendment, which is commonly implemented in commercial devices, has not been adequately addressed in this operational framework yet. IEEE 802.11n encompasses several enhancements both at the physical (PHY) and medium access control (MAC) layers that may bring considerable improvements to the performance of WLANs deployed in real–time industrial communication systems. To this regard, in this paper we present a thorough investigation of the most important IEEE 802.11n features, addressing in particular specific performance indicators, such as timeliness and reliability, that are crucial for industrial communication systems. To this aim, after an accurate theoretical analysis, we implemented a suitable experimental setup and carried out several measurement sessions to obtain an exhaustive performance assessment. The outcomes of these experiments, on the one hand revealed that the adoption of IEEE 802.11n can actually provide significant improvements to the performance of the IEEE 802.11 WLAN in the industrial communication scenario. On the other hand, the assessment allowed to select, among the various options of IEEE 802.11n, the parameter settings which may ensure the best behavior in this specific (and demanding) field of application.

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control, and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a stabilizing online distributed control law. Finally, numerical examples involving a cubic formation following a straight path in the 3D space are provided to validate the proposed control strategies.

We propose a unified framework for time-varying convex optimization based on the prediction-correction paradigm, both in the primal and dual spaces. In this framework, a continuously varying optimization problem is sampled at fixed intervals, and each problem is approximately solved with a primal or dual correction step. The solution method is warm-started with the output of a prediction step, which solves an approximation of a future problem using past information. Prediction approaches are studied and compared under different sets of assumptions. Examples of algorithms covered by this framework are time-varying versions of the gradient method, splitting methods, and the celebrated alternating direction method of multipliers (ADMM).

The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.

Industrial applications aimed at real–time control and monitoring of cyber–physical systems pose significant challenges to the underlying communication networks in terms of determinism, low latency and high reliability. The migration of these networks from wired to wireless could bring several benefits in terms of cost reduction and simplification of design, but currently available wireless techniques cannot cope with the stringent requirements of the most critical applications. In this work, we consider the problem of designing a high–performance wireless network for industrial control, targeting at Gbps data rates and 10 ?s–level cycle time. To this aim, we start from analysing the required performance and deployment scenarios, then we take a look at the most advanced standards and emerging trends that may be applicable. Building on this investigation, we outline the main directions for the development of a wireless high performance system.