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International Conference on Intelligent Transportation

作者： K. Binjaku E.K. Meçe C. Pasquale S. Sacone Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

A modelling framework for freeway traffic systems specifically oriented to the definition of suitable control schemes is developed in this paper. The traffic system includes different vehicle classes (passengers and freight vehicles) whose behaviour is explicitly modelled. Vehicles can enter the free-way arriving from the mainstream or through on-ramps on which traffic volumes may be regulated with ramp metering techniques. Moreover, Artificial Intelligence (AI) techniques and, specifically a Machine Learning (ML) approach, are the modelling methodology proposed in this work. More in detail, a hybrid model is defined in which a physics-based component, provided by a multi-class version of METANET model, is added to a machine learning model as a regularization component and the derived model is trained to reproduce the behavior of different classes of users. The effectiveness of the hybrid model is shown by training and testing it with real traffic data.

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An Optimization Model for the Optimal Control of the Oxygen Excess Ratio in a Hydrogen Fuel Cell System

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IFAC-PapersOnLine 2024年第13期58卷 496-501页

作者： Yassine Ennassiri Giulio Ferro Loredana Magistri Michela Robba DIBRIS—Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa 16145 Italy Thermochemical Power Group (TPG) University of Genoa Via Montallegro 1 16145 Genoa Italy

This paper focuses on the control of a fuel cell system, for which an optimization model is proposed to control the oxygen excess ratio in order to tackle the fuel cell starvation phenomenon and increase the net power production of the system. The optimization problem considers the detailed nonlinear dynamics of the stack and the air supply sub-system as constraints and upper and lower bounds to consider the technical limitations in the system’s operation. The goal is to maintain the oxygen excess ratio at its reference value, using the compressor input voltage as a control variable, under several load variations of the current demand, considered as the external noise to the system. The proposed nonlinear optimization problem has been applied to a 75 kW stack used in the Ford P2000 fuel cell prototype vehicles, and results have been compared to the proportional-integral (PI) control technique. The proposed control approach has shown a significant enhancement in the control performance of the oxygen excess ratio.

关键词： fuel cell nonlinear programming optimization oxygen excess ratio

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Self representation and interaction in immersive virtual reality 16

Self representation and interaction in immersive virtual rea...

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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021

作者： Viola, Eros Solari, Fabio Chessa, Manuela Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy

ISBN: (纸本)9789897584886

Inserting a self-representation in Virtual Reality is an open problem with several implications for both the sense of presence and interaction in virtual environments. To cope the problem with low cost devices, we devise a framework to align the measurements of different acquisition devices used while wearing a tracked VR head-mounted display (HMD). Specifically, we use the skeletal tracking features of an RGB-D sensor (Intel Realsense d435) to build the user’s avatar, and compare different interaction technologies: a Leap Motion, the Manus Prime haptic gloves, and the Oculus Controllers. The effectiveness of the proposed systems is assessed through an experimental session, where an assembly task is proposed with the three different interaction mediums, with and without the self-representation. Users reported their feeling by answering the User Experience and Igroup Presence Questionnaires, and we analyze the total time to completion and the error rate. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Surveys

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RICO-MR: An Open-Source Architecture for Robot Intent Communication through Mixed Reality

RICO-MR: An Open-Source Architecture for Robot Intent Commun...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Simone Macciò Mohamad Shaaban Alessandro Carfì Renato Zaccaria Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa Italy

This article presents an open-source architecture for conveying robots’ intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios.

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Robot-Induced Group Conversation Dynamics: A Model to Balance Participation and Unify Communities

Robot-Induced Group Conversation Dynamics: A Model to Balanc...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lucrezia Grassi Carmine Tommaso Recchiuto Antonio Sgorbissa Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy RAISE Ecosystem Genova Italy

The purpose of this research is to study the impact of robot participation in group conversations and assess the effectiveness of different addressing policies. The study involved a total of 300 participants, who were divided into groups of four and engaged in a dialogue with a humanoid robot. The robot acted as a moderator, using information obtained during the conversation to determine which speaker to address. The study found that the policy used by the robot significantly impacted the conversation dynamics. Specifically, the robot provided more balanced attention to each participant and reduced the number of subgroups.

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Personality- and Memory-based framework for Emotionally Intelligent agents

Personality- and Memory-based framework for Emotionally Inte...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Alice Nardelli Giacomo Maccagni Federico Minutoli Antonio Sgorbissa Carmine Tommaso Recchiuto Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Reply S.p.A. - Corso Francia Turin Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The goal-directed behavior observed in humans arises from the intricate interplay of various processes, including personality dynamics, emotional responses to others, memory encoding, the anticipation of future actions, and associated hedonic experiences. Integrating these multiple processes characteristic of human intelligence into a robotic framework aims to enhance the human-likeness of artificial agents and facilitate more natural and intuitive interactions with *** this purpose, in this paper, we propose a comprehensive psychological and cognitive architecture where, personality, as it happens for humans, not only influences the execution of actions but also shapes internal reactions to human emotions and guides anticipatory decision-making processes tailored to the agent’s traits. We demonstrate the framework’s effectiveness in generating perceivable synthetic personalities through an experiment involving participants in a dyadic conversation scenario with a digital human, where the digital human’s behavior is driven by its assigned personality. The results show that participants accurately perceive the artificial personality displayed by the digital human. We also demonstrate the potential of our robotic framework to bridge the gap between cognitive and psychological agents, as the findings highlight its ability to create a cognitively and emotionally intelligent digital human.

关键词： Solid modeling Shape Human intelligence Taxonomy Humanoid robots Oral communication Encoding Digital humans Intelligent agents Testing

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Trustworthy AI in Video Surveillance: The IMMAGINA Project

Trustworthy AI in Video Surveillance: The IMMAGINA Project

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Ledda, Emanuele Putzu, Lorenzo Delussu, Rita Fumera, Giorgio Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Dodecaneso 35 Genova16146 Italy Department of Electric and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09100 Italy

The increasing adoption of machine learning and deep learning models in critical applications raises the issue of ensuring their trustworthiness, which can be addressed by quantifying the uncertainty of their predictions. However, the black-box nature of many such models allows only to quantify uncertainty through ad hoc superstructures, which require to develop and train a model in an uncertainty-aware fashion. However, for applications where previously trained models are already in operation, it would be interesting to develop uncertainty quantification approaches acting as lightweight "plug-ins" that can be applied on top of such models without modifying and re-training them. In this contribution we present a research activity of the Pattern Recognition and Applications Lab of the University of Cagliari related to a recently proposed post hoc uncertainty quantification method, we named dropout injection, which is a variant of the well-known Monte Carlo dropout, and does not require any re-training nor any further gradient descent-based optimization;this makes it a promising, lightweight solution for integrating uncertainty quantification on any already-trained neural network. We are investigating a theoretically grounded solution to make dropout injection as effective as Monte Carlo dropout through a suitable rescaling of its uncertainty measure;we are also evaluating its effectiveness in the computer vision tasks of crowd counting and density estimation for intelligent video surveillance, thanks to our participation in a project funded by the European Space Agency. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Pattern recognition

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An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility

arXiv

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arXiv 2025年

作者： Thomas, Antony Mastrogiovanni, Fulvio Baglietto, Marco Robotics Research Center IIIT Telangana Hyderabad500032 India Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All’Opera Pia 13 Genoa16145 Italy

We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot’s configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into different connected regions. For a path to exist between the start and goal configurations, they must lie in the same connected region of the free space. Thus, to ascertain plan infeasibility, we merely need to sample adequate points from the obstacle region that isolate start and goal. Accordingly, we progressively construct the configuration space (initially assumed to be entirely free) by sampling from the discretized space and updating the bitmap cells representing obstacle regions. Subsequently, we partition this partially built configuration space to identify different connected components within it and assess the connectivity of the start and goal cells. We illustrate this methodology on five different scenarios with configuration spaces having up to 5 degrees-of-freedom (DOF). Additionally, we discuss further optimizations designed to significantly accelerate the proposed algorithm. The scalability of our approach to higher-dimensional configuration spaces is also examined, with experimental demonstrations involving 6-DOF and 7-DOF robots. © 2025, CC BY.

关键词： Motion planning

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Multi-objective optimization of electric automated bus trajectories based on the ε-constraint method

Multi-objective optimization of electric automated bus traje...

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Mediterranean Conference on Control and Automation (MED)

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with electric automated buses that have to follow a given route in inter-urban roads including stops, with a given timetable. Some stops are provided with a charging infrastructure allowing to charge the batteries while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the road and to minimize two objectives, respectively related to the minimization of the deviations from the timetable and the minimization of the energy lack, at the end of the bus route, with respect to a desired final energy level. To address this problem and to investigate the conflicting nature of these objectives, two multi-objective methods based on the $\varepsilon$-constraint approach are applied in this paper, allowing to find different sets of efficient solutions for the problem. The results obtained in a real case study show that the two objective are in conflict, and compromise solutions can be found using the methods proposed in this paper.

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Proprioceptive-Based Control Strategy to Assist Walking and Carrying Tasks in Back-Support Exoskeletons

Proprioceptive-Based Control Strategy to Assist Walking and ...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Vasco Fanti Sergio Leggieri Darwin G. Caldwell Christian Di Natali Department of Advanced Robotics Istituto Italiano di Tecnologia Genova Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) Università degli Studi di Genova (UniGe) Genova Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Back-support exoskeletons are becoming very popular in workplaces to provide assistance to workers during heavy-duty activities and to mitigate risks of work-related injuries. Lifting, lowering, and carrying loads are the most common operations in Manual Material Handling (MMH). Although many back-support exoskeletons have been developed to assist workers in MMH tasks, the primary concern addresses mainly lifting and lowering activities. This work aims to fill the gap in MMH assistance by proposing a control strategy for supporting workers also during walking and carrying. This control strategy for back-support exoskeletons generates assistive torques that comply with the user's natural gait and support the swing and stance phases. The presented Finite State Machine (FSM) algorithm detects successive early swings by relying on the actuators' angular velocities to provide the required assistance. Experiments on eight subjects evaluate the control strategy effectiveness in performing walking and carrying activities. The results on the metabolic consumption show a metabolic cost reduction of 50% when comparing the performance with and without the exoskeleton. In addition, the tests prove that the energy expenditure is comparable during walking tasks and it is lower during carrying tasks despite the additional exoskeleton weight.

关键词： Legged locomotion Actuators Costs Exoskeletons Employment Materials handling Automata Biomechatronics Angular velocity Injuries

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