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arXiv 2023年

作者： Thomas, Antony Mastrogiovanni, Fulvio Baglietto, Marco Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All'Opera Pia 13 Genoa16145 Italy

The minimum constraint removal problem seeks to find the minimum number of constraints, i.e., obstacles, that need to be removed to connect a start to a goal location with a collision-free path. This problem is NP-hard and has been studied in robotics, wireless sensing, and computational geometry. This work contributes to the existing literature by presenting and discussing two results. The first result shows that the minimum constraint removal is NP-hard for simply connected obstacles where each obstacle intersects a constant number of other obstacles. The second result demonstrates that for n simply connected obstacles in the plane, instances of the minimum constraint removal problem with minimum removable obstacles lower than ( n + 1)/3 can be solved in polynomial time. This result is also empirically validated using several instances of randomly sampled axis-parallel rectangles. © 2023, CC BY.

关键词： Computational geometry

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Safe Motion Planning with Environment Uncertainty

arXiv

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arXiv 2023年

作者： Thomas, Antony Mastrogiovanni, Fulvio Baglietto, Marco Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All'Opera Pia 13 Genoa16145 Italy

We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties during robot localization. Existing planning approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can wrongly localize the robot, leading to inefficient plans. Our approach thus incorporates the landmark uncertainty within the Bayes filter estimation framework. We also analyse the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Second, we extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian distributed robot motion, perception and obstacle location uncertainties. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. In contrast, existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We demonstrate and evaluate our approach using a theoretical example and simulations. We also present a comparison of our approach to different state-of-the-art methods. © 2023, CC BY.

关键词： Motion planning

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Development of a ML-Control Strategy for a Wrist Exoskeleton Based on EMG and Force Measurements with Sensor Strategy Optimisation

Development of a ML-Control Strategy for a Wrist Exoskeleton...

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

作者： Roberto F. Pitzalis Nicholas Cartocci Christian Di Natali Darwin G. Caldwell Giovanni Berselli Jesús Ortiz Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Mechanical Energy and Transportation Engineering (DIME) University of Genoa Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

The aim of this paper is to infer the development of a control and sensor strategy for an industrial wearable wrist exoskeleton through preliminary classification and prediction of workers' actions gathered in a real working environment. The effort intensity, correlated with the exerted force, and sensor strategy optimization are considered and evaluated for design purposes. In this paper, an EMG-based wrist motion classification and a force prediction models are presented based on measurements taken on six healthy subjects in a manufacturing plant. A wrist motion pattern recognition algorithm has been developed on top of surface EMG data measured through an 8-channel commercially available sensor (Myo Armband). Moreover, a force regression model has been built based upon wrist force measurements detected by using a commercial hand-held dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy lays the basis for a more complex architecture which will be designed to control an active wrist exoskeleton for industrial applications, simplifying its design, costs and data process by adopting as few sensors and electronic components as possible while maintaining sufficient reliability and accuracy to provide adequate assistance.

关键词： Wrist Maximum likelihood estimation Accuracy Force Exoskeletons Prediction algorithms Feature extraction Reliability engineering Electromyography Classification algorithms

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Power Demand Forecasting for a Hybrid Marine Energy System with Shallow and Deep Learning

Power Demand Forecasting for a Hybrid Marine Energy System w...

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OCEANS

作者： Jake M. Walker Andrea Coraddu Luca Oneto Department of Maritime and Transport Technology Delft University of Technology Delft The Netherlands Department of Informatics Bioengineering Robotics and Systems Engineering Università Degli Studi di Genova Genova Italy

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

To ensure that future autonomous surface ships sail in the most sustainable way, it is crucial to optimize the per-formance of the Energy and Power Management (EPM) system. However, marine EPM systems are complex and often coordinate various distributed energy resources, energy storage systems, and power grids to ensure reliable and safe power delivery. Traditional control methods for marine EPM systems are limited by evaluating processes using simplified component models over a short time horizon, or relying on historical insights gained from earlier journeys, and are not the optimal approach for complex hybrid marine EPM systems. Advanced control strategies, such as Model Predictive Control (MPC), offer a promising control method that considers predicted future system responses over an extended time horizon to determine the best control input, making them an effective strategy for optimizing the performance of hybrid marine EPM systems. However, to learn the onboard energy profiles based on component behavior in a hybrid system from past experiences is not a trivial task, and one of the primary barriers to implementing MPC for marine EPM control. For this reason, in this work, we address the challenge of learning energy profiles for a marine EPM system by utilizing shallow and deep machine learning for total power demand forecasting. The forecast is an essential reference for an MPC-based controller and will enable this control strategy to provide reliable and safe power delivery for hybrid marine EPM systems. The proposed approach compares state-of-the-art machine learning models to identify the best-performing algorithm, considering accuracy and computational requirements. We illustrate the potential of the proposed approach by using real world operational data from a vessel with a hybrid marine EPM system. Results indicate that shallow models, trained on engineered features handcrafted with classical signal processing techniques, allow forecasting the total pow

关键词： Power demand Accuracy Computational modeling Predictive models Signal processing Data models Power system reliability

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines

A Multi-Scale Control Framework for Electric and Automated B...

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IEEE Conference on Decision and Control

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

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme.

关键词： Energy consumption Accuracy Optimal control Minimization Real-time systems Delays Batteries State of charge Brakes MATLAB

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Distributed operational management of microgrids: a second order dual update approach

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IFAC-PapersOnLine 2023年第2期56卷 1540-1545页

作者： Giulio Ferro Michela Robba Federico Delfino Rabab Haider Anuradha M. Annaswamy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02141 USA Department of of Electrical Electronic Telecommunications Engineering and Naval Architecture University of Genova

This paper develops a Distributed Energy Management System (EMS) to optimally allocate electric and thermal power production in a polygenerative microgrid. The EMS problem is formulated as a multiperiod convex optimization problem and solved using AL-SODU, a new distributed algorithm based on the augmented Lagrangian method with second order dual updates. The proposed AL-SODU algorithm significantly outperforms state of the art algorithms employing first order dual updates, with 15-times speedup in convergence speed. A case study of the EMS based on AL-SODU is conducted on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The EMS determines the optimal schedules for electric and thermal power plants every 15-minutes. This real time scheduling of the microgrid is enabled by the Al-SODU algorithm, which solves the scheduling problem in 1.86s.

关键词： Microgrids distributed control second order methods

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Exploiting LLMs for E-Learning: A Cybersecurity Perspective on AI-Generated Tools in Education

Exploiting LLMs for E-Learning: A Cybersecurity Perspective ...

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Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Danilo Greco Luca Chianese DIG - Department of Management Economics and Industrial Engineering Politecnico di Milano Milan Italy DIBRIS - Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

AI and LLM technologies have emerged rapidly, creating more opportunities to develop and enhance e-learning. This paper provides a detailed account of the tools built by the current and future generations of LLMs and AI technologies for creating complex e-learning applications. It introduces the application of these tools, including innovative learning, context-awareness and intelligent learning systems. Moreover, it delves systematically into the security issues of these AI-integrated e-learning environments. The work applies strict research approaches to determine and describe possible risks in system design, data processing, and AI model deployment. We also undertake deeper penetration tests to assess the level of preparedness of these platforms against different types of attacks from the attackers such as attacks targeting the AI components, injection of unverified data and privacy violations. Our study findings reveal the opportunity to apply AI in e-learning, thus highlighting the importance of addressing these concerns. It is a complete guide for the developers and the educational institutions to integrate the LLMs and AI and keep the security strong. This paper adds to the existing literature on the role of AI in education and cybersecurity, providing essential information to the future advancements of safe and innovative e-learning systems.

关键词： Data privacy Analytical models Electronic learning Conferences Data processing Data models Application security Artificial intelligence System analysis and design

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Multi-Objective Microservice Orchestration: Balancing Security and Performance in CCAM

Multi-Objective Microservice Orchestration: Balancing Securi...

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Stefano Berlato Silvio Cretti Domenico Siracusa Silvio Ranise Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Center for Cybersecurity Fondazione Bruno Kessler Trento Italy Department of Mathematics University of Trento Trento Italy

We (devise and) demonstrate the benefits of a methodology and a toolset for orchestrating Cloud-native applications to balance the minimization of risks due to the presence of security threats and the achievement of service performance requirements - expressed on, e.g., computational resources, network throughput and latency. The demo proves the effectiveness of the methodology in orchestrating a set of microservices implementing a prominent Cooperative, Connected and Automated Mobility (CCAM) service.

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A Modular Framework for Flexible Planning in Human-Robot Collaboration

arXiv

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arXiv 2024年

作者： Belcamino, Valerio Kilina, Mariya Lastrico, Linda Carfì, Alessandro Mastrogiovanni, Fulvio TheEngineRoom Department of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Italian Institute of Technology Genoa Italy

This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system’s scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications. © 2024, CC BY.

关键词： Robot programming

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Energy Management System for a Smart Green Nanogrid feeding a Research Laboratory with Autonomous Mobile Robots

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IFAC-PapersOnLine 2024年第2期58卷 156-161页

作者： Matteo Fresia Lorenzo De Simone Ilaria Traverso Stefano Bracco Silvia Siri Department of Electrical Electronic Telecommunication Engineering and Naval Architecture University of Genoa 16145 Genova Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genova Italy

This paper proposes a mixed-integer linear programming optimization model used to define an energy management system tailored for nanogrids in buildings, integrating renewable energy sources, battery energy storage systems and task-executing autonomous mobile robots. Focused on a nanogrid to be realised at the Savona Campus of the University of Genoa, the energy management system optimizes power flows and robot task scheduling in order to minimize the operating costs, the curtailment of the photovoltaic source and the number of unperformed tasks. Its novelty lies in combining energy and task planning constraints, offering significant potential for sustainable building energy management.

关键词： Energy management system Optimal operation Nanogrids Prosumer building Autonomous mobile robots Renewable energy sources Battery energy storage system

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