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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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Real-Time Face-Tracking control of a 4 DOF Manipulator Using Deep Learning Detection

Real-Time Face-Tracking Control of a 4 DOF Manipulator Using...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ali Asadi Amirreza Hassan Nejad S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This paper has presented the design, simulation, and real-time implementation of a four-degree-of-freedom (4-DOF) robotic system for face detection and tracking. The system has combined kinematic control with deep learning. It has used the Xception network for high-accuracy facial detection. A Fuzzy PID-MIJ control algorithm has optimized the trajectory planning. This has ensured smooth and precise target tracking. Both simulations and physical experiments have validated the system's real-time performance. The results have shown that the system can reliably track faces with minimal error. This approach has demonstrated significant potential for dynamic, human-interactive applications.

关键词： Deep learning Target tracking Trajectory planning Target recognition Face recognition Kinematics Real-time systems Robustness Planning Face detection

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A Negative Imaginary Solution to an Aircraft Platooning Problem

A Negative Imaginary Solution to an Aircraft Platooning Prob...

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European control Conference (ECC)

作者： Yu-Hsiang Su Parijat Bhowmick Alexander Lanzon Department of EEE Control Systems and Robotics Group School of Engineering University of Manchester Manchester UK Department of EEE IIT Guwahati Assam India

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the next decade, the growth of commercial aircraft is expected to increase by 30%, causing significant challenges in air traffic management and control. To address this problem, we propose the idea of aircraft platooning during the descending and pre-landing phases. The objective is to design a distributed flight guidance and control system that assists onboard pilots in finding a feasible and collision-free trajectory from descent to pre-landing. The proposed aircraft platoon control scheme comprises a feedback linearising controller in the inner loop that transforms the nonlinear aircraft dynamics into a MIMO double-integrator, inherently a Negative Imaginary system. The outer loop employs a distributed output feedback Strictly Negative Imaginary controller, enabling networked aeroplanes to maintain the desired inter-aircraft spacing along each coordinate by synchronising their velocities. In addition, a contingency strategy is proposed to handle potential runway failures (e.g. sudden blockage, damage, etc.) by switching a descending aircraft platoon into a time-varying hover formation for each aircraft, maintaining a safe vertical gap. Finally, a comprehensive MATLAB simulation case study is conducted to test the feasibility and performance of the NI theory-based aircraft platoon control scheme.

关键词： Contingency management Transforms Switches Aerodynamics Nonlinear dynamical systems Trajectory Air traffic control

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Experimental Testing of a Prototype of an Active Elbow Orthosis Based on in vivo Investigation of Elbow Flexion/Extension of Healthy Subjects

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International Journal Bioautomation 2022年第2期26卷 161-174页

作者： Angelova, Silvija Petrov, Emil Raykov, Plamen Raikova, Rositsa Department of Motor Control Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Acad G. Bonchev Str. Bl. 105 Sofia Bulgaria Department of Mechatronic and Bio/Technological Systems Institute of Robotics Bulgarian Academy of Sciences Acad. G. Bonchev Str.Bl. 2 Sofia Bulgaria Department of Robotics and Mechatronic Intelligent Systems Institute of Robotics Bulgarian Academy of Sciences Acad G. Bonchev Str.Bl. 2 Sofia Bulgaria

Many people have problems with elbow joint driving because of different diseases, sport trauma, road traffic injuries, etc. A good way for restoring elbow motions is a selfrehabilitation using an active orthosis. It has to be light, convenient for daily use, active, easy to control by patients, and safe. The paper presents a prototype of an active elbow orthosis. The plastic details were designed using a CAD system and were 3D printed. The joint was driven by a Dynamixel actuator. To increase the joint moment, a reduction gear-belt drive was constructed and applied. Experiments with six healthy subjects were performed using a Noraxon measuring system, aiming to investigate elbow joint angles in natural movements with four different velocities of elbow flexion and extension without and with a load of 0.5 kg in the hand. The four velocities (from very slow to very fast) were controlled by a specialized PC application. In order to achieve similar motions of the orthosis, the angle profiles were approximated so that the motion was between 10° and 120° because of electro/mechanical and software safety stoppers. Experiments were performed with the orthosis following the given angle profile without load and with a load of 0.5 kg. The results show that the orthosis’ forearm performs the given angle and angular speed profiles with enough precision © 2022. International Journal *** Rights Reserved

关键词： Computer aided design

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification*

Long-Range Vehicle Detection of LiDAR using Adaboost Classif...

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IEEE/SICE International Symposium on System Integration

作者： Na-Young Lim Jeon-Hyeok Lee Tae-Hyoung Park Department of Intelligent Systems & Robotics Chungbuk National University Cheongju South Korea Department of Control & Robot Engineering Chungbuk National University Cheongju South Korea

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering-based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and foreground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generating 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments.

关键词： Laser radar Three-dimensional displays Accuracy Vehicle detection System integration Object detection Computational efficiency Faces Standards Autonomous vehicles

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Resilient Event-Triggered Model Predictive control for Adaptive Cruise control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Navigating the Future: Advancing Autonomous Vehicles through Robust Target Recognition and Real-Time Avoidance

Navigating the Future: Advancing Autonomous Vehicles through...

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control Theory and Applications (ICoCTA), International Conference on

作者： Mohammed A. M. Hussein Maki K. Habib Robotics Control and Smart Systems The American University in Cairo Cairo Egypt Department of Mechanical Engineering The American University in Cairo Cairo Egypt

ISBN: (数字)9798331506155

ISBN: (纸本)9798331506162

This research addresses the critical challenge of enhancing object recognition and real-time response capabilities in autonomous vehicles (AVs) under varying simulated conditions, which is crucial for ensuring both navigational safety and operational efficiency. Utilizing the Carla 0.9.14 simulator and Unreal Engine 4.26 on Ubuntu 20.04, we focus on improving the detection and classification of key on-road obstacles—vehicles, pedestrians, and cyclists—using the YOLOv7 object detection algorithm. By integrating advanced sensory technologies, specifically stereo vision cameras and LIDAR, we create a dynamic testing environment that simulates diverse urban and rural scenarios. Our methodology enhances the YOLOv7 algorithm's accuracy and speed through extensive training on a meticulously curated dataset of 4,113 images, reflecting a broad spectrum of environmental conditions, including varying lighting and weather conditions. This rigorous approach yielded a significant increase in mean average precision (mAP) from 64.3% to 76.3%, and enhanced the algorithm's reliability, with notable improvements over previous models. The research delineates a clear advancement in AV technology by demonstrating substantial improvements in object detection metrics, contributing foundational insights for future implementations in real-world settings and supporting the further development of real-time avoidance systems. This study not only progresses the field of AV but also sets a new benchmark for object detection performance, aligning with industry and academic goals to optimize AV systems for complex and unpredictable driving scenarios.

关键词： Laser radar Navigation Heuristic algorithms Object detection Real-time systems Data models Classification algorithms Vehicle dynamics Autonomous vehicles Engines

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Algorithm for Robustness Estimation of Large Deviations in Linear Discrete-Time systems 17th

Algorithm for Robustness Estimation of Large Deviations in...

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17th International Conference on Informatics in control, Automation and robotics, ICINCO 2020

作者： Vunder, Nina Dudarenko, Natalia Institute of Control Engineering TU Braunschweig Hans-Sommer-Str. 66 Braunschweig Germany Department of Control Systems and Industrial Robotics ITMO University 49 Kronverksky Pr. Saint-Petersburg Russia

ISBN: (纸本)9783030924416

The paper presents the algorithm for robustness estimation of large deviations in free motion of linear discrete-time systems. A tracking discrete-time systems with the modal control law are discussed, where the modal control law is designed taking into account the value of delay and the deviation. Two cases of discrete-time systems with control signal delay are considered in the paper. The first case relates to predictable delay of control signal, and the second one relates to unpredictable delay of control signal. It is assumed that parameters of the system are linearly dependent on the uncertainties. The problem is solved with the state space approach and the sensitivity theory methods. An upper bound estimation of trajectory deviations for discrete-time systems is obtained. The estimation contains the condition number of the eigenvectors matrix of the system state matrix. Therefore, sensitivity functions of singular values of the eigenvectors matrix are used to calculate the robustness estimation of the deviations. Based on the obtained equations, an algorithm for the robustness estimation of large deviations in linear discrete-time systems with parametric uncertainties is proposed. The proposed method is compared with an alternative LMI based approach for the peak estimation problem. Advantages and disadvantages of both approaches are illustrated with examples. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitivity analysis

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Multiplier analysis of Lurye systems with power signals

arXiv

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arXiv 2024年

作者： Heath, William P. Carrasco, Joaquin Control Systems and Robotics Group Department of Electrical and Electronic Engineering Engineering Building A University of Manchester M13 9PL United Kingdom

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closed-loop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean. © 2024, CC BY.

关键词： Slope stability

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