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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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On the Robustness of Adversarial Training Against Uncertainty Attacks

arXiv

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arXiv 2024年

作者： Ledda, Emanuele Scodeller, Giovanni Angioni, Daniele Piras, Giorgio Cinà, Antonio Emanuele Fumera, Giorgio Biggio, Battista 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

In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i.e., trustworthy) uncertainty measures, which downstream modules can securely employ to drive the final decision-making process. However, an attacker may be interested in forcing the system to produce either (i) highly uncertain outputs jeopardizing the system’s availability or (ii) low uncertainty estimates, making the system accept uncertain samples that would instead require a careful inspection (e.g., human intervention). Therefore, it becomes fundamental to understand how to obtain robust uncertainty estimates against these kinds of attacks. In this work, we reveal both empirically and theoretically that defending against adversarial examples, i.e., carefully perturbed samples that cause misclassification, additionally guarantees a more secure, trustworthy uncertainty estimate under common attack scenarios without the need for an ad-hoc defense strategy. To support our claims, we evaluate multiple adversarial-robust models from the publicly available benchmark RobustBench on the CIFAR-10 and ImageNet datasets. © 2024, CC BY.

关键词： Adversarial machine learning

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Robust Consensus of Perturbed Euler-Lagrange Agents with Unknown Disturbances

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IFAC-PapersOnLine 2024年第6期58卷 190-195页

作者： Alexey Bobtsov Romeo Ortega Jose Guadalupe Romero Emmanuel Nuño Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 197101 Saint Petersburg Russia Departament of Electrical and Electronic Engineering ITAM Progreso Tizapán 1 04100 Ciudad de México México Department of Computer Science CUCEI University of Guadalajara 44430 Guadalajara Mexico

In this paper we consider the problems of leaderless consensus for networks of fully actuated Euler-Lagrange agents perturbed by unknown additive disturbances. The network is an undirected weighted graph with time delays. The proposed controller has a PD structure that incorporates, in a certainty-equivalent way, the estimate of the unknown disturbance. The design of the disturbance estimator proceeds along the following steps. First, the derivation of a regression equation, that turns out to be nonlinearly parameterized, but with an injective mapping. Second, we propose to use a recently introduced least-squares plus dynamic regressor extension algorithm that allows us to estimate the unknown frequencies imposing extremely weak excitation assumptions. In this way, we derive a sufficient condition on the proportional and derivative gains of the controller to ensure that the systems globally and asymptotically converge to a consensus position.

关键词： Robust adaptive control Multi-agent systems Disturbance rejection Robust time delay systems

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Techno-Economic Assessment of a Large-Scale Electrolysis Plant in Hydrogen and Ancillary Service Markets

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IFAC-PapersOnLine 2024年第13期58卷 140-145页

作者： Yassine Ennassiri Giulio Ferro Michela Robba Gilney Damm Edemar O. Prado DIBRIS—Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova 16145 Italy IMSE Laboratory Gustave Eiffel University Marne-la-Vallée France Power Electronics and Control Research Group GEPOC Federal University of Santa Maria SantaMaria 97105-900 RS Brazil

Hydrogen electrolysis systems have seen considerable interest from energy communities as a potential solution for shifting from fossil-based energy resources to clean energy alternatives. The system consists of transforming electricity into hydrogen via water electrolysis. As it is expected to have a large-scale connection with the grid to compensate the fluctuation of renewables, it is important to understand the system’s technical and economic feasibility. Therefore, this work aims to provide a high-level techno-economic assessment for the participation of a large-scale electrolysis plant in hydrogen and grid ancillary service markets. The plant generates revenue from two sources: hydrogen and frequency regulation. Based on market prices of electricity and hydrogen, a linear programming (LP) optimization has shown that a 50MW electrolysis plant can generate a 33% increase in profit if the plant includes the grid support market in the business model. Additionally, simulations under unstable grid frequency have shown high reactivity of the system to maintain grid stability.

关键词： optimization hydrogen production frequency regulation linear programming

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A Gradient Neural Network for online Solving the Time-varying Inverse Kinematics Problem of Four-wheel Mobile Robotic Arm 10

A Gradient Neural Network for online Solving the Time-varyin...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Zhou, Yanpeng Liu, Keping Li, Chunxu Wang, Gang Liu, Yongbai Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130012 China University of Plymouth Centre for Robotics and Neural Systems PlymouthPL48AA United Kingdom

ISBN: (纸本)9781665424233

In this paper, a gradient neural network (GNN) is presented, analyzed and discussed to solve the time-varying inverse kinematics solution of the four-wheel mobile robotic arm, which can approximate the time varying inverse kinematics solution. A monolithic kinematics model of mobile robotic arm is established, and the inverse kinematics solution can synchronously coordinate the control of the mobile platform and the robotic arm to accomplish the task of the end-executor. Besides, the computer numerical results are provided to attest validity and high exactitude of GNN model in settling the time-varying inverse kinematics of a four-wheel mobile robotic arm. © 2021 IEEE.

关键词： Robotic arms

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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Large receptive field strategy and important feature extraction strategy in 3D object detection

arXiv

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arXiv 2024年

作者： Cui, Leichao Li, Xiuxian Meng, Min Jia, Guangyu Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201800 China The Centre for Robotics Research Department of Engineering King’s College London Strand LondonWC2R 2LS United Kingdom

The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a crucial information for this purpose. Our study focuses on key challenges in 3D target detection. To tackle the challenge of expanding the receptive field of a 3D convolutional kernel, we introduce the Dynamic Feature Fusion Module (DFFM). This module achieves adaptive expansion of the 3D convolutional kernel’s receptive field, balancing the expansion with acceptable computational loads. This innovation reduces operations, expands the receptive field, and allows the model to dynamically adjust to different object requirements. Simultaneously, we identify redundant information in 3D features. Employing the Feature Selection Module (FSM) quantitatively evaluates and eliminates non-important features, achieving the separation of output box fitting and feature extraction. This innovation enables the detector to focus on critical features, resulting in model compression, reduced computational burden, and minimized candidate frame interference. Extensive experiments confirm that both DFFM and FSM not only enhance current benchmarks, particularly in small target detection, but also accelerate network performance. Importantly, these modules exhibit effective complementarity. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Model-Free Predictive control for Harmonic Suppression of PMSMs Based on Adaptive Resonant controller

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IEEE Transactions on Energy Conversion 2025年

作者： Li, Teng Sun, Xiaodong Su, Zonghao Zha, Xuhao Dianov, Anton Prakht, Vladimir Demidova, Galina Ma, Jinlin Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China

To simultaneously suppress the periodic and aperiodic disturbances experienced by permanent magnet synchronous motors in model predictive control, a model-free predictive current control with adaptive resonance control based on an ultra-local model is proposed. Firstly, the ultra-local models of the speed loop and the current loop are established respectively, and the unmodeled and uncertain unknown part F in the ultra-local model is estimated by using a high-order fast terminal sliding mode observer, and its stability is analyzed. Then, an adaptive resonant controller (ARC) is designed to suppress the periodic disturbance caused by the dead zone effect. The ARC and the model-free control based on the ultra-local model are combined to compensate for their respective shortcomings, achieving the suppression of periodic and aperiodic disturbances in the system. Finally, by comparing with the traditional duty cycle model predictive control and the model-free model predictive control without ARC, the proposed scheme is proved to be robust to periodic and aperiodic disturbances. © 2025 IEEE. All rights reserved.

关键词： Permanent magnets

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Test-Stand Development and Performance Analysis of RoboWalk Assistive Device

Test-Stand Development and Performance Analysis of RoboWalk ...

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

作者： S. Ali A. Moosavian Omid Mahdizadeh Vahid Akbari Mahdi Nabipour Sina Ghanaat Shahin Naeemi Sina Hallajian Arash T. Beydokhti Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781665454537

Robotic devices are increasingly studied for the rehabilitation purposes of the lower limb disabilities. Wearable assistive devices such as exoskeletons have been recognized as a practical solution to increase the power and maneuverability of human beings in industries, as well as rehabilitation, elderly care, and sports exercises. RoboWalk is a lower limb assistive device that has been designed in ARAS lab at KNTU. To guarantee its proper performance, a Test-Stand is designed and manufactured with extended toolkit options. To validate the simulation results of various scenarios, is the main goal of developing this Test-Stand. Also, to examine the electronics boards and subsystems, the correct interaction between the sensors and the actuators, and investigate the performance of the main control loop, and so on, the Test-Stand is highly useful. In this paper, these various aspects of design and manufacturing of RoboWalk test stand are presented. Stress analyses in SOLIDWORKS software showed that the design of the proposed device is capable to handle the required tasks. Finally, a positioning task is implemented, which reveals the merits of the designed Test_Stand.

关键词： Performance evaluation Simulation Exoskeletons Software Sensors Manufacturing Assistive devices

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