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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Gonçalves, Vinícius Tzes, Anthony Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States Electrical Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersection between two convex shapes and to also compute the Jacobian of the scaling factor. In this paper, we propose a framework that uses this scaling factor, with an offset, to systematically define a CBF for obstacle avoidance tasks. We provide theoretical analyses of the continuity and continuous differentiability of the proposed CBF. We empirically evaluate the proposed CBF's behavior and show that the resulting optimal control problem is computationally efficient, which makes it applicable for realtime robotic control. We validate our approach, first using a 2D mobile robot example, then on the Franka-Emika Research 3 (FR3) robot manipulator both in simulation and experiment. Index Terms-Robot safety, collision avoidance. © 2023, CC BY.

关键词： Robot applications

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks

arXiv

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arXiv 2023年

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal The Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Systems Universidade Federal da Paraiba Brazil The GRVC Robotics Laboratory University of Seville Spain

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

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arXiv

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arXiv 2024年

作者： Lin, Zhenchao He, Li Yang, Hongqiang Sun, Xiaoqun Zhang, Guojin Chen, Weinan Guan, Yisheng Zhang, Hong School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China Meituan Technology Co. Ltd Shenzhen China

Large-scale point cloud consists of a multitude of individual objects, thereby encompassing rich structural and underlying semantic contextual information, resulting in a challenging problem in efficiently segmenting a point cloud. Most existing researches mainly focus on capturing intricate local features without giving due consideration to global ones, thus failing to leverage semantic context. In this paper, we propose a Similarity-Weighted Convolution and local-global Fusion Network, named SWCF-Net, which takes into account both local and global features. We propose a Similarity-Weighted Convolution (SWConv) to effectively extract local features, where similarity weights are incorporated into the convolution operation to enhance the generalization capabilities. Then, we employ a downsampling operation on the K and V channels within the attention module, thereby reducing the quadratic complexity to linear, enabling Transformer to deal with large-scale point cloud. At last, orthogonal components are extracted in the global features and then aggregated with local features, thereby eliminating redundant information between local and global features and consequently promoting efficiency. We evaluate SWCF-Net on large-scale outdoor datasets SemanticKITTI and Toronto3D. Our experimental results demonstrate the effectiveness of the proposed network. Our method achieves a competitive result with less computational cost, and is able to handle large-scale point clouds efficiently. The code is available at https://***/Sylva-Lin/SWCF-Net Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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Style Transfer Enabled Sim2Real Framework for Efficient Learning of Robotic Ultrasound Image Analysis Using Simulated Data

arXiv

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arXiv 2023年

作者： Li, Keyu Mao, Xinyu Ye, Chengwei Li, Ang Xu, Yangxin Meng, Max Q.-H. The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

Robotic ultrasound (US) systems have shown great potential to make US examinations easier and more accurate. Recently, various machine learning techniques have been proposed to realize automatic US image interpretation for robotic US acquisition tasks. However, obtaining large amounts of real US imaging data for training is usually expensive or even unfeasible in some clinical applications. An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a Sim2Real framework to efficiently learn robotic US image analysis tasks based only on simulated data for real-world deployment. A style transfer module is proposed based on unsupervised contrastive learning and used as a preprocessing step to convert the real US images into the simulation style. Thereafter, a task-relevant model is designed to combine CNNs with vision transformers to generate the task-dependent prediction with improved generalization ability. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic transesophageal echocardiography (TEE). Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed. Copyright © 2023, The Authors. All rights reserved.

关键词： Echocardiography

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A Self-Organized Constrained Flocking Protocol Design Subject to Sensor Detection Failure

A Self-Organized Constrained Flocking Protocol Design Subjec...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Wu, Xiongjun Wang, Man Meng, Xiangyu The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai China Shanghai Jiao Tong University Department of Automation Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton Rouge United States

ISBN: (纸本)9798350316308

This paper proposes a constrained flocking algorithm to achieve the velocity consensus of multiple agents subject to exogenous disturbances. The Reynolds boids model is adapted to capture the dynamic behavior of agents in flocks. Both collision avoidance and disturbance attenuation are considered in the flocking problem. The collision avoidance is designed to prevent a crash with static obstacles and among agents. The disturbance attenuation is solved by optimizing the energy-to-energy system gain. Stability and system gain conditions are provided in the form of linear matrix inequalities by exploiting the dissipative property of an energy storage function. Illustrative examples of no-fly zone simulations are presented to verify the effectiveness of the suggested approach, as well as the control protocol's feasibility. © 2023 IEEE.

关键词： Multi agent systems

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on robotics and automation (ICRA)

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

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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Enhancing Control of a 21-Level Modular Multilevel Converter through Grid-Based Model Predictive Current Control

Enhancing Control of a 21-Level Modular Multilevel Converter...

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2023 IEEE Asia Meeting on Environment and electrical engineering, EEE-AM 2023

作者： Tran Hung, Cuong Nguyen Nhat, Tung Pham Viet, Phuong Nguyen Xuan, Phuc Nguyen Quang, Thuan Nhat Hai, Nguyen Thuyloi University Faculty of Electrical & Electronics Engineering Ha Noi Viet Nam Ha Noi University of Science and Technology Department of Industrial Automation Ha Noi Viet Nam Key Laboratory for High Voltage Techniques Institute of Energy Ha Noi Viet Nam Hanoi University of Science and Technology Computer Science Department School of Information and Communications Technology Hanoi Viet Nam

ISBN: (纸本)9798350381061

The Modular Multilevel Converter (MMC) has a flexible structure, extending the number of voltage levels on the AC side. This structure has many advantages such as low switching frequency, and a smaller number of transistor valves. Therefore, the MMC converter makes it easy to apply control algorithms and is widely applied to high-voltage power grids. This paper presents the control method of an MMC converter on the AC side, with 10 Sub-modules (SM) in each phase to generate 21 voltage levels. The process is performed by the current control method based on Finite Control Set - Model Predictive Control (FCS-MPC). With the advantage of fast response, optimizing the converter's working process by predicting the working states of the converter, according to the model, the FCS-MPC control algorithm has been used in this model. The results of the proposed method are proven by using Matlab/Simulink software. The results have shown the requirements of the DC/AC converter such as sinusoidal current, and voltage with 21 voltages. © 2023 IEEE.

关键词： Model predictive control

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Transcranial Direct Current Stimulation Associated with Visual and Auditory Cueing during Gait Training: A Case Study with a Parkinson's Disease Patient

Transcranial Direct Current Stimulation Associated with Visu...

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

作者： Angie Pino Patricio Barria Karim Baleta Rolando Aguilar José M. Azorín Marcela Múnera Carlos A. Cifuentes Club de Leones Cruz del Sur Rehabilitation Center Punta Arenas Chile Department of Electrical Engineering University of Magallanes Punta Arenas Chile Department of Systems Engineering and Automation Miguel Hernández University of Elche Elche Spain Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Parkinson's Disease (PD) is a neurodegenerative disorder that leads to motor dysfunctions and restricts walking independence. Approaches such as transcranial direct current stimulation (tDCS) and cueing gait are potential rehabilitation strategies that have proven efficient in improving motor function in individuals with PD in preliminary and separate studies. This case study with a single PD patient explores the feasibility and potential effects of combining anodal tDCS (a-tDCS) with visual and auditory cues during 15 gait training sessions. The patient was assessed before the first intervention and after completing all training weeks regarding kinematic parameters of gait, neuronal activation, muscle strength, and functional skills. Results showed a beneficial impact in significantly increasing the participant's speed, stride length, step length, and muscle strength. Furthermore, the decrease in beta brain activity and increase in the delta were associated with a reduction in the effects of the disease, improved motor performance, and shorter functional task times. This study suggests the a-tDCS strategy with double cueing gait can lead to therapeutic potential in relieving the disease's motor symptoms.

关键词： Training Visualization Motor drives Parkinson's disease Kinematics Muscles Motors Neurorehabilitation Transcranial direct current stimulation Robots

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