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Information, control, and Communication Technologies (ICCT), International Conference on

作者： Dmitriy Kuzivanov Kseniya Stepanova Maxim Lobastov Alexey Fedorov Igor Kinzhagulov Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russia Department of Scientific and Technical Development STC “Etalon” Saint-Petersburg Russia Desigh Engineering Department JSC “S&PA Energomash” Khimki Russia

This research paper considers the possibility of using an acoustic emission control method in the process of direct laser melting to determine the moment of the development of cracking in the samples being melted under incorrectly selected melting modes. After the samples were made, their metallography was carried out, confirming the presence of defects in one of the samples. The data obtained during post-processing of acoustic emission signals were also compared with the results of metallographic analysis.

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Adaptive State Observers of Linear Time-varying Descriptor Systems: A Parameter Estimation-Based Approach

arXiv

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arXiv 2024年

作者： Ortega, Romeo Bobtsov, Alexey Castaños, Fernando Nikolaev, Nikolay Department of Electrical and Electronic Engineering ITAM Ciudad de México Mexico The Hangzhou Dianzi University ITMO Joint Institute Xiasha Higher Education Zone Zhejiang Hangzhou China Faculty of Control Systems and Robotics ITMO University Kronverkskiy av. 49 St. Petersburg197101 Russia The Departamento de Control Automático IPN Cinvestav Mexico The Faculty of Control Systems and Robotics ITMO University Kronverkskiy av. 49 St. Petersburg197101 Russia

In this paper, we apply the recently developed generalized parameter estimation-based observer design technique for state-affine systems to the practically important case of linear time-varying descriptor systems with uncertain parameters. We give simulation results of benchmark examples that illustrate the performance of the proposed adaptive observer. © 2024, CC BY-NC-ND.

关键词： Parameter estimation

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Quadrotor Autonomous Landing on Moving Platform

Quadrotor Autonomous Landing on Moving Platform

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Pengyu Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Control Science and Engineering Shandong University Jinan China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong

This paper introduces a quadrotor's autonomous take-off and landing system on a moving platform. The designed system addresses three challenging problems: fast pose estimation, restricted external localization, and effective obstacle avoidance. Specifically, first, we design a landing recognition and positioning system based on the AruCo marker to help the quadrotor quickly calculate the relative pose;second, we leverage a gradient-based local motion planner to generate collision-free reference trajectories rapidly for the quadrotor;third, we build an autonomous state machine that enables the quadrotor to complete its take-off, tracking and landing tasks in full autonomy;finally, we conduct experiments in simulated, real-world indoor and outdoor environments to verify the system's effectiveness and demonstrate its potential. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Motion planning

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Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Weiwei, Liu Wenxuan, Hu Wei, Jing Lanxin, Lei Lingping, Gao Yong, Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Advanced Perception on Robotics and Intelligent Learning Lab Huzhou Institute Zhejiang University Huzhou China College of Information Engineering Huzhou University Huzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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OBHMR: Robust Partial-to-full Generalized Point Set Registration with Overlap-guided Bidirectional Hybrid Mixture Model

OBHMR: Robust Partial-to-full Generalized Point Set Registra...

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

作者： Xinzhe Du Zhengyan Zhang Ang Zhang Rui Song Yibin Li Max Q.-H. Meng Zhe Min School of Control Science and Engineering Shandong University China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Yuanhua Robotics Perception and AI Technologies Ltd Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a novel overlap-based bidirectional point set registration approach, i.e., Overlap-guided Bidirectional Hybrid Mixture Registration (OBHMR), which incorporates geometric information (i.e., normal vectors) in both the correspondence and transformation stages and formulates the optimization objective of registration in a bidirectional manner. More importantly, to address the issue of partial-to-full registration, OBHMR utilises the predicted point-wise overlap score using networks to formulate the overlap-guided Hybrid Mixture Model consisting of the Gaussian Mixture Model (GMM) and Fisher Mixture Model (FMM). OBHMR contains four components: (1) the overlap-guided correspondence network that estimates the correspondence probabilities and calculates the point-wise overlap score; (2) the learning posterior module that estimates the overlap-guided HMM parameters; (3) the transformation module that computes the rigid transformation by formulating the optimisation objective in a bidirectional registration way, given correspondences and overlap-guided HMM parameters. Experiments using 1457 human femur and 1301 human hip models demonstrate significant improvements in partial-to-full registration performance (p < 0.01) under different overlapping ratios, compared to state-of-the-art registration approaches. Furthermore, individual contributions of three modules (i.e., additional normal vectors, overlap score estimation module and the bidirectional mechanism) in OBHMR have been validated in ablation studies. The results demonstrate OBHMR’s capability of tackling the challenging partial-to-full registration problems in computer-assisted orthopedic surgery. The codes are available at https://***/Dxinz/DeepOBHMR.

关键词： Accuracy Orthopedic surgery Noise Hidden Markov models Estimation Mixture models Probability Vectors Optimization Intelligent robots

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Mechatronics system for solving SuDoKu puzzle based on Cartesian robot

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IFAC-PapersOnLine 2024年第9期58卷 149-153页

作者： Radosław Tomaszewski Wojciech Walendziuk Roman Trochimczuk Adam Wolniakowski Kacper Jaroszewicz Zdenek Slanina Doctoral School of Bialystok University of Technology Bialystok Poland Department of Electrotechnics Power Electronics and Electrical Power Engineering Bialystok University of Technology Bialystok Poland Department of Automatic Control and Robotics Bialystok University of Technology Bialystok Poland VSB – Technical University of Ostrava 17. listopadu 2172/15 Ostrava - Poruba Czech Republic

The article describes an original solution of a mechatronic device with a Cartesian robot and a character recognition system for autonomous solving the SuDoKu puzzle. The device demonstrates the use of intelligent mechatronic devices for tasks that are difficult to algorithmize. The work contains a description of the mechanical executive system, including the motor apparatus of the system, a description of the electrical and information subsystem, and the software responsible for controlling the device in question. The OCR system operation principle, responsible for recognizing and locating characters on the SuDoKu board, is explained in the article. It also presents how to find a solution using the Backtracking algorithm. The results of testing the correct operation of the designed mechatronic system and estimating the execution time of individual test tasks are presented here.

关键词： Artificial Intelligence Smart Mechatronics SuDoKu Optical Character Recognition Cartesian Robot

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Renewable Power Generation Maximization for UAVs with Adaptive Sliding Mode control

Renewable Power Generation Maximization for UAVs with Adapti...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Khalid Alfuwail Ali Nasir Md Shafiullah Ayman Abdallah Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC Intelligent Manufacturing and Robotics (IRC-IMR) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Aerospace Engineering Department IRC for Aviation & Space Exploration (IRC-ASE) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The demand for efficient and Fuel consumption alternative renewable energy sources in the aviation sector has led to the exploration of advanced technologies such as Ram Air Turbines (RATs). RATs are critical in applications requiring power generation in emergency scenarios that may happen to un-manned aerial vehicles (UAVs). However, the primary challenge lies in optimizing the performance of the RATs, such as power output and operational stability, under varying and unpredictable wind conditions. Traditional control methods often fail to adapt to these dynamic environments. This paper underscores the necessity of utilizing a recurrent neural network (RNN) to detect uncertain wind turbine dynamics; a sliding mode control rule is created in the suggested controller to monitor the optimal turbine rotation speed. Next, an online update technique is developed to provide real-time weight updates for the RNN, enabling control over maximum power extraction. According to simulation results, the suggested controller outperforms a traditional control approach in tracking the optimal turbine rotation speed and extracting the most wind power from RATs by 13 times, even in severe nonlinearities and system uncertainties.

关键词： Renewable energy sources Uncertainty Recurrent neural networks Stability analysis Real-time systems Hardware Wind turbines Vehicle dynamics Sliding mode control Wind energy conversion

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks

arXiv

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arXiv 2023年

作者： Ledda, Emanuele 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

Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating uncertainty. This approach, which we call dropout injection, provides clear benefits over its traditional counterpart (which we call embedded dropout) since it allows one to obtain a post hoc uncertainty measure for any existing network previously trained without dropout, avoiding an additional, time-consuming training process. Unfortunately, no previous work compared injected and embedded dropout;therefore, we provide the first thorough investigation, focusing on regression problems. The main contribution of our work is to provide guidelines on the effective use of injected dropout so that it can be a practical alternative to the current use of embedded dropout. In particular, we show that its effectiveness strongly relies on a suitable scaling of the corresponding uncertainty measure, and we discuss the trade-off between negative log-likelihood and calibration error as a function of the scale factor. Experimental results on UCI data sets and crowd counting benchmarks support our claim that dropout injection can effectively behave as a competitive post hoc uncertainty quantification technique. © 2023, CC BY.

关键词： Monte Carlo methods

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