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arXiv 2023年

作者： Ghosn, Agapius Bou Nolte, Marcus Polack, Philip de La Fortelle, Arnaud Maurer, Markus Center for Robotics Mines Paris PSL University Paris75006 France Institute for Control Engineering TU Braunschweig Braunschweig38106 Germany Heex Technologies Paris France

Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel neural network architecture based on Long Short-Term Memory (LSTM) networks to accurately estimate the vehicle's velocity in different driving conditions, including maneuvers at the limits of handling. The approach has been tested on real vehicle data and it provides more accurate estimations than state-of-the-art model-based and learning-based methods, for both regular and near-limits driving scenarios. Our approach is robust since the performance of the state-of-the-art observers deteriorates with higher dynamics, while our method adapts to different maneuvers, providing accurate estimations even at the vehicle's limits of handling. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Towards Safe Operations in Urban Environments with UAVs

Towards Safe Operations in Urban Environments with UAVs

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Rafael Caballero Pablo Jiménez Francisco J. Pérez-Grau Antidio Viguria Aníbal Ollero CATEC (Advanced Center for Aerospace Technologies) Calle Wilbury Orville Wright Sevilla Spain Robotics Vision and Control Group (GRVC) Universidad de Sevilla Seville Spain

The incorporation of uncrewed aerial vehicles (UAV) in urban environments is expanding with countless applications currently under development or even in prototype phase. The industry is facing many challenges, not only technological but also at the regulatory level, as flying in urban environments poses a significant challenge at the bureaucratic and regulatory levels. In this paper, we contribute to mitigating both challenges, showing, on the one hand, the process of obtaining permits to perform flights in a city and the publication of the data obtained with the sensors onboard the UAV for the benefit of the community. Also, to improve operations’ safety in urban environments, we propose an obstacle detection algorithm with the data obtained.

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Intelligent Model-Free control for Tendon-Driven Continuum Robotic Arms

Intelligent Model-Free Control for Tendon-Driven Continuum R...

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

作者： Nima Maghooli Omid Mahdizadeh S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotic and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

Continuum manipulator modeling is always associated with structured and unstructured uncertainties. Therefore, model-based control system design for this class of robotic systems will be very challenging. On the other hand, the performance quality of model-free controllers is completely dependent on their hyperparameters; they are also very sensitive to the considered trajectory for the system. As a result, using model-free controllers will require setting parameters for different scenarios. In this research, the design of a model-free controller with comparable performance to model-based control strategies is presented. To this end, various parameters are determined online by the gain adjustment system. The research innovation is to use a supervised machine learning method, fuzzy inference system (FIS), to implement the intelligent gain adaptation system to achieve this goal. The Modified Transpose Jacobian (MTJ) performs well in trajectory tracking due to its approximated feedback linearization tool. In addition, the PID controller structure makes it a locally robust control strategy. An adaptive gain adjustment system can greatly increase algorithm potential and establish the capability to follow trajectories in different work points in the system work space. This research aims to improve the performance of the MTJ model-free control strategy in tracking trajectories starting from arbitrary initial conditions in the system work space. This is achieved by the gain adjustment system design using a fuzzy inference system. Both simulation and experimental results reveal the merits of the proposed controller.

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

arXiv

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

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Steady Tracker: Tracking a Target Stably Using a Quadrotor

Steady Tracker: Tracking a Target Stably Using a Quadrotor

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Hongwen Zhong, Hang Lv, Yongsheng Sha, Jianjun Long, Yu Wang, Yaonan Harbin Engineering University Harbin150001 China Qingdao Innovation and Development Center Harbin Engineering University Qingdao266000 China School of Robotics National Engineering Research Center for Robot Visual Perception and Control Hunan University China College of Electrical and Information Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781665481090

Maneuvering target tracking of Unmanned Aerial Vehicle(UAV) in cluttered environments is a challenging issue owing to the unknown motion intention of the target and the complex moving environments. As the complexity of the environment increases, stable and secure target tracking is increasingly difficult to guarantee. To address the issue, this paper proposes a stable quadrotor tracking solution. The proposed solution contains two parts: target motion prediction and tracking path searching. The target motion prediction method predicts the future target motion based on the obtained target observations while considering observation noise and prediction errors. The tracking path searching method utilizes a sampling-based search method, using the homotopy of paths to ensure that the tracking path and the target position are in the same space. Finally, simulations, real-world experiments and statistical analysis verify the correctness and effectiveness of the proposed approach. © 2022 IEEE.

关键词： Antennas

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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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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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A Multi-Model Fusion Approach for Transformer Fault Detection 4

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhou, Ke Zhang, Wei Li, Ligang Pan, Shaoming Huang, Chengwei Electric Power Research Institute of Guangxi Power Grid CO. LTD. Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning530023 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd. China

ISBN: (纸本)9798350363586

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model. © 2024 IEEE.

关键词： Data accuracy

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Synthesis of a High-Level controller for controlling a Group of Uavs in the Gazebo Simulation Environment

Synthesis of a High-Level Controller for Controlling a Group...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： L. A. Rybak A. S. Pisarenko V. M. Polyakov Scientific Research Institute of Robotics and Control Systems Belgorod State Technological University n.a. V.G. Shukhov Belgorod Russia Federal Research Center “Informatics and Management” of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

The article presents the development and experimental verification of a control system based on a highlevel controller by a group of unmanned aerial vehicles (UAVs) based on the ROS 2 framework and the PX4 autopilot in the Gazebo simulation environment. The system is designed to address challenges related to autonomous navigation, group coordination, and obstacle avoidance in complex environments. The modular architecture of the control system is described, which includes such components as a high-level controller, a Gcommand processing module, a flight control module, and a data visualization module. For modeling and testing the system, the Gazebo simulation environment is used Gazebo, which provides realistic physical modeling and integration with ROS 2. The results of modeling and experimental studies of the movement of a group of UAVs are presented, during which the correctness of command execution, coordination of the group of UAVs and the stability of the system to external influences are checked.

关键词： Process control Data visualization Systems architecture Autonomous aerial vehicles Stability analysis Autopilot Collision avoidance Autonomous robots Aerospace control Testing

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Machine Learning Strategies for Wind Turbine Fault Diagnosis: A Comparative Study

Machine Learning Strategies for Wind Turbine Fault Diagnosis...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Galina Demidova Alecksey Anuchin Denis Semenov Konstantin Savichev Electric Drive Department Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Engineering Center JSC "Power Machines" Saint-Petersburg Russia

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This study investigates the application of various machine learning (ML) models for diagnosing faults in wind turbines, with a particular focus on preprocessing SCADA data to ensure accurate model training. Several algorithms, including Light Gradient Boosting Machine (LGBM), Decision Tree (DT), Random Forest (RF), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM), were evaluated. The findings indicate that the LGBM Classifier, specifically Model 2, demonstrated the best overall performance, consistently achieving high F1-scores across different fault types. Models diagnosing faults in the wind turbine generator, feeding faults, and air-cooling faults achieved a training accuracy of 90.5% and an overall model accuracy of 70%. However, challenges were observed in diagnosing mains failure fault and excitation fault due to their lower frequency in the dataset, which impacted model accuracy. The research emphasizes the development of a robust multi-class classification approach. These results contribute valuable insights into optimizing fault diagnosis systems for wind turbines, balancing accuracy with practical implementation in real-world scenarios.

关键词： Fault diagnosis Training Support vector machines Accuracy Atmospheric modeling Boosting Data models Wind turbines Classification algorithms Long short term memory

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