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A System for Controlling Electric Drives of the Lower Extremities of an Exoskeleton Based on a Two-Term Regulator with a Neural Network

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Russian Electrical Engineering 2021年第3期92卷 154-158页

作者： Belov, M.P. Kozlova, L.P. Truong, D.D. Department of Robotics and Industrial Automation St. Petersburg Electrotechnical University St. Petersburg197376 Russia

Abstract: A two-term (TT) regulator with a compensator based on a neural network (NN) is proposed for use in the system of controlling electric drives of the lower extremities of an exoskeleton to compensate uncertain changes in gravity and friction in the joints of the skeleton’s mechanical legs. The mathematical model of the lower extremities (two legs with five links) is drawn on a sagittal plane, taking nonlinear elements and external disturbances into account. Results of modeling the controlled movement of the exoskeleton in the sagittal plane are provided. According to the modeling results, this exoskeleton will enable the users to rhythmically move their hip and knee joints. © 2021, Allerton Press, Inc.

关键词： Electric drives

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An End-to-End Proprioception Framework for Soft Continuum Robot

An End-to-End Proprioception Framework for Soft Continuum Ro...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Cao, Hanwen Chen, Wei Lu, Yiang Huang, Junda Zhou, Jianshu Liu, Yunhui The Chinese University of Hong Kong The T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Soft robots are gaining more and more attention owing to their inherent compliance and excellent flexibility, enabling new potentials for robots to address real-world chal-lenges in the manner of their natural counterparts. The soft con-tinuum robot is one typical example that reflects the successful inspiration achievement from biomimetics to robotics. However, due to the nonlinearity and ultra-high degrees of freedom of soft continuum robots, traditional kinematic modeling methods obtain unsatisfying precision, especially when robots interact with their surroundings. To precisely estimate the kinematic model of soft continuum robots, we propose a novel end-to-end proprioception method. Our method inputs time series data from the soft continuum robot actuators and outputs the end-tip position in three dimensions. The experimental results show that the proposed method can improve the kinematic accuracy compared to the constant curvature model-based method either with contact or in noncontact conditions. © 2022 IEEE.

关键词： Robots

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Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation

arXiv

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arXiv 2024年

作者： Jia, Yongyi Miao, Shu Zhou, Junjian Jiao, Niandong Liu, Lianqing Li, Xiang The Department of Automation Tsinghua University China The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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SmartDrive: The Ultimate EV-IoT Control Hub 5

SmartDrive: The Ultimate EV-IoT Control Hub

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Elavarasi, K. Rajasekar, P. Chandra Mouli, B. Siva Subramanian, P. Bharanidharan, R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Robotics and Automation India SSM Institute of Engineering and Technology Department of Electrical and Electronics Engineering Engineering India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

IoT technology is crucial for advancing smart automation in electric vehicles (EVs). Interest in smart grids has surged due to the growth of distributed generation, the aging infrastructure of the existing grid, and the push for network modernization. As accurate usage predictions gain importance, so does the need for efficient energy storage systems, particularly for electrical energy. Green electrical energy storage systems are common in EVs due to their excellent electrical properties and adaptability, but the grid faces challenges from potential battery damage caused by overcharging, deep discharging, and high load surges. In this project, hardware utilizing GPS and IoT technology was developed to monitor and manage both the EV and its battery systems. A Battery Management System (BMS) with real-time monitoring and GPS tracking capabilities estimates the State of Charge (SoC) using coulomb counting techniques, alongside an MQTT protocol for message-based communication. The proposed BMS is implemented on a hardware platform utilizing the Arduino environment, a central processor, suitable sensing technologies, and interface devices. Project code was developed in the Arduino IDE and applied to the hardware system. © 2024 IEEE.

关键词： Battery management systems

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Extending Knowledge Representation System about Planners of Intelligent Information Agents

Extending Knowledge Representation System about Planners of ...

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2023 Seminar on Information Systems Theory and Practice, ISTP 2023

作者： Ptitsyna, Larisa K. Ptitsyn, Nikita А. El Sabayar Shevchenko, Nidal Belov, Mikhail P. Management Information Systems Department The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Robotics and Automation in Production Systems Department Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9798350345193

The article presents the reasons for the relevance of extending knowledge about action planners of intelligent information agents. The key trends for the necessary extending in the context of the development of the digital economy are highlighted. The concept of forming a system for representing knowledge about action planners of intelligent information agents is proposed. The basic components of the quality profiles of the considered artifacts are described. Classes of models of action planners and methods for their analysis are defined. The formed components of the knowledge representation system about generic action planners of intelligent information agents are considered. ©2023 IEEE.

关键词： Knowledge representation

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Visual-Lidar Odometry with Orientation Correction towards Millimeter-Level Localization for Indoor Construction Robots

Visual-Lidar Odometry with Orientation Correction towards Mi...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Hisatsugu, Hiroki Koshizuka, Hisahiro Hori, Moritoyo Yamamoto, Kenjiro Center for Sustainability Hitachi Ltd. Connective Automation Innovation Center Robotics Research Department Japan Hitachi Channel Solutions Corp. Channel Business Promotion Center Sales Department Japan Hitachi Channel Solutions Corp. Channel Business Promotion Center Products Development Department Japan

ISBN: (纸本)9798331531614

We propose a high-precision odometry method that fuses visual-lidar odometry with orientation information estimated by ceiling line features, aiming to achieve millimeter-level localization required at construction sites. Visual-Lidar Odometry and Mapping has demonstrated top-class accuracy with an error of 50 mm in benchmarks. However, when evaluated in environments with large open walls, such as indoor construction sites with few shape and image features, the method was found to be less accurate primarily due to errors in the orientation estimation. To address this issue, we propose a method that combines the use of ceiling line features to estimate the robot's orientation angle, thereby improving the accuracy of the odometry position estimates. We evaluated the accuracy using a real robot in an environment similar to a construction site and achieved an average error of 6.2 mm (straight-line route), 7.6 mm (rectangular route), surpassing the accuracy of conventional methods. These results confirm the effectiveness of utilizing orientation correction from ceiling line features. © 2025 IEEE.

关键词： Ceilings

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Application of Bayes Theory Model to Determine the Optimal Variant of Technological Process for Production of MEMS Components

Application of Bayes Theory Model to Determine the Optimal V...

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International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Igor Nevliudov Sofiia Khrustalova Olena Chala Andrii Sliusar Department of Computer-Integrated Technologies Automation and Robotics Kharkiv National University of Radio Electronic Kharkiv Ukraine

Bayesian model is proposed for determining the optimal variant of the MEMS component manufacturing technological process A software tool has been developed to automate this process Calculations are performed using the... 详细信息

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

关键词： Micromechanical devices Solid modeling Costs Design methodology Production Data models Bayes methods

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Electromechanical Systems Continuous Diagnostics Through Parametric Identification and Products Digitalization

Electromechanical Systems Continuous Diagnostics Through Par...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Irina Kolupaieva Igor Nevliudov Yurii Romashov Department of Computer-Integrated Technologies Automation and Robotics Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

Formalized universal approaches, which can be suitable for industrial products digitalization implementations providing the real time continuous technical diagnostics of electromechanical systems are developed. These approaches are based on the parametric identification of electromechanical systems by means the least square method with digitalization of real time measurements data. Increasing of the diagnostics accuracy with decreasing of the time discretization step of measured data digitalization shown through computer simulations proves correctness of the developed generalized approaches and it gives the ways of the further researches.

关键词： Phase measurement Current measurement Mechanical variables measurement Data processing Time measurement Mathematical models Real-time systems Reliability Electromechanical systems Electric motors

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Theoretical Data-Driven MobilePosenet: Lightweight Neural Network for Accurate Calibration-Free 5-DOF Magnet Localization

arXiv

引用

arXiv 2025年

作者： Xie, Wenxuan Zhang, Yuelin Shan, Jiwei Sun, Hongzhe Tan, Jiewen Cheng, Shing Shin Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

Permanent magnet tracking using the external sensor array is crucial for the accurate localization of wireless capsule endoscope robots. Traditional tracking algorithms, based on the magnetic dipole model and Levenberg-Marquardt (LM) algorithm, face challenges related to computational delays and the need for initial position estimation. More recently proposed neural network-based approaches often require extensive hardware calibration and real-world data collection, which are time-consuming and labor-intensive. To address these challenges, we propose MobilePosenet, a lightweight neural network architecture that leverages depthwise separable convolutions to minimize computational cost and a channel attention mechanism to enhance localization accuracy. Besides, the inputs to the network integrate the sensors' coordinate information and random noise, compensating for the discrepancies between the theoretical model and the actual magnetic fields and thus allowing MobilePosenet to be trained entirely on theoretical data. Experimental evaluations conducted in a 90 × 90 × 80 mm workspace demonstrate that MobilePosenet exhibits excellent 5-DOF localization accuracy (1.54 ± 1.03 mm and 2.24 ± 1.84◦) and inference speed (0.9 ms) against state-of-the-art methods trained on real-world data. Since network training relies solely on theoretical data, MobilePosenet can eliminate the hardware calibration and real-world data collection process, improving the generalizability of this permanent magnet localization method and the potential for rapid adoption in different clinical settings. © 2025, CC BY-NC-ND.

关键词： Calibration

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Active Search and Relocalization Framework for Position-Lost Recovery Under Limited Number of AMRs

Active Search and Relocalization Framework for Position-Lost...

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IEEE/SICE International Symposium on System Integration

作者： Hiroki Hisatsugu Jiro Hashizume Robotics Research Department Connective Automation Innovation Center Center for Sustainability Hitachi Ltd. Japan

Autonomous mobile robots are required to operate continuously in uncertain environments without human intervention. Addressing the self-localization failure problem, this paper proposes an efficient re-localization method for multiple autonomous mobile robot systems that utilizes lost-robot searching actions by surrounding robots, based on a rough estimation of the lost-robot's positional area. This approach ensures swift re-localization through active movements, effectively countering occlusion challenges inherent to cooperative localization. The system implemented functions for lost detection, searching, relative position recognition and information sharing. Real-world experiments in a 5 x 6 m indoor environment demonstrate the effectiveness of our approach. Re-localization is successfully achieved within 0.5 m of localization error in all 20 tests in an environment of occlusion between two robots.

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