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

作者： Jeonghyeon Yoon Junhyun Park Hyojae Park Hakyoon Lee Sangwon Lee Minho Hwang Department of Robotics and Mechatronics Engineering DGIST Daegu Republic of Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon’s working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.

关键词： Measurement Medical robotics Minimally invasive surgery Accuracy Kinematics End effectors Optimization Intelligent robots

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Lower Limb Joint Torque Estimation via Bayesian Regularized Backpropagation Neural Networks 2

Lower Limb Joint Torque Estimation via Bayesian Regularized ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Narayan, Jyotindra Gritli, Hassène Dwivedy, Santosha K. Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India National Engineering School of Tunis Le Belvédère Tunis1002 Tunisia

ISBN: (纸本)9798350360523

Prior gait analysis has played a crucial role in assessing overall health and well-being, particularly in individuals of different age groups. Nevertheless, acquiring gait data has necessitated the in-person presence of participants, incurring potential expenses. We introduce a deep learning technique called Bayesian regularized backpropagation neural network (BR-BPNN) to estimate joint torques in lower-limb motion based on anthropometric data and joint movements. The study collects joint movement data from 40 healthy participants (25 males and 15 females) spanning ages 7 to 65, utilizing an established motion-capture system. The input data encompassed various parameters such as age, gender, total body mass, height, thigh length, calf length, ankle-foot length, thigh mass, calf mass, ankle mass, and joint angles for the hip, knee, and ankle. The output data included joint torques calculated for a simplified leg model using the inverse dynamics method derived from the Euler-Lagrangian principle. We presented the conceptual details of the proposed BR-BPNN model and contrast model named Levenberg-Marquardt-based BPNN (LM-BPNN). Performing a comparative analysis, the results revealed that the BR-BPNN model (Rtrain = 0.985, Rtest = 0.955) excelled in accurately estimating joint torques compared to the LM-BPNN model (Rtrain = 0.973, Rtest = 0.924). Furthermore, the BR-BPNN model is found to be effective by 87.19%, 77.87%, and 72.73% in estimating hip, knee, and ankle joint torques for a 51-year-old male subject from the testing dataset. © 2024 IEEE.

关键词： Neural network models

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Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

arXiv

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arXiv 2025年

作者： Park, Myeongbo An, Chunggil Park, Junhyun Kang, Jonghyun Hwang, Minho Department of Robotics and Mechatronics Engineering DGIST Daegu42988 Korea Republic of

Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis—caused by friction, backlash, and tendon elongation—leads to significant tracking errors. Conventional modeling and compensation methods struggle with these nonlinearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon’s movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS. Copyright © 2025, The Authors. All rights reserved.

关键词： Friction

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Experimental and numerical study of deposition mechanisms for cold spray additive manufacturing process

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Chinese Journal of Aeronautics 2022年第2期35卷 276-290页

作者： Tianyu YU Mingjun CHEN Zhuoru WU State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Department of Mechanical Engineering Iowa State UniversityAmes 50011USA

Cold spray is an attractive and rapidly developing process for additive manufacturing with high efficiency and precision,repairing and coating,especially in aircraft and aerospace *** spray additive manufacturing deposits micro-particles with large plastic deformation below their melting point,eliminating heat effect zone which could deteriorate the quality of repairing *** particle deposition in cold spray is a complex process which involves high strain rate,high contact pressure and high *** we develop,utilize and validate a thermomechanical model to provide a definitive way to predict deposition mechanics and surface deformation evolution for particle deposition process in cold spray additive *** a single particle and dual particles models were developed to investigate the contact interaction between particle/substrate and particle/*** combinations of particle/substrate materials(Cu/Cu,Al/Al,steel/steel,and nickel/nickel)and process parameters were considered in this *** experimental study was conducted to validate simulation results,providing useful information for understanding the limitations and challenges associated with cold spray additive *** framework provides insights into improving the quality and precision of stress/strain formation,particle interactions and particle deposition in cold spray additive manufacturing process.

关键词： Additive manufacturing Cold spray Finite element analysis Residual stresses Surface morphology

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Multi-Class Machine Learning Based Approach for SleepDisorder Identification Using EEG Signals 7

Multi-Class Machine Learning Based Approach for SleepDisorde...

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7th IET Smart Cities Symposium, SCS 2023

作者： Murugesan, Rajalakshmi Chandran, Karthik Dutta, P.K. Department of Mechatronics Thiagarajar College of Engineering Tamilnadu Madurai India Department of Robotics & Automation Jyothi Engineering College Kerala India Department of Electronics and Communication School of Engineering and Technology Amity University Kolkata India

ISBN: (纸本)9781839539831

Accurately identifying sleep stages is crucial for the analysis and management of sleep disorders. Electroencephalogram (EEG) signals have shown promise in sleep stage identification. This research presents a novel method for classifying different sleep stages using EEG data based on a multi-class support vector machine (SVM) approach. The proposed method involves preprocessing the EEG signals to remove noise and artifacts, followed by feature extraction using wavelet transform and statistical methods. A multi-class SVM classifier is then trained to identify four sleep stages: waking, non-rapid eye movement (NREM) stage 1, non-rapid eye movement (NREM) stage 2, and rapid eye movement (REM) stage. The method is evaluated on a dataset comprising EEG signals collected from 20 healthy individuals during a full-night sleep study. The results demonstrate that the proposed technique outperforms existing EEG-based approaches, achieving anaverage classification accuracy of 86.2 for sleep stage identification. The suggested method has potential applications in clinical settings for sleep disorder evaluation and management. Additionally, it can be extended to other applications requiring the identification of various classes from EEG data. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Path Tracing and Validation of Omni Wheeled Mini Robot Using Visual Feedback

Path Tracing and Validation of Omni Wheeled Mini Robot Using...

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International Conference on Advanced Computer Control, ICACC

作者： Aftaab Ahamed Karthik Jayaraj Shibin Fazil Nikitha S Sudheer A P Robotics Interest Group Mechatronics/Robotics Laboratory National Institute of Technology Calicut Mechanical Engineering Department Mechatronics/Robotics Laboratory National Institute of Technology Calicut

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Holonomic drives are gaining popularity in many fields due to their versatility in various industrial applications. The ability of the holonomic drive robots to move in all directions makes it distinct from other drive mechanisms. Open-loop control of omnidirectional bots can be achieved with relative ease whereas automating it is not as easy. When trying to create an automated system with lower cost, off the shelf components and hardware, other problems may arise as a result of the poor performance of the components. Here a three wheeled omnidirectional robot that can trace a predefined path without human intervention has successfully been developed.

关键词： Location awareness Visualization Costs Service robots Debugging Drives Hardware Mobile robots Robots Wireless fidelity

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Multi-purposeful activities for robot-assisted autism therapy: What works best for children's social outcomes? 23

Multi-purposeful activities for robot-assisted autism therap...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Zhanatkyzy, Aida Telisheva, Zhansaule Amirova, Aida Rakhymbayeva, Nazerke Sandygulova, Anara Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Graduate School of Education Nazarbayev University Astana Kazakhstan

ISBN: (纸本)9781450399647

This research designed and applied 24 multi-purposeful robot activities of varying social mediation levels in a multiple-session experiment with 34 children of diverse autistic characteristics in a rehabilitation setting. This paper explores what type of robot activities can meet individual needs to bring more socio-behavioral progress and juxtaposes child characteristics to identify behavioral outcomes in each activity. This knowledge would help us to respond to the question of what activity types suit specifc subgroups of Autism Spectrum Disorder (ASD). Our data analysis included coding 48.5 hours of video data for a total of 14 measures to fully capture children's activity-based socio-emotional outcomes. Overall, the activities on varying social mediation levels brought more or less positive social outcomes to all children. However, children showed some diferent behavioral outcomes as mediated by core autism-related and age-specifc characteristics. This study provides in-depth accounts of what might be helpful in designing and applying multi-purposeful activities responsive to the diverse needs of children. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Robots

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths 3

Investigating Neural Network Hyperparameter Variations in Ro...

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3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Bouzid, Rania Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India Tunis1002 Tunisia

ISBN: (纸本)9798350349207

This research delves into the evaluation of Artificial Neural Networks (ANNs) in tackling the intricate domain of inverse kinematics problems within the context of robotic systems. Compared to direct kinematics, inverse kinematics presents significant challenges, especially when dealing with different arm lengths, which can impact the system's overall functionality. In this study, we conduct a comprehensive comparative analysis to assess the performance of ANNs across various robotic arm lengths. Following the forward kinematics of a simple 2-DoF robotic manipulator, three distinct datasets (fixed step size, random step size, and sinusoidal step size) are generated for training, testing, and validation phases. For every dataset, three optimizers, namely Levenberg Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient, are considered with different hyperparameters to investigate the performance of the ANN models. Based on the validation of MSE values, ANN models with different hyperparameters are found to be a promising approach in estimating the inverse kinematic solutions for different arm lengths with data complexities. Such solutions can be further extended to higher DoF robotic solutions and save the computational burden of evaluating analytical solutions. © 2024 IEEE.

关键词： Inverse kinematics

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Implementation of HRI System for Mobile Manipulator to be Operated in the Elevator

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Journal of Institute of Control, robotics and Systems 2022年第8期28卷 714-723页

作者： Kang, Min-Woo Kim, Tae-Hyeon Suh, Hyun-Uk Kim, Donghan Department of Electronics Engineering Kyung Hee University Korea Republic of Department of Robotics and Mechatronics Korea Institute of Machinery Materials Korea Republic of Kyung Hee University Korea Republic of

Unlike conventional industrial robots that operate in a limited manufacturing environment, service robots should be able to operate in various environments. Where there are many complex high-rise buildings, service robots require the ability to ride and control elevators. Furthermore, a human-robot interaction (HRI) approach is critical because service robots have the characteristics of interacting directly with humans. Therefore, this paper proposes an HRI system that enables direct elevator button manipulation to enable mobile manipulator robots to perform tasks in various high-rise building environments. The system is divided into three subsystems: Elevator button recognition, command reception through voice recognition, and elevator button operation. Elevator button recognition and command reception through voice recognition receive the required topics and communicate with the lower end of the elevator button operation. Although the three distinct substructures interact with each other and are independent, they can be replaced by different algorithms at any time. Because of the same characteristics, the system can be applied to various mobile manipulators with different specifications. The experiment proceeds with pressing the button on floors B1-4 through voice recognition in a simulation environment and pressing the button on floors 5-7 without voice recognition. The experimental results compared the central positions of the button and end effector’s x, y, and z coordinates after control. The system, verified through simulation, has since been applied to real robots. © ICROS 2022.

关键词： Human robot interaction

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language 11

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Fawzy, Arwa Hassan, Ahmed Gad, Ahmed Zaky, Ahmed B. El-Hussieny, Haitham Alexandria Egypt program Alexandria Egypt Benha University Faculty of Engineering at Shoubra Electrical Engineering Department Egypt Egypt-Japan University of Science and Technology Department of Mechatronics and Robotics Engineering Alexandria Egypt

ISBN: (纸本)9798350369328

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain. © 2023 IEEE.

关键词： Emotion Recognition

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