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IEEE International Conference on robotics and Automation (ICRA)

作者： Pietro Balatti Idil Ozdamar Doganay Sirintuna Luca Fortini Mattia Leonori Juan M. Gandarias Arash Ajoudani HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Systems Engineering and Automation Department Robotics and Mechatronics Lab University of Malaga Malaga Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

关键词： Target tracking Navigation Robot kinematics End effectors Real-time systems Path planning Sensors

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Human Hand Gesture Recognition System Using Body Sensor Network 4

Human Hand Gesture Recognition System Using Body Sensor Netw...

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4th International Conference on robotics and Automation in Industry, ICRAI 2021

作者： Maheen, Asra Bin Shahzad, Moosa Asif, M. Usama Ahmad, Syed Fasih Zafar, Saqib Maqbool, H. Farhan Usman, Muhammad Arshad, Hira Department of Mechanical Mechatronics And Manufacturing Engineering Faisalabad Pakistan Human-Centered Robotics Lab National Center of Robotics And Automation Lahore Pakistan Pakistan Engineering Council Islamabad Pakistan Department of Mechanical Mechatronics And Manufacturing Engineering Faisalabad Pakistan

ISBN: (纸本)9781665423434

Wearable technology has gained massive popularity for applications in healthcare in recent years. Body sensor network (BSN) includes wearable sensors such as Myoware muscle sensor that can capture electromyography (EMG) signal from human muscles during a movement. This study involves the control of a 3d prosthetic hand using EMG data of basic hand gestures from healthy subjects and operating the prosthetic hand with this classified data. Artificial Neural Networks (ANN) are biologically inspired computational networks which help to build predictive models from large databases. Backpropagation combined with feedforward ANN is used to classify data here. Classification results showed an accuracy of about 91% for four-hand gestures - hand closed, hand open, pointing index finger, and pinch motion. The BSN-based prosthetic hand can facilitate rehabilitation of amputees, perform surgery using recorded EMG data from a surgeon's hands and reduce industrial manpower in the future. © 2021 IEEE.

关键词： Neural networks

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Usama Younis Faisal Riaz Umar Raza Gokhan Atali Hamid Rabnawaz Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Engineering Manchester Metropolitan University Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya Turkey Selftech Pvt Ltd

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions.

关键词： Accuracy Computational modeling Vehicle detection Neural networks Road safety Reliability robotics and automation Optimization Autonomous vehicles Standards

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Continuous learning with random memory for object detection in robotic applications

Continuous learning with random memory for object detection ...

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Nenakhov, Ivan Mazhitov, Ruslan Artemov, Kirill Zabihifar, SeyedHassan Semochkin, Aleksandr Kolyubin, Sergey ITMO University Biomechatronics and Energy-Efficient Robotics Lab St. Petersburg Russia PJSC Sberbank Sber Robotics Laboratory Moscow Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781665424066

If the robot is to interact with environment designed for humans it has to be able to cope with new objects in it's surrounding, and not only to classify but also effectively localize objects in its reach based on visual sensing. We cannot predict all objects the robot will face. So it needs to learn them while operating, without an external update of software. One of the promising approaches to handle this task is using detectors based on convolution neural networks (CNN) with continuous learning setup. However, there is a well-known problem of catastrophic forgetting (CF) that negatively affects accuracy of neural networks, the main algorithms in object recognition nowadays. Moreover, we need to reach a trade off between training time (because it is unacceptable if the robot trains for hours) and good acccuracy of detection. In this paper we review some of the latest approaches in the field and adapt one of SOtA methods in classification task to detection task. Particularly, we adapt YOLOv5 to work in scenario with continuous learning by adding random memory mechanism from [1] and applying layer freezing in order to increase training speed. We benchmark the proposed method using iCubWorld Transformations dataset and report results with significantly improved metrics and inference speed. © 2021 IEEE.

关键词： Object detection

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Improved Efficiency of Weakly Coupled Wireless High-Power Transfer Systems by Loss-Separation Strategy

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International Journal of Circuit Theory and Applications 2025年第0期

作者： The Nguyen, Hoach Al-Sumaiti, Ameena Saad Alhassan, Ahmad Bala Do, Ton Duc Smart OR Lab Advanced Power and Energy Center Department of Electrical Engineering Khalifa University Abu Dhabi United Arab Emirates Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan

In the equivalent T-model of the loosely coupled transformers, the small mutual inductance can lead to higher conducting currents, which cause high losses in the primary circuit and significantly reduce the overall transfer efficiency under weak-coupling states. To overcome this challenge, this paper proposes a strategy to separate the primary loss components from the weak-coupling stage. In the proposed strategy, a gyrator in the form of a double-resonance T-block is added just before the weak-coupling stage to improve the overall efficiency. Then, the strategy is realized by various topologies such as compensating circuits, added coils, isolated transformers, and integrated-/split- inductors/coils. Also, the optimized designs and component selection for resonance and the mathematical derivation for optimal load resistances were investigated. ANSYS comparative analyses of the topologies are presented by considering practical aspects, including component designs, power flows, transfer efficiency, resonance frequency shifting, and optimal loads. Finally, the analyses were validated using the fabricated experimental setups and demonstrated an efficiency improvement of about 5% for a case with a coupling factor of 0.1. The proposed strategy offers suggestions for industrial designs of high-power wireless battery charging systems using resonant inductive coils. © 2025 John Wiley & Sons Ltd.

关键词： Inductive power transmission

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Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Diagnosis of Localized Bearing Defects using Texture Analysi...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Nguyen, Cong Dai Nasir, Fazal E. Khan, Shahbaz Khan, Sheraz Ali Kim, Jong-Myon Le Quy Don Technical University Faculty of Radio-Electronic Engineering Hanoi Viet Nam Advanced Robotics and Automation Lab National Center for Robotics and Automation Peshawar Pakistan Institute of Manufacturing University of Engineering and Applied Sciences Swat Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan University of Ulsan Department of IT Convergence Ulsan Korea Republic of PD Technology Ulsan Korea Republic of

ISBN: (纸本)9781665464727

Bearings are important because they play a critical role in the operation of many machines and devices by reducing friction and enabling smooth motion between two moving parts. They are commonly used in rotating machinery, such as electric motors, turbines, pumps, and gearboxes, where they support and guide the rotating shafts. The primary cause of failure in wind turbines and induction motors is bearing faults, resulting in significant downtime and economic losses. Detecting these faults accurately and promptly is crucial to preventing unexpected shutdowns. Most of the existing methods for bearing fault detection rely on envelope analysis, which involves filtering and demodulating the raw signal, and Fourier analysis of the envelope signal to identify defect frequencies. Recent research has focused on improving the visibility of these frequencies by selecting an optimal band for filtering, using sub-band analysis and spectral kurtosis, which is a complex process and can be time-consuming. This paper presents a new approach to envelope signal analysis using texture analysis of the envelope signals that are projected as 2D grayscale images where each fault generates a unique texture. These textures are encoded using the local binary pattern operator and then used for fault classification. The method is tested on a publicly available seeded fault dataset. © 2023 IEEE.

关键词： Signal analysis

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Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region

Enhancing Model-Based Step Adaptation for Push Recovery thro...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tobias Egle Yashuai Yan Dongheui Lee Christian Ott Automation and Control Institute TU Wien Vienna Austria Autonomous Systems Lab TU Wien Vienna Austria Institute of Robotics and Mechatronics (DLR) German Aerospace Center Wessling Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory.

关键词： Legged locomotion Limiting Perturbation methods Humanoid robots Reinforcement learning Robustness Real-time systems Timing Trajectory IEEE Regions

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Advancement in Smart Cane Technology: Enhancing Mobility for the Visually Impaired Using ROS and LiDAR

Advancement in Smart Cane Technology: Enhancing Mobility for...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Saqib Zafar Hafiz Farhan Maqbool Naveed Ahmad Arham Ali Abdul Moeizz Faizan Ali Juri Taborri Stefano Rossi Department of Economics Engineering Society and Business Organization University of Tuscia Viterbo VT Italy Department of Mechanical Mechatronics and Manufacturing Engineering University of Engineering and Technology Lahore (Faisalabad Campus) Pakistan Human-Centred Robotics Lab National Center of Robotics and Automation Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Visual impairment affects the ability to navigate independently in an unknown environment due to reliance on human/dog assistance, potential miscommunication, and discomfort during mobility. To make the visually impaired (VI) individuals self-reliant and confident in their mobility the proposed system has been developed that contains a light detection and ranging (LiDAR) sensor for obstacle detection and two omni-wheels coupled with the DC gear encoder motors for precise movement in any direction without lifting the cane. Hector Simultaneous Localization and Mapping (SLAM), A *, and steering wheel algorithms have been implemented in the robot operating system (ROS) for real-time environment mapping, efficient path planning, and navigating in an unknown indoor static environment, respectively. The Raspberry Pi 3B+ and Arduino Mega 2560 are used in the master-slave configuration for decision-making on path planning and controlling the maneuverability of the system in real-time. The proposed system in the paper is successfully tested on one subject in two indoor environments. The results demonstrate that the system serves as an assistive mobile robot, helping the VI individual to navigate confidently in an unknown environment and reach the destination without assistance.

关键词： Simultaneous localization and mapping Laser radar Navigation Operating systems Decision making Visual impairment Wheels Real-time systems Path planning robotics and automation

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Learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

Learning a Shape-Conditioned Agent for Purely Tactile In-Han...

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

作者： Johannes Pitz Lennart Röstel Leon Sievers Darius Burschka Berthold Bäuml Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany DLR Institute of Robotics & Mechatronics (German Aerospace Center) Machine Vision and Perception Group Technical University of Munich

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ~90% even for non-convex ***: https://***/manipulation/iros24

关键词： Training Visualization Three-dimensional displays Torque Shape Tactile sensors Reinforcement learning Position measurement Vectors Sensors

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The Impact of Robots’ Facial Emotional Expressions on Light Physical Exercises

arXiv

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arXiv 2023年

作者： Abdulazeem, Nourhan Hu, Yue Active & Interactive Robotics Lab Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L3G1 Canada

To address the global challenge of population aging, our goal is to enhance successful aging through the introduction of robots capable of assisting in daily physical activities and promoting light exercises, which would enhance the cognitive and physical well-being of older adults. Previous studies have shown that facial expressions can increase engagement when interacting with robots. This study aims to investigate how older adults perceive and interact with a robot capable of displaying facial emotions while performing a physical exercise task together. We employed a collaborative robotic arm with a flat panel screen to encourage physical exercise across three different facial emotion conditions. We ran the experiment with older adults aged between 66 and 88. Our findings suggest that individuals perceive robots exhibiting facial expressions as less competent than those without such expressions. Additionally, the presence of facial expressions does not appear to significantly impact participants’ levels of engagement, unlike other state-of-the-art studies. This observation is likely linked to our study’s emphasis on collaborative physical human-robot interaction (pHRI) applications, as opposed to socially oriented pHRI applications. Additionally, we foresee a requirement for more suitable non-verbal social behavior to effectively enhance participants’ engagement levels. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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