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16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Zhong, Heran Zhou, Zhihao Wang, Qining Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China Institute for Artificial Intelligence Peking University Beijing100871 China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing100871 China

ISBN: (纸本)9783031457043

After a transfemoral amputation (TFA), individuals’ movement patterns have numerous changes. TFA significantly induces asymmetry not only in the lower limbs but also in the upper body. The study aims to assess the extent and underlying causes of kinematic asymmetry in the pelvis, torso and spine during gait with a passive prosthesis after a TFA. In the experiments, a single participant with a passive transfemoral prosthesis walked on a treadmill at three different speeds (0.6, 0.8, and 1.2 m/s), while kinematic data were collected. The key outcomes analyzed the kinematics of the pelvis, shoulders, and spine in both the frontal and horizontal planes. Results indicate that all these segments exhibited asymmetry in both planes, with certain parameters being associated with walking speed. Specifically, the upper body asymmetry in TFA amputees was attributed to compensation by the torso and pelvis. This finding underscores the importance of incorporating pelvic and torso training in gait rehabilitation for TFA patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Kinematics

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Development of a Lower Limb Gait Asymmetry Index with Cyclogram Analysis Using Image Processing 12

Development of a Lower Limb Gait Asymmetry Index with Cyclog...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Etaati, Sepideh Javaherat, Fatemeh Delrobaei, Mehdi K. N. Toosi University of Technology Faculty of Electrical Engineering Department of Mechatronics Tehran Iran K. N. Toosi University of Technology Advanced Robotics and Automated Systems lab. Faculty of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Ahstract- This study focuses on developing a gait asymmetry index and applying image processing techniques to cyclogram analysis. We evaluated gait asymmetry under different walking conditions, specifically examining the effects of wearing a knee brace and holding a weight in hand. Using inertial measurement units (IMUs), we collected gait data to analyze knee, hip, and ankle angles in the sagittal plane. The joint kinematics data were assessed using our proposed asymmetry index, which combines image processing techniques and linear regression models. The proposed asymmetry index incorporates cyclogram area, trend symmetry, and the longest length of the cyclogram shape, effectively capturing variations in symmetry and providing a comprehensive measure. The results demonstrate that the proposed asymmetry index can effectively distinguish between different gait conditions. The findings potentially offer insights for researchers and clinicians in human biomechanics, aiding in the planning of interventions and the assessment of motion patterns, as well as for developers of bipedal walking robots in the field of robotic locomotion, enhancing their ability to design hardware and evaluate motion patterns. © 2024 IEEE.

关键词： Robot programming

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Zhu, Kaiwei Kong, Shihan Wu, Fang Sun, Jinan Yu, Junzhi College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China SPIC Nuclear Energy Co. Ltd. China Peking University National Engineering Research Center for Software Engineering Beijing100871 China

ISBN: (纸本)9798350372601

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world. © 2024 IEEE.

关键词： Target tracking

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Design of a Remote Controlled Puppet Robot Imitating a Manual Driven Puppet Using Deep Learning Pose Detection

Design of a Remote Controlled Puppet Robot Imitating a Manua...

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

作者： Hamed Hamzeh Kiarash Shahroozi Ahmad Nabipour Parham Kazemi Mohammad Malek-zahedi Mehdi Hallajian Hadi Moradia Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particularly in the presence of children in educational settings. In this paper, we present the design, development, and integration of a cost-effective robotic system for remotely manipulating puppets in real-time. Additionally, the system integrates a YOLOv8 Pose Detection model, trained on a custom dataset with an accuracy of 91.4%, enabling real-time gesture recognition and allowing the robot to be controlled by imitating the motions of a manually operated puppet. The system demonstrates the potential for many applications, including interactive entertainment, teleoperation, and education.

关键词： Hands Mechatronics Education Entertainment industry Manuals Gesture recognition Real-time systems Maintenance Robots Intelligent control

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Deep Learning Based Foreign Object Debris (FOD) Detection on Runway

Deep Learning Based Foreign Object Debris (FOD) Detection on...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Alka Kumari Abhishek Dixit Pooja Agrawal School of Robotics Defence Institute of Advanced Technology Pune India Aerospace Engineering Defence Institute of Advanced Technology Pune India

An improved method for detecting Foreign Object Debris (FOD) in a runway environment is presented in this paper. The method involves implementing detection model based on deep learning algorithm which can classify and detect object with high accuracy i.e mean Average Precision (mAP). Regular method like radar technology and optical imaging system are used more frequently but these are sensitive to weather and this affects the accuracy. To avoid this Computer Vision (CV) and Machine Learning (ML) are being used. In this work, use of YOLO model has been done and the dataset taken is named FOD in Airports (FOD-A) which is publicly available and contains 31 object categories (nuts, bolts, washers, safety wires, etc.) and over 30,000 object instances. Deep learning model YOLO v5 and YOLO v8 are used for FOD detection and results are compared. In the study, it is analysed that, the average model accuracy, specifically mAP50 and mAP50-95, improved by 0.163% and 7.714%, respectively. The study brings the efficacy of YOLO v8. Here, YOLO v8 ensured better results with mAP50 of 99.022% and mAP50-95 of 88.354% for FOD detection.

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Research on multi-motor speed synchronous control based on mean coupling control 9

Research on multi-motor speed synchronous control based on m...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wu, Xiong Wei, Shouqi You, Jinwei Sun, Jianquan School of Mechanical and Engineering Guilin University of Electronic Technology Guilin541004 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781510672802

In order to improve the tracking and synchronization performance of multi-motor synchronous system after disturbance in steady state operation, a multi-motor control system was proposed. Firstly, the multi-motor speed synchronization control structure was designed based on the traditional mean coupling control (MCC) structure. Then the particle swarm optimization and the improved variable speed integral term were introduced into it to optimize the regular compensation factors and the parameters of PI controller. The improved system was called MCC_PSO system. In MATLAB/Simulink, we chose three permanent magnet synchronous motors (PMSM) to build the multi-motor synchronous control system model and from the results we can know the improved synchronous control system is superior to the traditional control system in both tracking performance and synchronization performance. © 2024 SPIE.

关键词： mean coupling multi-motor synchronous control particle swarm optimization variable speed integration

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Representation of Human Arm Dynamic Intents with An Electrical-Impedance-Tomography (EIT)-Driven Musculoskeletal Model for Human-Robot Interaction

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IEEE Transactions on robotics 2025年 41卷 3278-3296页

作者： Zheng, Enhao Liu, Xiaodong Xu, Chenfeng Zhou, Zhihao Wang, Qining State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute for Artificial Intelligence Peking University Beijing100871 China Faculty of Engineering School of Advanced Manufacturing and Robotics Peking University The Institute for Artificial Intelligence Beijing100871 China

Representing human arm dynamic intent is essential for efective human-robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses signifcant challenges. This study introduces an EIT-driven musculoskeletal model which integrates an EIT sensing system with methods for muscle identifcation, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multi-DoF wrist kinematics estimation under varying contraction intensities, arm endpoint stifness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks. © 2025 IEEE. All rights reserved.

关键词： Muscle

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TDANet: Target-Directed Attention Network For Object-Goal Visual Navigation With Zero-Shot Ability

arXiv

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arXiv 2024年

作者： Lian, Shiwei Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

The generalization of the end-to-end deep reinforcement learning (DRL) for object-goal visual navigation is a longstanding challenge since object classes and placements vary in new test environments. Learning domain-independent visual representation is critical for enabling the trained DRL agent with the ability to generalize to unseen scenes and objects. In this letter, a target-directed attention network (TDANet) is proposed to learn the end-to-end object-goal visual navigation policy with zero-shot ability. TDANet features a novel target attention (TA) module that learns both the spatial and semantic relationships among objects to help TDANet focus on the most relevant observed objects to the target. With the Siamese architecture (SA) design, TDANet distinguishes the difference between the current and target states and generates the domain-independent visual representation. To evaluate the navigation performance of TDANet, extensive experiments are conducted in the AI2-THOR embodied AI environment. The simulation results demonstrate a strong generalization ability of TDANet to unseen scenes and target objects, with higher navigation success rate (SR) and success weighted by length (SPL) than other state-of-the-art models. TDANet is finally deployed on a wheeled robot in real scenes, demonstrating satisfactory generalization of TDANet to the real world. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Adaptive Integral Sliding Mode Control for Attitude Tracking of Underwater Robots With Large Range Pitch Variations in Confined Space

arXiv

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arXiv 2024年

作者： Wang, Xiaorui Sha, Zeyu Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

Underwater robots play a crucial role in exploring aquatic environments. The ability to flexibly adjust their attitudes is essential for underwater robots to effectively accomplish tasks in confined space. However, the highly coupled six degrees of freedom dynamics resulting from attitude changes and the complex turbulence within limited spatial areas present significant challenges. To address the problem of attitude control of underwater robots, this letter investigates large-range pitch angle tracking during station holding as well as simultaneous roll and yaw angle control to enable versatile attitude adjustments. Based on dynamic modeling, this letter proposes an adaptive integral sliding mode controller (AISMC) that integrates an integral module into traditional sliding mode control (SMC) and adaptively adjusts the switching gain for improved tracking accuracy, reduced chattering, and enhanced robustness. The stability of the closed-loop control system is established through Lyapunov analysis. Extensive experiments and comparison studies are conducted using a commercial remotely operated vehicle (ROV), the results of which demonstrate that AISMC achieves satisfactory performance in attitude tracking control in confined space with unknown disturbances, significantly outperforming both PID and SMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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A Multi-Modal Approach Based on Large Vision Model for Close-Range Underwater Target Localization

arXiv

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arXiv 2024年

作者： Yang, Mingyang Sha, Zeyu Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

Underwater target localization uses real-time sensory measurements to estimate the position of underwater objects of interest, providing critical feedback information for underwater robots in tasks such as obstacle avoidance, scientific exploration, and environmental monitoring. While acoustic sensing is the most acknowledged and commonly used method in underwater robots and possibly the only effective approach for long-range underwater target localization, such a sensing modality generally suffers from low resolution, high cost and high energy consumption, thus leading to a mediocre performance when applied to close-range underwater target localization. On the other hand, optical sensing has attracted increasing attention in the underwater robotics community for its advantages of high resolution and low cost, holding a great potential particularly in close-range underwater target localization. However, most existing studies in underwater optical sensing are restricted to specific types of targets, thus lacking generalization capabilities. In addition, these studies typically focus on the design of estimation algorithms and ignore the influence of illumination conditions on the sensing performance, thus hindering wider applications in the real world. To address the aforementioned issues, this paper proposes a novel target localization method that assimilates both optical and acoustic sensory measurements to estimate the 3D positions of close-range underwater targets. The proposed sensing method integrates a large vision model with unique acoustic-based model prompt design to process multi-modal sensor measurements, ensuring the generalizability and robustness of underwater target localization. A test platform with controllable illumination conditions is developed. Extensive experiments are conducted, the results of which validate the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Strain measurement

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