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74th International Astronautical Congress, IAC 2023

作者： Dinkel, Holly Di, Julia Santos, Jamie Albee, Keenan Borges, Paulo Moreira, Marina Alexandrov, Oleg Coltin, Brian Smith, Trey Department of Aerospace Engineering The University of Illinois Urbana-Champaign UrbanaIL United States Department of Mechanical Engineering Stanford University StanfordCA United States Department of Physics Chalmers University of Technology Gothenburg Sweden Maritime and Multi-Agent Autonomy Group The Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Robotics and Autonomous Systems Group CSIRO Data61 BrisbaneQLD Australia Intelligent Robotics Group The NASA Ames Research Center Moffett FieldCA United States

Assistive free-flyer robots autonomously caring for future crewed outposts-such as NASA's Astrobee robots on the International Space Station (ISS)-must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： 3D Reconstruction Artemis Change Detection Mapping Microgravity robotics

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Changed Extended State Observer based Position Control for Autonomous Underwater Vehicle

Changed Extended State Observer based Position Control for A...

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Data Driven Control and Learning systems (DDCLS)

作者： Wanping Song Chen Zheng Zengqiang Chen Mingwei Sun Qinglin Sun College of Artificial Intelligence Nankai University Tianjin China Beijing Institute of Astronautical Systems Engineering Beijing China Key Laboratory of Intelligent Robotics of Tianjin Tianjin China

Extended state observer (ESO) has been used to estimate the unknown nonlinear function of the system using input and output data. Recently, a new observer, the compensation function observer (CFO), has a significant improvement over ESO in estimating accuracy. This paper refers to the theory of CFO of fully using the available information and the innovation of the new definition of disturbance, designs a new observer, and verifies the availability of the new observer on the depth control simulation of the autonomous underwater vehicle (AUV). The simulation results demonstrate that the new observer is capable of estimating the unknown nonlinear function more quickly and accurately.

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IIPC: Intra-Inter Patch Correlations for Garment Collision Handling

IIPC: Intra-Inter Patch Correlations for Garment Collision H...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ruisheng Yuan Minzhe Tang Dongliang Kou Mingyang Sun Dingkang Yang Xiao Zhao Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Ministry of Education Engineering Research Center of AI and Robotics Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Realistic garment simulation is critical for digital humans. However, noticeable penetrations still exist in current learning-based garment simulation techniques. To reduce penetrations in predicted garments, we resort to the garment geometry and neural Signed Distance Fields (SDFs) for effective collision handling. The key idea of our method is that we divide the garment into patches and model the local and global garment geometry through Intra- and Inter-Patch Correlations (IIPC), which can be easily learned through the powerful context-understanding ability of Transformers. The geometry information is then utilized to predict a per-vertex moving offset, according to which we move the penetrating vertices along the SDF’s gradient directions to solve collisions. Our module can be coupled with learning-based backbones to effectively solve penetrations while retaining real-time performance. Extensive experiments show that the proposed method excels the prior works significantly.

关键词： Geometry Correlation Art Clothing Predictive models Transformers Real-time systems Digital humans Context modeling

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IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using Wheel-Encoder and Inertial-RGB-Depth Measurements

IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using ...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yuanhao Liu Wei Liang Chu Wang Xin Zhang Huanbing Gao Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences School of Information and Electrical Engineering Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Safe & smart move (SSM) aims to help industrial vehicular robot (IVR), such as warehousing robots, running safely and smartly by detecting the obstacles in front and maintaining a safe distance & attitude. However, the traditional SSM methods suffer the drawbacks of limited ranges, inaccurate regression of safety distance & orientation between the robot and the forward unexpected obstacles, and the inappropriate IVR control strategies. To solve these issues, this paper designs an innovative real-time safety-aware framework using the complementary Wheel-Encoder and Inertial-RGB-Depth measurements to ensure the IVR running safely, reasonably and reliably. To estimate the robot running states, we take advantages of the complementary wheel encoder, monocular RGB images and inertial measurements to obtain the robot positions, orientations, angular and linear velocities in a tightly-coupled manner. To improve the safety relative distance & attitude estimation accuracy, we exploit the RGB and depth measurements in a task-oriented fashion. The forward obstacles with the specific contours are detected using the Mask-RCNN model, followed by the Gaussian Mixture Model to obtain the forward obstacle depth. Eventually, an IVR control strategy is designed to maintain the robot safety distance and orientation, and to keep the minimum damage to the robot mechanical system. The presented method has been evaluated on the developed industrial vehicular robot safety perception platform. The experimental results prove the competitiveness against the state-of-the-art methods.

关键词： Service robots Warehousing Robot vision systems Wheels Reliability engineering Safety Mobile robots Mechanical systems Velocity measurement Gaussian mixture model

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Exploring Neural Networks for Forward Kinematics of the Robotic Arm with Different Length Configurations: A Comparative Analysis

Exploring Neural Networks for Forward Kinematics of the Robo...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia

Our work investigates the utilization of Artificial Neural Networks (ANNs) to address the complexities associated with Forward Kinematics (FK) problems within the field of robotics. We undertake an extensive comparative analysis to assess how ANNs perform under different circumstances with robotic arms of varying lengths and impact the overall system’s functionality. The training, testing, and validation of ANNs are carried out using MATLAB for a simulated 2-DoF serial robotic arm involving three distinct datasets: fixed step size, random step size, and sinusoidal step size. Three training optimizers, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Stochastic Conjugate Gradient (SCG), are considered within the ANN architecture. Based on Mean Square Error (MSE) values, the numerical findings reveal the potential of ANN in estimating forward kinematic solutions of complex robotic manipulators with different arm lengths and reducing computational complexity.

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A Track-based Colon Endoscopic Robot with Depth Perception Stereo Cameras for Haustral Fold Detection during Colonic Navigation

A Track-based Colon Endoscopic Robot with Depth Perception S...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shujing He Yujie Zhang Baoyi Huang Jie Lin Chaoyang Shi Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Guangzhou University of Chinese Medicine Shenzhen China Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Colon endoscopic robots represent a promising screening modality for the visualization of colon cancers with high sensitivity. However, current colonoscopy robots are often characterized by intricate and bulky mechanical structures, which pose practical challenges when moving through the complex and narrow environment of the colon. Moreover, these robots are typically equipped with a single camera, limiting their ability to accurately estimate the depth of haustral folds in the colon, which is of great importance for the active colonic navigation of the robots. To address these challenges, we develop a track-based stereoscopic endoscopic robot (TSER) which is equipped with four tracks positioned at the corners of its body. This innovative design maximizes the contact between the tracks and the colon wall, enhancing maneuverability. The tracks are constructed from de-molded polydimethylsiloxane (PDMS) and incorporate micro-patterns on their outer surfaces. We have proposed a straightforward strategy for detecting haustral folds using TSER’s stereo camera, which allows for precise identification of their position and depth. The TSER achieves an average motion speed of 9.8 mm/s in a bellows tube that contains silicone oil and a speed of 5.2 mm/s in an exvivo porcine intestinal segment. Impressively, the TSER boasts an 88.11% accuracy rate in haustral fold depth estimation, surpassing the performance of existing geometric shape fitting methods. These results demonstrate that the TSER holds great potential for effective and efficient movement and inspection within the colon, offering a promising solution for improved colon cancer screening.

关键词： Visualization Sensitivity Tracking Navigation Shape Stereo image processing Oils

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A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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Real-Time Locomotion Transitions Detection: Maximizing Performances with Minimal Resources

Real-Time Locomotion Transitions Detection: Maximizing Perfo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zeynep Özge Orhan Andrea Dal Prete Anastasia Bolotnikova Marta Gandolla Auke Ijspeert Mohamed Bouri REHAssist Group Ecole Polytechnique Federale de Lausanne (EPFL) BioRobotics Laboratory (BioRob) Ecole Polytechnique Federale de Lausanne (EPFL) Department of Mechanical Engineering Politecnico di Milano Laboratory for Analysis and Architecture of Systems (LAAS) Robotics and InteractionS Team

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.

关键词： Accuracy Exoskeletons Automata Machine learning Wearable robots Real-time systems Robustness

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T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

arXiv

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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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Facial Emotion Recognition in Smart Education systems: A Review

Facial Emotion Recognition in Smart Education Systems: A Rev...

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IEEE International Smart Cities Conference (ISC2)

作者： Haleem Farman Admed Sedik Moustafa M. Nasralla Maged Abdullah Esmail Department of Communications and Networks Engineering Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of The Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt

Facial Emotion Recognition (FER) plays a pivotal role in the realm of Smart Education systems (SES), catering to the growing need for personalized and adaptive learning experiences. This paper presents a comprehensive review of the current state-of-the-art techniques and methodologies employed in FER in general and within the context of SES in particular. The primary objective of this review is to explore the FER technology for the enhancement of educational environments. The review begins by providing an insightful overview of the significance of emotions in education, leveraging deep learning approaches to elucidate the impact of emotional states on cognitive performance and academic outcomes. Subsequently, it explores the key components and working principles of FER systems, encompassing the extraction, representation, and classification of facial emotions. Furthermore, this paper highlights the updated literature in the field of FER applied to SES, ranging from real-time emotion analysis during classroom interactions to adapting instructional content based on learners' emotional states. Furthermore, the review critically analyzes the existing facial emotion recognition datasets utilized in facial emotion recognition research. The dataset is subcategorized in to two parts, images and videos, providing readers with convenience in understanding the various data sources employed in FER model training and evaluation.

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