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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Liang, Lebin Rao, Haotian Shen, Guohao Wang, Can Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China South China Agricultural University College of Electronic Engineering College of Artificial Intelligence Guangzhou510642 China

ISBN: (纸本)9798350327182

This paper proposed a real-time Unmanned Aerial Vehicle (UAV) autonomous localization framework based on onboard sensors, including 2D Lidar, Stereo Camera and Inertial Measurement Unit (IMU), built on the open-source VINS-Fusion software to achieve real-time and drift-free UAV pose estimation. The proposed framework eliminates the motion distortion of Lidar during UAV motion by combining Visual-Inertial Odometry (VIO). It obtains global localization information of Lidar by PL-ICP algorithm based on a global point cloud map. Graph optimization method is used to fuse the local positional estimation and the global information from the Lidar, which eliminate the cumulative error caused by VIO. The feasibility of the method is verified by experiments and compared with the method of only running Visual-Inertial Navigation Systems (VINS), the framework improves the accuracy of localization. In addition, we have implemented the construction of octree maps of 3D environments through the framework of this system. © 2023 IEEE.

关键词： Optical radar

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Steel Defect Detection in Bridges Using Deep Encoder-Decoder Networks 13

Steel Defect Detection in Bridges Using Deep Encoder-Decoder...

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13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021

作者： Ahmed, Habib La, Hung Manh Advanced Robotics and Automation Laboratory Dept. of Computer Science and Engineering University of Nevada Reno RenoNV89527 United States

ISBN: (纸本)9781605956879

Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.

关键词： Embedded systems

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Robust Wheel Speed Control of Electric Vehicles with Respect to Backlash and Dead-time in Powertrains

Robust Wheel Speed Control of Electric Vehicles with Respect...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Yussuf Shakhin Ahmad Bala Alhassan Minh Cao Ta Binh-Minh Nguyen Ton Duc Do Dept. of Robotics and Mechatronics School of Engineering and Digital Science (SEDS) Nazarbayev University Astana Kazakhstan Dept. of Electrical Engineering and Computer Engineering University of Sherbrooke Sherbrooke Canada Dept. of Advanced Energy The University of Tokyo Tokyo Japan

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

The primary advantage of electric vehicles (EVs) lies in their rapid and precise torque generation. Consequently, the research and development of traction control systems that ensure safety, stability, and reliability in EVs are becoming increasingly popular. In this paper, an analysis of the wheel-speed control of an EV under various operating conditions is presented. A double-loop cascade disturbance observer (DOB) was designed to estimate the wheel speed from the estimation of the total disturbance acting on the vehicle. The influence of gear backlash, torque delay, air drag, and road inclination were analyzed to investigate the effectiveness of the control system. The preliminary Matlab simulation analysis demonstrated that the DOB-based control design was effective and robust to the effects of external disturbances.

关键词： Torque Uncertainty Simulation Velocity control Wheels Electric vehicles Stability analysis Disturbance observers Delays Standards

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Fractional order based RISE controller for autonomous aerial vehicle

Fractional order based RISE controller for autonomous aerial...

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Range Technology (ICORT), International Conference on

作者： Abhishek Dixit Pooja Agrawal Ajay Misra Aeronautical Engineer (Electronics) Indian Air Force Delhi India School of Robotics Defence Institute of Advanced Technology Pune India Department of Aerospace Engineering Defence Institute of Advanced Technology Pune India

In this work, fractional order based robust integral of the sign of the error controller (FRISE) is proposed to achieve longitudinal and lateral stability for fixed wing autonomous aerial vehicle (AAV). The AAV dynamics under consideration are affected because of external disturbances. A robust control strategy is essential for mitigating the effects of external disturbances. In this regard, the proposed FRISE controller is a viable approach. The proposed FRISE controller consists of self tuned gain. Here, the mentioned self tuned gain is modeled as an error function based on a fractional order. The stability analysis based on Lyapunov is done for the proposed FRISE controller. The simulation results provide a comparison between the proposed FRISE controller and the conventional sliding mode controller (SMC). The effectiveness of the FRISE controller is also validated with performance indices based on Integral Absolute Time Error (IATE). As a result, the robustness provided by the FRISE controller makes it more efficient for real-time applications.

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Negative correlation between cross-plane bonding strength and in-plane thermal transport in bent van der Waals materials

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Physical Review B 2024年第24期110卷 245411-245411页

作者： Shuo Qiao Yi Tao Lin Yang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing 100871 People's Republic of China School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments Southeast University Nanjing 210096 People's Republic of China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Peking University Beijing 100871 People's Republic of China

Strain engineering stands out as a promising avenue for manipulating the physicochemical characteristics of two-dimensional (2D) van der Waals (vdW) materials. Amidst the flourishing investigations into the modulation of electronic band structures, the influence of elastic strain on the evolution of phonon spectra and, consequently, thermal transport has garnered comparatively less attention. Yet, this facet offers a realm of intricate physics and engineering possibilities. Bending, a process that couples in-plane (IP) and cross-plane (CP) motion, provides a unique opportunity to investigate the influence of elastic anisotropy on the thermal properties of inhomogeneous strain-modulated 2D materials. Here we studied the effects of CP bonding on the IP thermal conductivities of a model layered material using molecular dynamics simulation. Notably, our findings unveiled an intriguing negative correlation between CP bonding strength and IP thermal conductivity while keeping IP bonding constant. This can be attributed to the heightened stress gradient spanning across multiple layers due to the pronounced CP bonding, which in turn, engenders a broadened phonon spectrum, and eventually stronger phonon scattering. Our work sheds light on the intricate interplay between CP bonding and thermal transport in bent vdW materials, offering insights extendable to the broader family of 2D materials.

关键词： Van der Waals forces

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Safety Protection Method of Rehabilitation Robot Based on fNIRS and RGB-D Information Fusion

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Journal of Shanghai Jiaotong university(Science) 2022年第1期27卷 45-54页

作者： LI Dong FAN Yulin LU Na CHEN Guodong WANG Zheng CHI Wenzheng School of Mechanical and Electrical Engineering/Jiangsu Provincial Key Laboratory of Advanced Robotics/Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow UniversitySuzhou 215123JiangsuChina Department of Gerontology Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghai 200233China Department of Instrument Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

In order to improve the safety protection performance of the rehabilitation robot,an active safety protection method is proposed in the rehabilitation *** oxyhemoglobin concentration information and RGB-D information are combined in this method,which aims to realize the comprehensive monitoring of the invasion target,the patient’s brain function movement state,and the joint angle in the rehabilitation *** main focus is to study the fusion method of the oxyhemoglobin concentration information and RGB-D information in the rehabilitation *** analysis of brain functional connectivity coefficient was used to distinguish the basic motion *** human skeleton recognition algorithm was used to realize the angle monitoring of the upper limb joint combined with the depth *** with speed and separation monitoring,the protection method of multi-information fusion is safer and more comprehensive for stroke *** building the active safety protection platform of the upper limb rehabilitation robot,the performance of the system in different safety states is tested,and the safety protection performance of the method in the upper limb rehabilitation scene is verified.

关键词： upper limb rehabilitation robot safety protection functional near-infrared spectroscopy(fNIRS) multi-information fusion

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In-hand manipulation of a circular dynamic object by soft fingertips without angle measurement

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Science China(Information Sciences) 2021年第5期64卷 193-208页

作者： R.GARCIA-RODRIGUEZ V.PARRA-VEGA Aeronautical Engineering Program and Postgraduate Program in Aerospacial Engineering Universidad Politecnica Metropolitana de Hidalgo Robotics and Advanced Manufacturing Department Research Center for Advanced Studies (CINVESTAV)

A multi-fingered robotic hand with curved fingertips enables contact re-positioning without reattaching at the expense of fingertip rolling. This rolling stands for a characteristic that facilitates dexterous manipulation but results in an algebraically complex dynamic model subject to such constraints. The hemispherical shape of fingertips allows a dexterous manipulation when controlling the tangent forces, which are essential to rotate object. However, the measurement of the object angle in practice requires tactile-optical sensing. In this paper, considering robotic fingers with curved soft tips, we propose a feedback control that ensures optimal dynamical grasping of a circular rigid object. It is shown that the collaboration of the contact forces, to get a minimum pose of internal forces, and the tangential forces, to induce the conditions for assuring the grasp closure, is necessary to get a skillful manipulation. In this case, the orientation control of a circular object to the desired angle while avoiding direct measurement of the object angle is presented. Stability conditions of the system are presented in the sense of stability-in-the-manifold. Finally, representative simulations are shown and discussed.

关键词： optimal grasping manipulation soft-fingertips circular object stability-in-the-manifold

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MSCKF-DVIO: Multi-State Constraint Kalman Filter based RGB-D Visual-Inertial Odometry with Spline Interpolation and Nonholonomic Constraint

MSCKF-DVIO: Multi-State Constraint Kalman Filter based RGB-D...

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International Conference on Ubiquitous Robots (UR)

作者： Kwangyik Jung Jaebong Song Samwoo Seong Hyun Myung Department of Autonomous Driving Software Development Twinny Yuseong-gu Republic of Korea Urban Robotics Lab School of Electrical Engineering Korea Advanced Institute of Science and Technology Republic of Korea

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

This study presents MSCKF-DVIO (Multi-State Constraint Kalman Filter - Depth-aided Visual Inertial Odom-etry) as an innovative approach to address the limitations of existing methods. MSCKF-DVIO leverages RGB-D images and low-cost IMU measurements to enhance the accuracy and efficiency of visual-inertial odometry systems. The proposed framework jointly optimizes RGB-D images and low-cost IMU measurements, enabling robust and precise state estimation. By reducing the number of state variables compared to the existing MSCKF-VIO, the efficiency of state augmentation and covariance computations is significantly enhanced. Notably, the prediction of the future state is achieved through the interpolation of past keyframe viewpoint poses from the image and short-time IMU integration. The proposed MSCKF-DVIO improves the accuracy of estimation by applying nonholonomic constraints (NHCs) of a differential-wheeled mobile robot and utilizing the zero velocity update (ZUPT) based on stationary state determination. The efficacy of MSCKF -DVIO is evaluated through the Absolute Pose Error (APE) using 3D LiDAR-based localization as the ground truth. The evaluation includes pose estimation outcomes and performance evaluations in comparison to other algorithms. The results demonstrate significant promise of MSCKF-DVIO for improving the performance and reliability of visual-inertial odometry systems.

关键词： Interpolation Visualization Accuracy Stationary state Filtering algorithms Information filters Real-time systems

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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Pose Quality Prediction for Vision Guided Robotic Shoulder Arthroplasty

Pose Quality Prediction for Vision Guided Robotic Shoulder A...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Morgan Windsor Jing Peng Ashish Gupta Peter Pivonka Michael J Milford QUT Centre for Robotics School of Electrical Engineering and Robotics at the Queensland University of Technology Queensland Unit for Advanced Shoulder Research and Greenslopes Private Hospital Brisbane QLD Australia QUT Centre for Biomedical Technologies School of Mechanical Medical and Process Engineering at the Queensland University of Technology

Surgical assistive robots offer the potential for drastically improved patient outcomes through more accurate, more repeatable surgical procedures like shoulder arthroplasty operations. Existing robotic systems typically rely on optical marker tracking and require invasive marker attachment for localization, complicating the surgical workflow and patient recovery. But moving towards a markerless system is very challenging, both because of the absolute difficulty and the large variation in localization conditions across thousands of surgical procedures. In this paper we propose an alternative approach: rather than try to create a “perfect” and fully generalizable markerless localization system, instead create a reliable and trustworthy localization system that is able to continually self-assess the likely quality of its localization esti-mates, and act accordingly. We propose a lightweight method for predicting vision-based pose estimation performance using internal pipeline artifacts (without needing external ground truth from a marker-based system). Using extensive real robot experiments with challenging actual imagery from surgery, we demonstrate our prediction system accurately self-characterizes the localization system's performance across a wide range of localization conditions, and demonstrate that this prediction system generalizes to a range of surgical conditions. We then show how online performance prediction can drive active robot navigation that minimizes localization error, reducing target pose estimation error by 96.1% for rotation and 96.7% for translation compared to rejected alternative trajectories.

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