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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shupei Li Chi Zhang Feng Yi Pingping Lv Ting Yuan Meitong Zhang Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Electrical Engineering Hebei University of Science & Technology Ningbo China

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5 th and 6 th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method.

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基于注意力机制的LSTM时变水声信道深度学习预测

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哈尔滨工程大学学报（英文版） 2023年第3期22卷 650-658页

作者：朱政亮童峰周跃海张梓峤张福民 National and Local Joint Engineering Research Center for Navigation and Location Service Technology Xiamen UniversityXiamen 361005China College of Ocean and Earth Sciences Xiamen UniversityXiamen 361005China School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlanta 30314USA Cheng Kar-Shun Robotics Institute Hong Kong University of Science and TechnologyHong Kong 00852China

This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention *** is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA *** attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM *** soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed *** performance of the proposed model is validated using different simulation time-varying UWA *** with the adaptive channel predictors and the plain LSTM model,the proposed model is better in terms of channel prediction accuracy.

关键词： Long short-term memory(LSTM) Attention mechanism Underwater acoustic communication Underwater acoustic channel Channel prediction

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Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving

arXiv

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arXiv 2023年

作者： Li, Siyu Yang, Kailun Shi, Hao Zhang, Jiaming Lin, Jiacheng Teng, Zhifeng Li, Zhiyong The School of Robotics Hunan University Changsha410082 China The State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom The School of Computer Science and Electronic Engineering Hunan University Changsha410082 China

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views into BEV directly with the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In addition, another stream of methods without prior knowledge can learn the transformation between front perspective views and BEV implicitly with a global view. Considering that the fusion of different learning methods may bring surprising beneficial effects, we propose a Bi-Mapper framework for top-down road-scene semantic understanding, which incorporates a global view and local prior knowledge. To enhance reliable interaction between them, an asynchronous mutual learning strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results on nuScenes and Cam2BEV datasets verify the consistent effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a real-world driving scenario. The source code is publicly available at BiMapper. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Precise segmentation of offshore farms in high-resolution SAR Images based on improved UNet++

Precise segmentation of offshore farms in high-resolution SA...

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2021 International Conference on computer Application and Information Security, ICCAIS 2021

作者： Yu, Chuang Liu, Yunpeng Xia, Xin Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510655218

The segmentation of the offshore farm area in the high-resolution SAR image is of great significance for the statistics of the farming area and the analysis of the rationality of the farming layout. However, the SAR images have the characteristics of a lot of noise and inconspicuous features. It is difficult to achieve precise segmentation by directly using non-learning image segmentation methods. Therefore, we propose a precise segmentation scheme for offshore farms in high-resolution SAR images based on improved UNet++. We first adopt a simulated annealing strategy for the update of the learning rate during the network training. By initializing the learning rate multiple times, we avoid the network from falling into a local optimum. Secondly, for the data set studied, we verify that the segmentation performance of resizing the image to 256×256 pixels is better than that of 512×512 pixels. Finally, we propose an improved UNet++, which uses SE-Net as the feature extraction network to enhance the feature learning ability. Extensive experimental results show that, compared to some state-of-the-art methods, the proposed scheme achieves superior performance with a Frequency Weighted Intersection over Union (FWIoU) of 0.9853 on the high-resolution SAR offshore farm data set. © The Authors.

关键词： Synthetic aperture radar

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Rearranging the Environment to Maximize Energy with a Robotic Circuit Drawing

Rearranging the Environment to Maximize Energy with a Roboti...

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IEEE International Conference on robotics and Biomimetics

作者： Xianglong Tan Zhikang Liu Chen Yu Sören Schwertfeger Andre Rosendo Hamlyn Centre Imperial College London UK Department of Computer Science University of York UK School of Information Science and Technology ShanghaiTech University China Robotics Engineering Worcester Polytechnic Institute USA

ISBN: (纸本)9781665481106

The ability of acquiring energy form environments is greatly beneficial for robot to function in uncertain environments. In this work, we present a robot capable of drawing circuits with conductive ink while also rearranging the visual world to receive maximum energy from a power source. A range of circuit drawing tasks is designed to simulate real-world scenarios, including avoiding physical obstacles and regions that would discontinue drawn circuits. We adopt the state-of-the-art Transporter networks for pick-and-place manipulation from visual observation. We conduct experiments in both simulation and real-world settings, and our results show that, with a small number of demonstrations, the robot learns to rearrange the placement of objects (removing obstacles and bridging areas unsuitable for drawing) and to connect a power source with a minimum amount of conductive ink. As autonomous robots become more and more involved in our daily lives, our proposed approach brings a novel way for machines to keep themselves functional by rearranging their surroundings to create their own electric circuits.

关键词： Training Visualization Power supplies Planets Surface resistance Ink Machine learning

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A Multi-model Fusion of LiDAR-inertial Odometry via Localization and Mapping

A Multi-model Fusion of LiDAR-inertial Odometry via Localiza...

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

作者： An Duy Nguyen Chuong Phuoc Le Pratik Walunj Anton Netchaev Thanh Nho Do Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA Graduate School of Biomedical Engineering UNSW Sydney Kensington Campus NSW Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub 4 for further exploration.

关键词： Location awareness Technological innovation Laser radar Accuracy Smoothing methods Computational modeling Feature extraction Odometry Kalman filters Testing

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Cross-Image Relational Knowledge Distillation for Semantic Segmentation

arXiv

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arXiv 2022年

作者： Yang, Chuanguang Zhou, Helong An, Zhulin Jiang, Xue Xu, Yongjun Zhang, Qian Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Horizon Robotics School of Computer Science Wuhan University China

Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples. However, they ignore the global semantic relations among pixels across various images that are valuable for KD. This paper proposes a novel Cross-Image Relational KD (CIRKD), which focuses on transferring structured pixel-to-pixel and pixel-to-region relations among the whole images. The motivation is that a good teacher network could construct a well-structured feature space in terms of global pixel dependencies. CIRKD makes the student mimic better structured semantic relations from the teacher, thus improving the segmentation performance. Experimental results over Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our proposed approach against state-of-the-art distillation methods. The code is available at https://***/winycg/CIRKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11189-11201页

作者： Zhijun Zhang Zhongwen Cao Xingru Li School of Automation Science and Engineering South China University of Technology Guangzhou China Key Library of Autonomous Systems and Network Control Ministry of Education Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China Jiangxi Thousand Talents Plan Nanchang University Nanchang China College of Computer Science and Engineering Jishou University Jishou China Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Laboratory) Guangzhou China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong China School of Information Science and Engineering Changsha Normal University Changsha China Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming China

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

关键词： Manipulators Robots Fault tolerant systems Fault tolerance End effectors Planning Kinematics Manipulator dynamics Jacobian matrices Collaboration

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NeuralGF: unsupervised point normal estimation by learning neural gradient function 23

NeuralGF: unsupervised point normal estimation by learning n...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qing Li Huifang Feng Kanle Shi Yue Gao Yi Fang Yu-Shen Liu Zhizhong Han School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Department of Computer Science Wayne State University Detroit

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision. However, normal supervision in benchmarks comes from synthetic shapes and is usually not available from real scans, thereby limiting the learned priors of these methods. In addition, normal orientation consistency across shapes remains difficult to achieve without a separate postprocessing procedure. To resolve these issues, we propose a novel method for estimating oriented normals directly from point clouds without using ground truth normals as supervision. We achieve this by introducing a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds and yield unit-norm gradients at the points. Specifically, we introduce loss functions to facilitate query points to iteratively reach the moving targets and aggregate onto the approximated surface, thereby learning a global surface representation of the data. Meanwhile, we incorporate gradients into the surface approximation to measure the minimum signed deviation of queries, resulting in a consistent gradient field associated with the surface. These techniques lead to our deep unsupervised oriented normal estimator that is robust to noise, outliers and density variations. Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks than the latest methods. The source code and pre-trained model are available at https://***/LeoQLi/NeuralGF.

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Shape Sensing of a Soft Robot Using Fiber Bragg Gratings for Single-port Transvesical Radical Prostatectomy

Shape Sensing of a Soft Robot Using Fiber Bragg Gratings for...

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IEEE International Conference on robotics and Biomimetics

作者： Zefeng Liu Ru Li Yongfeng Cao Fan Feng Le Xie School of Materials Science and Engineering and the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Materials Sci-ence and Engineering and the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Mathematics and Computer Science Quanzhou Normal University Quanzhou China Institute of Medical Robotics Institute of Forming Technology & Equipment Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

Robot-assisted laparoscopic radical prostatectomy is a common treatment for prostate cancer. Traditional rigid instruments, on the other hand, have operating blind areas and lack real-time position and shape feedback, which may result in additional trauma during the surgery. To this end, this work describes the development of a soft robot for single-port transvesical radical prostatectomy (STRP) using a multicore fiber with Fiber Bragg Gratings (FBGs). The shape sensing method is proposed in the meanwhile to rebuild the soft robot's shape from the wavelength data of FBGs. The experiments are carried out to confirm that the suggested strategy is workable.

关键词： Shape Multicore processing Surgery Soft robotics Reconstruction algorithms Robot sensing systems Fiber gratings

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