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第32届中国过程控制会议(CPCC2021)

作者： Shuai Li Xiaofeng Zhou Haibo Shi Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences

With the increasing scale and complexity,how to analyze the hybrid characteristics including multimode,nonGaussianity and nonlinearity is one of the most difficult problems faced by multimode industrial process *** paper proposes a monitoring method using hierarchical mode division with serial independent component analysis for multimode processes with *** the multimodal uncertainty and transition mode,hierarchical mode information with transitions is first obtained by mode division using hierarchical density peaks ***,by integrating independent component analysis(ICA) and kernel independent component analysis(KICA) using the serial structure,a hybrid non-Gaussian and nonlinear process modeling method using serial independent component analysis is used for hierarchical process ***,the stable and transition mode identification using geodesic distance is developed for multimode industrial process *** monitoring results for the penicillin fermentation process show the feasibility and effectiveness.

关键词： Process monitoring multimode industrial process transition mode hierarchical mode division serial independent component analysis

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NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping

arXiv

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arXiv 2023年

作者： Yu, Xuan Liu, Yili Mao, Sitong Zhou, Shunbo Xiong, Rong Liao, Yiyi Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China

LiDAR Mapping has been a long-standing problem in robotics. Recent progress in neural implicit representation has brought new opportunities to robotic mapping. In this paper, we propose the multi-volume neural feature fields, called NF-Atlas, which bridge the neural feature volumes with pose graph optimization. By regarding the neural feature volume as pose graph nodes and the relative pose between volumes as pose graph edges, the entire neural feature field becomes both locally rigid and globally elastic. Locally, the neural feature volume employs a sparse feature Octree and a small MLP to encode the signed distance function (SDF) of the submap with an option of semantics. Learning the map using this structure allows for end-to-end solving of maximum a posteriori (MAP) based probabilistic mapping. Globally, the map is built volume by volume independently, avoiding catastrophic forgetting when mapping incrementally. Furthermore, when a loop closure occurs, with the elastic pose graph based representation, only updating the origin of neural volumes is required without remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to the sparsity and the optimization based formulation, NF-Atlas shows competitive performance in terms of accuracy, efficiency and memory usage on both simulation and real-world datasets. The project page is: https://***/NFAtlas/ Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

arXiv

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arXiv 2022年

作者： Zhang, Zhuqing Song, Yang Huang, Shoudong Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of the map information, the proposed invariant EKF can naturally maintain the correct observability properties of the system. To consider the uncertainty of the map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of the map information can be taken into consideration to avoid over-confident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of the map information. Finally, we validate our proposed system’s high consistency, accuracy, and efficiency via extensive simulations and real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Lie groups

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Joint Task Offloading and Uav Trajectory Optimization in Dynamic Edge Computing systems: A Deep Reinforcement Learning Approach

SSRN

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SSRN 2025年

作者： Wang, Yitian Wang, Hui Ding, Jingfang Yu, Haibin School of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

This paper addresses latency challenges in cloud-based task offloading caused by geographical server-user disparities by integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC). We propose a dynamic UAV-assisted framework that optimizes real-time parameter adjustments to minimize system latency. key challenges include joint task offloading, UAV trajectory optimization, and User Equipment (UE) occlusion detection under mobility constraints. To resolve these, we transform the problem into a Markov Decision Process (MDP) and develop an enhanced Adaptive Delayed Deep Deterministic Policy Gradient (AD3PG) algorithm, which improves upon DDPG by incorporating delayed updates and neural network tuning. The algorithm dynamically optimizes three critical aspects: UAV-UE connectivity establishment, occlusion-aware dual noise power configuration, and adaptive task offloading ratios. Extensive simulations demonstrate AD3PG’s superiority over baseline methods such as DDPG and Twin-Delayed DDPG (TD3) in reducing total system latency by 13.2–35.3% under dynamic scenarios. These results validate the framework’s efficacy for latency-sensitive applications in UAV-MEC systems. © 2025, The Authors. All rights reserved.

关键词： Drones

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PanoVpr: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows Across the Panoramic View

PanoVpr: Towards Unified Perspective-to-Equirectangular Visu...

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International Conference on Intelligent Transportation

作者： Ze Shi Hao Shi Kailun Yang Zhe Yin Yining Lin Kaiwei Wang State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China Shanghai SUPREMIND Technology Co. Ltd Shanghai China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose PanoVPR, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at PanoVPR.

关键词：

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Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers

arXiv

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arXiv 2023年

作者： Jiang, Qi Gao, Shaohua Gao, Yao Yang, Kailun Yi, Zhonghua Shi, Hao Sun, Lei Wang, Kaiwei The State Key Laboratory of Modern Optical Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

High-quality panoramic images with a Field of View (FoV) of 360° are essential for contemporary panoramic computer vision tasks. However, conventional imaging systems come with sophisticated lens designs and heavy optical components. This disqualifies their usage in many mobile and wearable applications where thin and portable, minimalist imaging systems are desired. In this paper, we propose a Panoramic Computational Imaging Engine (PCIE) to achieve minimalist and high-quality panoramic imaging. With less than three spherical lenses, a Minimalist Panoramic Imaging Prototype (MPIP) is constructed based on the design of the Panoramic Annular Lens (PAL), but with low-quality imaging results due to aberrations and small image plane size. We propose two pipelines, i.e. Aberration Correction (AC) and Super-Resolution and Aberration Correction (SR&AC), to solve the image quality problems of MPIP, with imaging sensors of small and large pixel size, respectively. To leverage the prior information of the optical system, we propose a Point Spread Function (PSF) representation method to produce a PSF map as an additional modality. A PSF-aware Aberration-image Recovery Transformer (PART) is designed as a universal network for the two pipelines, in which the self-attention calculation and feature extraction are guided by the PSF map. We train PART on synthetic image pairs from simulation and put forward the PALHQ dataset to fill the gap of real-world high-quality PAL images for low-level vision. A comprehensive variety of experiments on synthetic and real-world benchmarks demonstrates the impressive imaging results of PCIE and the effectiveness of the PSF representation. We further deliver heuristic experimental findings for minimalist and high-quality panoramic imaging, in terms of the choices of prototype and pipeline, network architecture, training strategies, and dataset construction. Our dataset and code will be available at https://***/zju-jiangqi/PCIE-PART. Copyrig

关键词： Optical transfer function

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Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning 21

Benchmarking Lane-changing Decision-making for Deep Reinforc...

引用

Proceedings of the 7th International Conference on robotics and Artificial Intelligence

作者： Junjie Wang Qichao Zhang Dongbin Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences China and College of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450385855

The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. Then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. The designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task1 .

关键词： Autonomous driving Reinforcement learning Lane change scenarios

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Research on Coupled Task Allocation and Scheduling of Multi-type Robots

Research on Coupled Task Allocation and Scheduling of Multi-...

引用

IEEE International Conference on Networking, Sensing and control

作者： Xingkai Wang Zichao Xing Weimin Wu Xinyu Chen State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665440493

Task allocation problem and scheduling problem have always been hot topics in the robotics research field. Both of them can be divided into many categories. Among all these categories, coupled task allocation and scheduling problem (CTASP) is a quite difficult problem, especially for of multi-type robots. However, few researches are conducted on this area. Usually, the CTASP is studied for multi-type robots with fixed execution sequence in the reported literature. However, this paper will study the CTASP for multi-type robots with flexible execution sequence. A two-dimension genetic algorithm (TDGA) is designed to solve this type of problem, combined with rank minimal heuristic (RMH) algorithm to provide an elite strategy. The simulation experiment proves that TDGA can solve this kind of problems efficiently, and the efficiency of hybrid genetic algorithm has been further improved.

关键词： Heuristic algorithms Conferences Robot sensing systems Scheduling Sensors Resource management Task analysis

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Hierarchical Classification of Grasp Motions using EMG signals

Hierarchical Classification of Grasp Motions using EMG signa...

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

作者： Wei Xu Xu Shi Xinjun Sheng Xiangyang Zhu State Key Laboratory of Mechanical Systems and Vibrations Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665431545

Upper-limb myoelectrical prosthesis provides an intuitive interface for prosthesis users to restore an acceptable level of grasping function. However, the existing EMG classifi-cation algorithms are still incapable of decoding various human grasping gestures that are important to control dexterous multi-finger hands fully. Here, we present a novel method to interpret sophisticated grasping motions, using a combination of unsupervised clustering with non-mandatory leaf-node prediction (NMLNP) hierarchical classification. Results show that the optimized subset of gestures specific to the individual selected by clustering leads to higher classification accuracy. In addition, hierarchical classification can achieve better performance than flat classifiers and provide an adaptive interface that automatically outputs a larger set of gestures for subjects with better EMG quality.

关键词： Mechatronics Machine vision Taxonomy Grasping Prediction algorithms Electromyography Classification algorithms

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