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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Jiang, Chao Wang, Zhiling Liang, Huawei Zhang, Shijing Tan, Shuhang Hefei Institutes of Physical Science Chinese Academy of Sciences HeFei230031 China University of Science and Technology of China HeFei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application HeFei230061 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences HeFei230061 China

ISBN: (纸本)9789811694912

Detecting or estimating the state of the moving target accurately on the road helps predict the behavior of the moving target, and this is beneficial for self-vehicles’ trajectory planning and safe navigation. The difficulty of state estimation lies in how to estimate the state of the target in real-time and accurately, and it will not be divergence when the sensor has missed detection, false detection and time-varying measurement noise. In response to this difficulty, it is proposed to use multi-source information fusion and Kalman filter to make up for the missed detection of the sensor, use the nearest neighbor correlation algorithm to match the detection and prediction to reduce the impact of false detection, and use the adaptive measurement noise estimation method to estimate the current value of the measurement noise in real-time to improve the stability and accuracy of the filter when the measurement noise is time-varying. The proposed method relies on the smart car platform for experiments. The experimental results show that the position and velocity estimation accuracy of the proposed algorithm are all better than the comparison method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spurious signal noise

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Cooperative Route Planning for Fuel-constrained UGV-UAV Exploration

Cooperative Route Planning for Fuel-constrained UGV-UAV Expl...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Zhang, Mingjia Liang, Huawei Zhou, PengFei Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Nanjing Polytechnic Institute Nanjing China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Heterogeneous UGV-UAV system can efficiently complete large-scale tasks such as exploration and rescue by collaborative operation. This paper proposes a route planner based on a dynamic aggregation strategy to select the time-optimal routes for function-constrained UGV and fuel-constrained UAV to reach all the mission points. To reduce the time consumption of waiting at the take-off point, UGV and UAV are inclined to rendezvous at the next meeting point by no-wait operations. We present a two-step routing strategy to address this cooperative problem. First, the Voronoi diagram and greedy set cover method are used to determine meeting points and task areas. Then, solving a traveling salesman problem to find a UGV path by A-star and Lin-Kernighan heuristic. After that, an adaptive large neighborhood search heuristic is proposed to address vehicle routing problem with recharge for UAV among multiple task areas. The simulation and real experiment results demonstrate that our approach can effectively generate a time-optimal path for UGV-UAV Cooperative exploration. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Phase Error Correction Algorithm Based on Complementary Gray Code and Reverse Error Compensation 19th

Phase Error Correction Algorithm Based on Complementary Gray...

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19th Chinese Conference on Image and Graphics Technologies and applications, IGTA 2024

作者： Zhang, Pengjie Kong, Bin Wang, Shaoping Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Research Center for Intelligent Driving Technology and Application Hefei230088 China

ISBN: (纸本)9789819799183

This study addresses the periodic phase error problem arising from the nonlinear response of instruments in structured light three-dimensional measurement systems. A novel phase error correction algorithm that integrates complementary Gray codes and reverse error compensation is proposed. The algorithm utilizes complementary Gray codes to assist in phase unwrapping, thereby reducing computational resources and improving the accuracy of the unwrapping process. Furthermore, effective phase error compensation is achieved through two measurements and their phase averaging, significantly enhancing the system's measurement accuracy. Simulation experiments are conducted to analyze the specific impact of nonlinear response on phase measurement, and theoretical derivations validate the effectiveness of the reverse compensation method in eliminating periodic phase errors. The experimental results demonstrate that, compared to traditional methods, the proposed algorithm improves measurement accuracy by more than 80%, confirming its efficacy. This research offers a new solution for enhancing structured light measurement systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Phase measurement

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A strained π-extendedcycloparaphenylene carbon nanoring

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Chinese Chemical Letters 2024年第5期35卷 171-174页

作者： Kang Wei Jiayu Li Wen Zhang Bing Yuan Ming-De Li Pingwu Du Key Laboratory of Precision and Intelligent Chemistry Anhui Laboratory of Advanced Photon Science and TechnologyDepartment of Materials Science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province Shantou UniversityShantou 515063China

Herein,we report the facile synthesis of a highly strained hexabenzocoronene-containing carbon nanoring,cyclo[4]-paraphenylene[2]-2,11-hexabenzocoronenylene([4,2]CPHBC),as the segment of a[10,10]single-walled carbon nanotube([10,10]SWNT).[4,2]CPHBC was synthesized based on the platinummediated assembly of diborylbiphenyl and diborylhexabenzocoronene,forming a tetranuclear platinum complex,followed by reductive *** nanoring molecule was confirmed by NMR and HRMS,and its photophysical properties were studied using steady-state and time-resolved ***,the selective supramolecular host-guest interaction between[4,2]CPHBC and C_(60) was also investigated.

关键词： [10]CPP Hexabenzocoronene Fullerene Supramolecular chemistry

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Drivable Area Segmentation in Unstructured Roads for Autonomous Vehicles based on Multi-sensor Fusion 22

Drivable Area Segmentation in Unstructured Roads for Autonom...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Hanqi Wang Huawei Liang Liangji Chen Diancheng Gong Pengfei Zhou Bin Kong Hefei Institutes of Physical Science Chinese Academy of Sciences China and University of Science and Technology of China China Hefei Institutes of Physical Science Chinese Academy of Sciences China and Anhui Engineering Laboratory for Intelligent Driving Technology and Application China

ISBN: (纸本)9781450397056

Drivable area segmentation is vital for autonomous vehicle driving safety, especially on unstructured roads. Mainstream drivable area algorithms are suited for structured environments, such as urban roads. However, these algorithms perform poorly in unstructured environments. This paper proposes a drivable area segmentation algorithm based on multi-sensor late-fusion for unstructured environments. The algorithm uses the visual segmentation results to correct the light detection and ranging (LiDAR) segmentation results, which can effectively solve those environments with unapparent boundary height differences. Desert experiments show that our algorithm achieves 96.02 on Intersection over Union (IoU), which is 36.75 and 38.31 higher than the LiDAR-based and the Vision-based algorithm, respectively.

关键词： autonomous vehicles unstructured roads multi-sensor fusion drivable area segmentation

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Multiscale Space Adaptation-Based Dynamic Object Removal for LiDAR Mapping

Multiscale Space Adaptation-Based Dynamic Object Removal for...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Haitao Xu Hanqi Wang Xueshi Zhang Pengfei Zhou Huawei Liang Institutes of Physical Science and Information Technology Anhui University Hefei China Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China Chinese Academy of Sciences Innovation Research Institute of Robotics and Intelligent Manufacturing Hefei China

Simultaneous Localization And Mapping (SLAM) is a crucial technology for mobile robots to locate themselves when working in a location environment. However, when a large number of dynamic objects appear in the environment, the positioning and mapping results of the robot will be affected. At present, online real-time removal algorithms can effectively solve such problems. Therefore, this paper proposes a dynamic object removal algorithm based on multi-scale spatial information, which identifies and eliminates moving objects in the environment during self-localization and environmental modeling. In this paper, the Inertial Measurement Unit (IMU) data is used to pre-liminarily judge the robot's position and attitude transformation, and the dynamic target distance is combined to adjust the target feature extraction scale, and modify the dynamic object detection conditions to screen objects with high confidence. To remove misjudgment information caused by irregular objects in the environment, road information is used to remove misjudgment information and obtain dynamic object coordinate information in the environment. Through quantitative and qualitative experiments, it has been proven that this algorithm can improve the positioning accuracy of robots in urban road scenes and optimize environmental models. This algorithm reduces Absolute Pose Error by 0.11032m.

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Robust Curb Detection Based on the Accessible Route Analysis and Key Frames Prediction

Robust Curb Detection Based on the Accessible Route Analysis...

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Yu, Zehai Zhu, Hui Lin, Linglong Yang, Haozhe Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Anhui Hefei230031 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Anhui Hefei230031 China

ISBN: (纸本)9780738110837

For autonomous driving in urban environments, road curb plays a significant role in tasks such as lane-keeping, assisted localization, and path planning. A real-time robust curb detection algorithm based on 3D LiDAR is proposed in this paper. Firstly, the iterative beam model is applied to get the accessible route of the road to obtain the starting point of the search step for each scan line. Secondly, the candidate curb points are extracted according to the spatial distribution characteristics of the point cloud. To effectively combine the historical boundaries information, a Bayesian filter is used to track the road width to reduce the false detection of curb points when the boundaries are interrupted, or on-road obstacles appear. The proposed algorithm is tested in different road environments. The experimental results show that our method has strong scene adaptability. The detection accuracy is over 90%, and the average runtime is 34.62 ms. © 2021 IEEE.

关键词： Autonomous vehicles

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An Improved Feature-Based Visual Slam Using Semantic Information

An Improved Feature-Based Visual Slam Using Semantic Informa...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Songyu Ma Huawei Liang Hanqi Wang Tiejuan Xu Anhui Jianzhu University Hefei China Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China Chinese Academy of Sciences Innovation Research Institute of Robotics and Intelligent Manufacturing Hefei China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China University of Science and Technology of China Hefei China

The traditional visual slam method for feature extraction is single and has poor robustness. This paper proposes an improved feature-based SLAM (Simultaneous localization and mapping) by adding weights to the features of objects matching the same semantic category and incorporating semantic information into loop closure detection. The basic idea is to use the deep neural network YOLOV5 to classify things, associate feature points with objects appearing in the bounding box, and thus assign the feature points to the semantic labels of these objects. In the feature matching process of the SLAM algorithm, the matching of feature points with the same semantic label will be weight, which increases the matching of similar features on the same category of objects, and at the same time intefrates semantic information into loop detection to improve the accuracy of loop closure detection. The test results show that the absolute trajectory error of the improved algorithm is more minor, the pose estimation accuracy is higher, and the tracking robustness can be effectively improved.

关键词： Training Visualization Simultaneous localization and mapping Semantics Pose estimation Neural networks Object detection

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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

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Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Obstacle-surmounting Analysis of a Novel Deformable Wheel-track Composite Walking Platform

Obstacle-surmounting Analysis of a Novel Deformable Wheel-tr...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Wang Kun Liang Huawei Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China University of Science and Technology of China Hefei China Hefei China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China Chinese Academy of Sciences Innovation Research Institute of Robotics and Intelligent Manufacturing Hefei China Hefei China

Based on high mobility requirements of unmanned ground platforms in various environments and combining the advantages of traditional wheeled, crawler and legged walking mechanisms, a novel deformable wheel-track composite walking mechanism with multi-movement modes is proposed,obtaining better obstacle-surmounting performances than the famous reconfigurable wheel-track mechanism(RWT type) in recent years. The walking mechanism has two stable forms of round wheel configuration and prolate polygon configuration with three modes of wheel rolling, track movement and gait rotation, which are respectively suitable for travelling on hard roads at high speeds, moving on soft earth with low contact pressure and crossing obstacles. Design of the innovative structure and a platform equipped with the novel machines is carried out. Subsequently, the platform's obstacle surmounting performance is analyzed through dynamic modelling and calculation. Ultimately to verify the advantages of the machine and its design rationality, comparative simulation experiments of obstacle-surmounting performances is implemented in the multi-body simulation environment,setting up obstacle models and building experimental platform models integrating this machine and the RWT type machine respectively.

关键词： Legged locomotion Deformable models Analytical models Adaptation models Tracking Dynamics Crawlers

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