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arXiv 2024年

作者： Guo, Ziang Perminov, Stepan Konenkov, Mikhail Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology Moscow Russia LLC IntegraNT Moscow Russia

Many established vision perception systems for autonomous driving scenarios ignore the influence of light conditions, one of the key elements for driving safety. To address this problem, we present HawkDrive, a novel perception system with hardware and software solutions. Hardware that utilizes stereo vision perception, which has been demonstrated to be a more reliable way of estimating depth information than monocular vision, is partnered with the edge computing device Nvidia Jetson Xavier AGX. Our software for low light enhancement, depth estimation, and semantic segmentation tasks, is a transformer-based neural network. Our software stack, which enables fast inference and noise reduction, is packaged into system modules in Robot Operating System 2 (ROS2). Our experimental results have shown that the proposed end-to-end system is effective in improving the depth estimation and semantic segmentation performance. Our dataset and codes will be released at https://***/ZionGo6/HawkDrive. © 2024, CC BY-NC-ND.

关键词： Robot Operating System

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Cable-Integrated Single-Axis Load Cell for Tendon-Driven Robotic Actuation Enabled by Thin-Film Soft Sensor

Cable-Integrated Single-Axis Load Cell for Tendon-Driven Rob...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Jiyeon Cho Taejun Park Simon Kim Yong-Lae Park Department of Mechanical Engineering Institute of Advanced Machines and Design Institute of Engineering Research Seoul National University Seoul Korea Soft Robotics and Bionics Lab. Seoul National University Seoul Korea

Cable-driven force transmission is one of the most widely used actuation mechanisms in robotics, finding applications in robotic arms, grippers, and wearable robots. However, measuring cable tension directly is challenging due to limited space and difficulties in attaching sensors to flexible cables. In this paper, we propose a single-axis load cell designed for direct integration into a high-strength woven cable, enabling tension measurement. A small portion of the cable becomes slightly stretchable by infusing an uncured polymer into its weaving pattern, and a liquid-metal thin-film soft sensor is affixed externally. As the cable experiences axial strain, the thin-film soft sensor stretches and changes its electrical resistance, providing real-time tension information. To mitigate the hysteresis and the nonlinearity caused by the viscoelastic effect of the cable and tendon materials, a metal spring casing surrounds the sensor, making the behavior of the sensor governed by that of the spring. The proposed sensor is experimentally characterized, and the result shows a linearity in the range of 5 N to 100 N with negligible hysteresis. It is tested with a twisted-string actuator to control the axial force generated by the actuator through a closed-loop control as an application.

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Two-Photon Image-Guided Automated 3D Locating of Micropipette Tip

Two-Photon Image-Guided Automated 3D Locating of Micropipett...

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第43届中国控制会议

作者： Zizhen Li Jie Li Qili Zhao Xin Zhao Mingzhu Sun National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin Key Laboratory of Intelligent Robotics (tikLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The 21st century has been universally recognized by the scientific community as the era of neuroscience, with the patch clamp technique serving as a key to unraveling the intricate mysteries of brain science. Due to its advantages such as reduced sample damage and enhanced penetration capability, the application of two-photon microscope in deep imaging of live animals has become prevalent, making it a preferred choice over confocal microscope. At present, there exist a few near complete automation techniques of image-guided patching in vivo, which typically start from the micropipette tip appearing in the imaging field of view. The operational workflow for the step involving the installation of the micropipette until the micropipette tip becomes visible within the field of view has regrettably not been provided. However, it is crucial information that is of utmost importance to experimental personnel. In this paper, we present a comprehensive workflow for 3D locating of micropipette tip in a self-built two-photon microscope tailored for patch clamp experiments. Moreover, by fully incorporating the distinctive features of two-photon imaging, the study applies statistical methods to achieve automated execution of the corresponding workflow. The experimental results indicate that the process of micropipette tip locating takes an average of 58.4 ***, the method is not sensitive to parameter selection, allowing experimenters to obtain suitable parameters for different systems through simple testing. The automated 3D micropipette tip locating method proposed in this paper, with its versatility, holds great potential for seamless integration into commercial two-photon microscope systems. It significantly contributes to reducing the work burden on experimenters and minimizing the time required for experimental preparation.

关键词： 3D micropipette tip locating two-photon microscope automated method

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A Comprehensive SLAM Dataset for Indoor Exhibition Environments: Data Collection, Processing, and Comparative Evaluation

A Comprehensive SLAM Dataset for Indoor Exhibition Environme...

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IEEE International Conference on robotics and Biomimetics

作者： Songyu Yuan Jing Zhang Chen Pan Chunlong Zhang Minhong Wan Tao Zheng Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

The field of Simultaneous Localization and Mapping(SLAM) has witnessed significant advancements in recent years. However, due to the difficulty of data obtains and ground-truth makes, there is a lack of real indoor scenarios datasets, especially the scenarios with less-texture, repeat-texture, extreme light condition, causing the slowly progress in indoor SLAM. In this paper, we present a dataset captured in a real indoor exhibition hall with diverse type of sensor data. Which contributes to diverse school of SLAM that using different sensors. We provide ground-truth 6DoF pose captured by motion capture devices, RGB-D data captured by a RealSense D455 camera. Laser-data captured by Ouster. And a high-resolution color point cloud captured by a laser scanner. This dataset offers three distinct sources of ground truth from real-world scenarios. It combines motion capture, ICP based laser localization, and visual localization methods to provide comprehensive and accurate reference data. This dataset is designed for evaluating positioning methods that employ various types of sensors, moving beyond a singular matrix-based evaluation approach. Besides, We show results of several state-of-the-art SLAM method in our datasets, illustrates the utility of our dataset and served as a reference for other researcher. We converge LiDAR-based SLAM with ground truth, made a good closure. An ensemble approach-based Visual SLAM, achieves results of odometry errors of 4% and loop closure position errors of 40 cm. Additionally, visual localization demonstrated an absolute position error of 21 cm using high-resolution point clouds. We highlight the potentials of our indoor datasets in LiDAR SLAM, RGB-D SLAM, and visual localization and combines method. The datasets are available at https://***/IMBAfarmer09/ExhibitionDataset

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Adaptive Formation Control for Multiple Quadrotors with Nonlinear Uncertainties Using Lipschitz Neural Network

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IFAC-PapersOnLine 2023年第2期56卷 8714-8719页

作者： Yu-Wen Chen Ming-Li Chiang Li-Chen Fu Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan Ocean University KeeLung Taiwan Department of Electrical Engineering and Center for Artificial Intelligence and Robotics National Taiwan University Taipei Taiwan

This paper proposes a new distributed formation control scheme for multiple quadrotors with uncertainties, which is more practical for real implementation. The proposed design takes into account the inherent nonlinear and coupled dynamics of quadrotor and incorporates the Lipschitz model uncertainty. The multi-quadrotor formation control system is divided into the high-level distributed formation controller for position control, and the low-level attitude controller for desired acceleration tracking. To improve the performance of the low-level system, we employ the Lipschitz neural network (LipNet) adaptation. LipNet adaptation learns the model uncertainty and provides a significant improvement in acceleration tracking by modifying the reference signal of the low-level system. Consequently, the performance of the whole system is enhanced. We provide a stability analysis of the proposed design and validate the performance by some simulation examples.

关键词： Formation control adaptive control unmanned aerial vehicle multi-agent systems neural network

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Wu, Min-Jyun Lin, Hsiang-Jui Hung, Shih-Hao Shih, Chi-Sheng Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Center National Taiwan University Taipei Taiwan Institute of Networking and Multimedia High Performance and Scientific Computing Center Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. This work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. The proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. The experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework. Copyright © 2025 held by the owner/author(s).

关键词： Object detection

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Measuring Energy Consumption of BBOB Fitness Functions 28th

Measuring Energy Consumption of BBOB Fitness Functions

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Merelo-Guervós, Juan J. Romero López, Gustavo García-Valdez, Mario Department of Computer Engineering Automatics and Robotics and CITIC University of Granada Granada Spain Department of Graduate Studies National Technological Institute of Mexico Tijuana Mexico

ISBN: (纸本)9783031900648

Making software greener is a process that includes identifying the functions that consume the most energy, developing a methodology that can measure precisely that energy consumption and eventually measuring that energy under different design decisions and circumstances to be able to, eventually, produce best practices for minimizing said consumption. In this paper we are focusing on well-known floating-point fitness functions: some functions included in the black box optimization benchmark that cover all different types of functions under study. In general, these fitness functions will be the single operation that consumes the most energy;this is why in this paper we use them to test a methodology that is able to measure the energy consumed by their implementation in a low-level language C++. We test different single-element representations (single and double precision) as well as individual level representation (fixed size vs. variable size), drawing conclusions on the adequacy and accuracy of the methodology as well as which combination of the above elements would consume the least. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Power management (telecommunication)

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Design of a Miniaturized Magnetic Actuation System for Motion Control of Micro/Nano Swimming Robots

Design of a Miniaturized Magnetic Actuation System for Motio...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Sun, Liwen Wang, Huaping Shi, Qing Guo, Siyu Gao, Zhiqiao Sun, Tao Huang, Qiang Fukuda, Toshio Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Magnetically controlled microrobots for drug delivery and noninvasive treatment have great potential applications in the biomedical field in the future. The construction of the magnetic actuation system is an important step to realize the automated control of micro/nano swimmers. However, the construction of a magnetic actuation system still faces challenges;for example, the magnetic field cannot be turned off immediately, the distribution of the magnetic field in the workspace is not uniform, the working space is limited and the feedback is inconvenient. In view of the above problems, a design method based on an eight-axis electromagnetic coil magnetic control system is introduced in this paper, which can compositely actuate the microrobot and ensure movement with five degrees of freedom. In addition, the overall size of the system can be reduced as much as possible under the condition that the magnetic field in the workspace is sufficiently uniform and the magnetic field intensity is sufficiently large. Finally, in the experimental part, the magnetic field uniformity is verified by magnetic field simulation and measurement, and then the path following of the square trajectory is realized with the 75 \mu \mathrm{m} helical microswimmer as the operating object. © 2022 IEEE.

关键词： Degrees of freedom (mechanics)

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Distilling Knowledge in Vision-Based Human Motor Assessment for Improved Accuracy and Running Efficiency

Distilling Knowledge in Vision-Based Human Motor Assessment ...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Peijun Zhao Zhan Shen Moises Alencastre-Miranda David Whiteman Javier Gervas-Arruga Hermano Igo Krebs Department of Mechanical Engineering Massachusetts Institute of Technology USA Robotics Institute University of Michigan USA Takeda Development Center Americas Inc. USA

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Numerous neurological disorders can lead to a decline in patients' gross motor function, requiring long-term tracking of the disease progression to tailor treatment plans and evaluate intervention effectiveness. In recent years, researchers have explored computer vision methods as a cost-effective, at-home solution for monitoring human motor control. However, the current state-of-the-art approach relies on transfer learning from a general human action recognition model to address the challenge of limited training dataset size. This results in a relatively hefty model that may strain user's devices when running locally. In this study, we propose distilling knowledge from the teacher model into a lightweight student model to enhance operational efficiency on user's devices. Compared to directly training the student model with a limited amount of data, transferring and distilling knowledge from a generalized model can notably enhance accuracy. The lightweight student network can operate 1.5 to 3 times faster on various user devices compared to the large teacher network, with improvement being more noticeable on older devices in particular. This speed improvement opens up numerous opportunities for the adoption of this technology, facilitating its integration into a wider range of devices and environments.

关键词： Training Knowledge engineering Accuracy Computational modeling Transfer learning Training data Medical services Motors Data models Strain

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A Joint Tracking System: Robot is Online to Access Surveillance Views

A Joint Tracking System: Robot is Online to Access Surveilla...

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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Shanshan Ji Wen Wang Mengjie Qin Rong Yang Minhong Wan Jason Gu Te Li Chunlong Zhang Research Center for Intelligent Robotics Zhejiang Laboratory Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot’s field of vision and heavy occlusion by people or objects.

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