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International Conference on Information Fusion

作者： Xianqing Li Zhansheng Duan Uwe D. Hanebeck Center for Information Engineering Science Research Xi&#x0027 an Jiaotong University Xi&#x0027 an Shaanxi China Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.

关键词： Performance evaluation Target tracking Parameter estimation Filtering White noise Radar tracking Frequency measurement

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Feature-based Event Stereo Visual Odometry

Feature-based Event Stereo Visual Odometry

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European Conference on Mobile Robots (ECMR)

作者： Antea Hadviger Igor Cvišić Ivan Marković Sacha Vražić Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia Rimac Automobili Croatia

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by feature reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on both MVSEC and DSEC sequences. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization.

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

arXiv

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arXiv 2021年

作者： Barišić, Antonella Petric, Frano Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results. © 2021, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

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3D-printing advances mass spectrometry in biomedical analysis

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LabMed Discovery 2024年第1期1卷 19-31页

作者： Wei Chen Bo-chao Zhang Magdalena M.Stevanovic Lin Huang Kun Qian State Key Laboratory of Systems Medicine for Cancer School of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai 200030China Group for Biomedical Engineering and Nanobiotechnology Institute of Technical SciencesSerbian Academy of Sciences and ArtsKneza Mihaila 35/IVBeograd 11000Serbia Department of Clinical Laboratory Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200030China Institute of Thoracic Oncology Shanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai 200030China

Mass spectrometry(MS)is an analytical technique for molecular identification and characterization,with applications spanning various scientific *** its significance,MS faces challenges in widespread adoption due to cost constraints,instrument portability issues,and complex sample handling *** recent years,3D printing has emerged as a technology across industries due to its cost-effectiveness,customization capabilities,and rapid prototyping *** review explores the integration of 3D printing with MS technology to overcome existing limitations and enhance biomedical analysis *** first categorize mainstream 3D printing methods and assess their potential in MS *** also discuss their roles in different MS categories such as liquid chromatography mass spectrometry(LCMS),gas chromatography mass spectrometry(GCMS),ambient ionization mass spectrometry(AIMS),and matrix-assisted laser desorption/ionization MS(MALDI MS)in biomedical ***,we highlight the current challenges and future research directions for advancing 3D printing-assisted mass spectrometry,emphasizing its role in enabling portable,costeffective,and customized MS solutions for biomedical analysis.

关键词： 3D-printing Mass spectrometry Liquid chromatography mass spectrometry (LCMS) Gas chromatograpgy(GCMS) Ambient ionization mass spectrometry(AIMS)

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

CPnP: Consistent Pose Estimator for Perspective-n-Point Prob...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Guangyang Zeng Shiyu Chen Biqiang Mu Guodong Shi Junfeng Wu School of Data Science Chinese University of Hong Kong Shenzhen P. R. China Key Laboratory of Systems and Control Institute of Systems Science Beijing P. R. China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China The Australian Center for Field Robotics School of Aerospace Sydney Mechanical and Mechatronic Engineering The University of Sydney Sydney Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen P. R. China

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has $O(n)$ time complexity. Simulations and real dataset tests show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time.

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Feature-based event stereo visual odometry

arXiv

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arXiv 2021年

作者： Hadviger, Antea Cvišić, Igor Marković, Ivan Vražić, Sacha Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia Rimac Automobili Croatia

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on the MVSEC sequences, while on the DSEC dataset ESVO, unlike our method, was unable to handle outdoor driving scenario with default parameters. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization. © 2021, CC BY.

关键词： Cameras

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Robust Stabilization of a Knee Exoskeleton Robot Using an LMI-Based Composite Controller

Robust Stabilization of a Knee Exoskeleton Robot Using an LM...

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Indian Control Conference (ICC)

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia RISC Lab. Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computing Imperial College London UK

ISBN: (数字)9798331517212

ISBN: (纸本)9798331517229

Rehabilitation exoskeleton robots are vital for restoring lower limb functionality in individuals with locomotor disorders. Extensive research has focused on optimizing the control of these robotic systems and make them more stable. However, there is still a considerable gap in developing robust controllers. This work introduces a robust control approach tailored for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot. Emphasizing position control, our method addresses challenges such as parameter uncertainties, solid and viscous frictions, external disturbances, and motion constraints. To effectively manage these issues, we propose a composite controller that integrates a linear state-feedback controller and a nonlinear control law. By employing a quadratic Lyapunov function, we derive the expression of the nonlinear controller and establish the LMI conditions required for calculating the matrix gain of the composite controller using two different design methods. To confirm the developed LMI conditions and demonstrate the proposed control method's ability to ensure robust position control of the knee exoskeleton robotic system, simulation results are finally provided.

关键词： Robust control Uncertain systems Limbs Friction Exoskeletons Position control Solids systems simulation Robots Lyapunov methods

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Path Tracking Control for Helical Microrobots Based on Fusion of Geometric and Model-Free Methods

Path Tracking Control for Helical Microrobots Based on Fusio...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Shihao Zhong Huaping Wang Qing Shi Yaozhen Hou Yukang Qiu Tao Sun Qiang Huang Toshio Fukuda School of Medical Technology Beijing Institute of Technology Beijing China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

Helical micron-sized robots driven by magnetic fields have garnered significant interest, particularly in the field of tracking control. However, at the microscale, time-varying uncertain perturbations in both the environment and electromagnetic system can cause these robots to deviate from their preset state. Here, we propose a novel control method that combines geometric and model-free controllers in a complementary manner to achieve accurate path-tracking of a helical microrobot on underwater unstructured surfaces. The geometric controller acts as a feedforward controller handling path information and formulating guidance law, while the model-free controller responds directly to the lateral error. To compensate for unmodeled nonlinear dynamics and unknown disturbances and achieve system dynamic linearization, we employ an extended state observer. To validate the proposed method and determine suitable ranges for input parameters of the controllers, simulations were carried out. The effectiveness of our path-tracking control method is validated through experimental results. The control technique provides an initial investigation into addressing the effects of uncertainties and perturbation on the control capabilities of magnetic microrobots.

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Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization

Land & Localize: An Infrastructure-free and Scalable Nano-Dr...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Mahyar Pourjabar Ahmed AlKatheeri Manuele Rusci Agata Barcis Vlad Niculescu Eliseo Ferrante Daniele Palossi Luca Benini Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy Autonomous Robotics Research Center (ARRC) Technology Innovation Institute UAE Department of Electrical Engineering KU Leuven Belgium Integrated Systems Laboratory (IIS) ETH Zürich Switzerland Dalle Molle Institute for Artificial Intelligence (IDSIA) USI-SUPSI Switzerland

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements between peer nano-drones and a stationary infrastructure of multiple UWB anchors. Therefore, we propose an UWB-based infrastructure-free nanodrones swarm, where part of the fleet acts as dynamic anchors, i.e., anchor-drones (ADs), capable of automatic deployment and landing. By varying the ADs' position constraint, we develop three alternative solutions with different trade-offs between flexibility and localization accuracy. In-field results, with four flying mission- drones (MDs), show a localization root mean square error (RMSE) spanning from 15.3 cm to 27.8 cm, at most. Scaling the number of MDs from 4 to 8, the RMSE marginally increases, i.e., less than 10 cm at most. The power consumption of the MDs' UWB module amounts to 342mW. Ultimately, compared to a fixed-infrastructure commercial solution, our infrastructure-free system can be deployed anywhere and rapidly by taking 5.7 s to self-localize 4 ADs with a localization RMSE of up to 12.3% in the most challenging case with 8 MDs.

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