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Conference on Lasers and Electro-Optics (CLEO)

作者： Owen M. O’Malley Svetlana Avramov-Zamurovic Nathaniel A. Ferlic Linda J. Mullen K. Peter Judd Weapons Robotics and Control Engineering Department United States Naval Academy Hopper Hall Annapolis MD USA Naval Air Warfare Center – Aircraft Division (NAWCAD) Patuxent River MD USA United States Naval Research Laboratory Washington DC USA

A Gaussian beam is propagated through a water tank containing Rayleigh-Bénard convection. The beam wavefront is measured using a Shack-Hartmann sensor to obtain optical wavefront statistics for characterization o... 详细信息

ISBN: (纸本)9781957171258

关键词： Atmospheric turbulence Beam expanders Gaussian beams Laser communications Optical sensing Optical turbulence

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive control Framework for Exciting Efficient Oscillations in Mechanical Systems

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control...

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IEEE Conference on Decision and control

作者： Andre Coelho Alin Albu-Schaeffer Arne Sachtler Hrishik Mishra Davide Bicego Christian Ott Antonio Franchi Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) Oberpfaffenhofen Germany Dextrous Robotics Inc Memphis TN USA Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Automation and Control Institute (ACIN) Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a Nonlinear Model-Predictive control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

关键词： Analytical models Aerospace electronics Energy efficiency Trajectory Mechanical systems Oscillators Robots

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Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

arXiv

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arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and 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 The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Symplectic Discrete-Time control of Flexible-Joint Robots: Experiments with Two Links ⁎

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IFAC-PapersOnLine 2021年第19期54卷 1-7页

作者： Tobias Thoma Xuwei Wu Alexander Dietrich Paul Kotyczka Technical University of Munich Department of Mechanical Engineering Chair of Automatic Control Garching Germany German Aerospace Center (DLR) Institute of Robotics and Mechatronics Oberpfaffenhofen Germany

We present the application and experimental validation of discrete-time control implementation based on symplectic integration - in short referred to as symplectic discrete-time control - to a serial robot manipulator with flexible-joints. Applying the implicit midpoint rule as a simple and popular symplectic integration scheme to both the model of the sampled system and the desired target dynamics, yields a discrete-time control law with the same structure as in continuous time. The arguments are, however, predicted stage values on the subsequent sampling interval resulting from a half implicit Euler step for the target system. The approach can be easily extended to a purely position-based feedback law. The experimental validation on a lightweight robot (KUKA LWR IV+) confirms the expected improvements at low sampling rates, with an increase in the assignable closed-loop stiffness of up to 29 %.

关键词： Sampled-data systems discrete-time control symplectic integration nonlinear mechanical systems flexible-joint robots serial elastic actuators KUKA LWR

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Salient Object Detection in Traffic Scene through the TSOD10K Dataset

arXiv

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arXiv 2025年

作者： Qiu, Yu Sun, Yuhang Mei, Jie Xiao, Lin Xu, Jing College of Information Science and Engineering Hunan Normal University Changsha410081 China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases College of Artificial Intelligence Nankai University Tianjin300350 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even with low visual contrast. This dual criterion, i.e., bridging perception and contextual risk, re-defines saliency for autonomous and assisted driving systems. To address the lack of task-specific benchmarks, we collect the first large-scale TSOD dataset with pixel-wise saliency annotations, named TSOD10K. TSOD10K covers the diverse object categories in various real-world traffic scenes under various challenging weather/illumination variations (e.g., fog, snowstorms, low-contrast, and low-light). Methodologically, we propose a Mamba-based TSOD model, termed Tramba. Considering the challenge of distinguishing inconspicuous visual information from complex traffic backgrounds, Tramba introduces a novel Dual-Frequency Visual State Space module equipped with shifted window partitioning and dilated scanning to enhance the perception of fine details and global structure by hierarchically decomposing high/low-frequency components. To emphasize critical regions in traffic scenes, we propose a traffic-oriented Helix 2D-Selective-Scan (Helix-SS2D) mechanism that injects driving attention priors while effectively capturing global multi-direction spatial dependencies. We establish a comprehensive benchmark by evaluating Tramba and 22 existing NSI-SOD models on TSOD10K, demonstrating Tramba’s superiority. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk perception

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Distributed IDA-PBC for a Class of Nonholonomic Mechanical Systems

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IFAC-PapersOnLine 2021年第14期54卷 275-280页

作者： A. Tsolakis T. Keviczky Cognitive Robotics Delft University of Technology 2628 CD Delft The Netherlands Delft Center for Systems and Control Delft University of Technology 2628 CD Delft The Netherlands

Nonholonomic mechanical systems encompass a large class of practically interesting robotic structures, such as wheeled mobile robots, space manipulators, and multi-fingered robot hands. However, few results exist on the cooperative control of such systems in a generic, distributed approach. In this work we extend a recently developed distributed Interconnection and Damping Assignment Passivity-Based control (IDA-PBC) method to such systems. More specifically, relying on port-Hamiltonian system modelling for networks of mechanical systems, we propose a full-state stabilization control law for a class of nonholonomic systems within the framework of distributed IDA-PBC. This enables the cooperative control of heterogeneous, underactuated and nonholonomic systems with a unified control law. This control law primarily relies on the notion of Passive Configuration Decomposition (PCD) and a novel, non-smooth desired potential energy function proposed here. A low-level collision avoidance protocol is also implemented in order to achieve dynamic inter-agent collision avoidance, enhancing the practical relevance of this work. Theoretical results are tested in different simulation scenarios in order to highlight the applicability of the derived method.

关键词： Distributed Passivity-Based control IDA-PBC Nonholonomic Mechanical

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Adaptive single-stage control for uncertain nonholonomic Euler-Lagrange systems

Adaptive single-stage control for uncertain nonholonomic Eul...

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IEEE Conference on Decision and control

作者： Tian Tao Spandan Roy Simone Baldi Delft Center for Systems and Control Delft University of Technology (TU Delft) Netherlands Robotics Research Centre International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic constraints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that address kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dynamics and compared to a standard double-stage approach.

关键词： Adaptive systems Philosophical considerations Kinematics control systems Stability analysis Mobile robots Numerical stability

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Modeling and Preliminary Characterization of a Large Differential-Drive Mobile Robot

Modeling and Preliminary Characterization of a Large Differe...

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Robotica Congreso Mexicano de (COMRob)

作者： José A. Zepeda-Hernández Ildeberto Santos-Ruiz Vicente Parra-Vega Francisco R. López-Estrada Tecnológico Nacional de México I.T. Tuxtla Gutiérrez Turix-Dynamics Diagnosis and Control Group Tuxtla Gutiérrez Mexico Centro de investigatión y Estudios Avanzados del IPN Robotics and Advanced Manufacturing Center for Research and Advanced Studies Ramos Arizpe Mexico

ISBN: (数字)9798331533397

ISBN: (纸本)9798331533403

A comprehensive procedure is presented for deriving the kinematic and dynamic models of an electrically driven differential drive mobile robot (DDMR) with four wheels, configured in a 2:1 arrangement. The synthesis of this Lagrangian formulation, subject to non-holonomic constraints, includes the dynamics of the motors for each wheel. This model is characterized using the Turix VE 2.0 mobile robot from the control Group Laboratory, aimed at executing arbitrary trajectories comparing it with an experimental data set based on sensor fusion from an IMU (Inertial Measurement Unit), encoder on the axis of each motor and the extended Kalman *** results show consistent data validating the effectiveness of the proposed modeling approach, using parameters from a real robot. Valuable insights are obtained that guide the next phase of closed-loop control, highlighting the importance of the third-order model, which integrates motor dynamics into the motion control architecture. These findings provide a foundation for the design and development of a robotic autonomous vehicle of these dimensions.

关键词： Analytical models Trajectory tracking Dynamics Wheels Kinematics Data models Trajectory Mobile robots Vehicle dynamics Testing

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CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP

arXiv

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arXiv 2024年

作者： Zuo, Zuo Dong, Jiahao Wu, Yao Qu, Yanyun Wu, Zongze National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'An710049 China China The School of Informatics Xiamen University Xiamen361005 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data in industrial scenarios, lots of few-shot anomaly detection methods emerge recently. In this paper, we propose an effective few-shot anomaly classification (FSAC) framework with one-stage training, dubbed CLIP-FSAC++. Specifically, we introduce a cross-modality interaction module named Anomaly Descriptor following image and text encoders, which enhances the correlation of visual and text embeddings and adapts the representations of CLIP from pre-trained data to target data. In anomaly descriptor, image-to-text cross-attention module is used to obtain image-specific text embeddings and text-to-image cross-attention module is used to obtain text-specific visual embeddings. Then these modality-specific embeddings are used to enhance original representations of CLIP for better matching ability. Comprehensive experiment results are provided for evaluating our method in few-normal shot anomaly classification on VisA and MVTEC-AD for 1, 2, 4 and 8-shot settings. The source codes are at https://***/Jay-zzcoder/clip-fsac-pp. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Towards Consistent Object Detection via LiDAR-Camera Synergy

Towards Consistent Object Detection via LiDAR-Camera Synergy

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Luo Hao Wu Kefu Yi Kailun Yang Wei Hao Rongdong Hu College of Automotive and Mechanical Engineering Changsha University of Science and Technology China School of Traffic and Transportation Engineering Changsha University of Science and Technology China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China Changsha Intelligent Driving Institute China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As human-machine interaction continues to evolve, the capacity for environmental perception is becoming increasingly crucial. Integrating the two most common types of sensory data, images, and point clouds, can enhance detection accuracy. Currently, there is no existing model capable of detecting an object's position in both point clouds and images while also determining their corresponding relationship. This information is invaluable for human-machine interactions, offering new possibilities for their enhancement. In light of this, this paper introduces an end-to-end Consistency Object Detection (COD) algorithm framework that requires only a single forward inference to simultaneously obtain an object's position in both point clouds and images and establish their correlation. Furthermore, to assess the accuracy of the object correlation between point clouds and images, this paper proposes a new evaluation metric, Consistency Precision (CP). To verify the effectiveness of the proposed framework, an extensive set of experiments has been conducted on the KITTI and DAIR-V2X datasets. The study also explored how the proposed consistency detection method performs on images when the calibration parameters between images and point clouds are disturbed, compared to existing post-processing methods. The experimental results demonstrate that the proposed method exhibits ex-cellent detection performance and robustness, achieving end-to-end consistency detection. The source code will be made publicly available at https://***/xifen523/COD.

关键词： Point cloud compression Human computer interaction Measurement Accuracy Correlation Source coding Instruments Object detection Robustness Inference algorithms

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