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LiDAR Extrinsic Calibration Without Accessing 3D Point Cloud Data: A New Approach for LiDAR-Vehicle Alignment Inspection

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IEEE Sensors Journal 2025年

作者： Song, Hyeong-Seok Park, Jae-Eun Ahn, Chang-Min Kim, Young-Keun Korea Institute of Robotics and Technology Convergence Korea Republic of Handong Global University Department of Mechanical and Control Engineering Pohang Korea Republic of Handong Global University School of Mechanical and Control Engineering Korea Republic of

LiDAR-vehicle external calibration involves estimating the relative pose of the LiDAR body from the ego-vehicle coordinate frame, which is required after the LiDAR is assembled on the vehicle. Current methods necessitate direct wiring to the LiDAR controller unit to access 3D point cloud data. However, situations can limit a third party’s access to the LiDAR control unit for security and other practical reasons. Can the LiDAR-vehicle pose be estimated under such circumstances? Therefore, this study addresses this issue by proposing a new approach to the LiDAR extrinsic calibration method that does not require accessing 3D point cloud data. The proposed solution uses a designed camera-target board system to capture images of LiDAR beams projected on a cylindrical target. By utilizing geometrical modeling equations, it can estimate the relative 6D pose of the LiDAR body on the reference frame. Experiments demonstrated accuracy within 0.05 degrees and 0.7 mm. This one-shot LiDAR-vehicle alignment inspection system when accessing 3D point cloud data is restricted for various practical applications. © 2001-2012 IEEE.

关键词： Cylindrical target LiDAR alignment LiDAR external calibration LiDAR pose estimation NIR laser one-shot calibration point cloud data

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ISLR101: an Iranian Word-Level Sign Language Recognition Dataset

arXiv

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arXiv 2025年

作者： Ranjbar, Hossein Taheri, Alireza Social and Cognitive Robotics Lab. Department of Mechanical Engineering Sharif University of Technology Tehran Iran

Sign language recognition involves modeling complex multichannel information, such as hand shapes and movements while relying on sufficient sign language-specific data. However, sign languages are often under-resourced, posing a significant challenge for research and development in this field. To address this gap, we introduce ISLR101, the first publicly available1 Iranian Sign Language dataset for isolated sign language recognition. This comprehensive dataset includes 4,614 videos covering 101 distinct signs, recorded from 10 different signers (3 deaf individuals, 2 sign language interpreters, and 5 L2 learners2) against varied backgrounds, with a resolution of 800×600 pixels and a frame rate of 25 frames per second. It also includes skeleton pose information extracted using OpenPose. We establish both a visual appearance-based and a skeleton-based framework as baseline models, thoroughly training and evaluating them on ISLR101. These models achieve 97.01% and 94.02% accuracy on the test set, respectively. Additionally, we publish the train, validation, and test splits to facilitate fair comparisons. © 2025, CC BY-NC-ND.

关键词： Musculoskeletal system

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Supernumerary Robotic Limbs to Augment Astronauts Performing Post-Fall Recoveries during Partial-Gravity Spacewalks

Supernumerary Robotic Limbs to Augment Astronauts Performing...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ballesteros, Erik Carpenter, Kalind Asada, H. Harry Department of Mechanical Engineering Massachusetts Institute of Technology Course 2 CambridgeMA02139 United States Advanced Robotics Systems Group 347Z NASA Jet Propulsion Laboratory PasadenaCA91011 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper investigates the effectiveness of a type of wearable robot, known as Supernumerary Robotic Limbs (SuperLimbs) in assisting an astronaut performing partial-gravity ExtraVehicular Activities (EVAs). SuperLimbs can directly address a key technology gap identified by NASA concerning that of incapacitated crew rescue devices during partial-gravity EVAs. To contextualize SuperLimbs in human spaceflight application, we start by reviewing other forms of wearable robotics technologies and perform a qualitative comparison with SuperLimbs. From there, we present a suite of operational modes that SuperLimbs is capable of delivering in a suited partial-gravity EVA. Within the operational mode suite, we selected the most urgent use-case: that of performing a post-fall recovery, and reviewed the results from a pilot-human study. To validate the effectiveness of SuperLimbs in providing the necessary assistive forces needed for a post-fall recovery, a series of validation experiments was performed with a prototype SuperLimbs system. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping

arXiv

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arXiv 2025年

作者： Liu, Guangyi Iloglu, Suzan Caldara, Michael Durham, Joseph W. Zavlanos, Michael M. Amazon Robotics United States Department of Mechanical Engineering and Materials Science Duke University United States

In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lead to increased package recirculation. To tackle this challenge, we introduce a Distributionally Robust Multi-Agent Reinforcement Learning (DRMARL) framework that learns a destination-to-chute mapping policy that is resilient to adversarial variations in induction rates. Specifically, DRMARL relies on group distributionally robust optimization (DRO) to learn a policy that performs well not only on average but also on each individual subpopulation of induction rates within the group that capture, for example, different seasonality or operation modes of the system. This approach is then combined with a novel contextual bandit-based predictor of the worst-case induction distribution for each state-action pair, significantly reducing the cost of exploration and thereby increasing the learning efficiency and scalability of our framework. Extensive simulations demonstrate that DRMARL achieves robust chute mapping in the presence of varying induction distributions, reducing package recirculation by an average of 80% in the simulation scenario. © 2025, CC BY.

关键词： Adversarial machine learning

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Energy Dynamics Powered by Traction and Stress Control Formation and Motion of +1/2 Topological Defects in Epithelial Cell Monolayers

arXiv

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arXiv 2025年

作者： Bera, Pradip K. McCord, Molly Zhang, Jun Notbohm, Jacob Department of Mechanical Engineering Biophysics Program Department of Nuclear Engineering and Engineering Physics University of Wisconsin–Madison MadisonWI53706 United States

In confluent cell monolayers, patterns of cell forces and motion are systematically altered near topological defects in cell shape. In turn, defects have been proposed to alter cell density, extrusion, and invasion, but it remains unclear how the defects form and how they affect cell forces and motion. Here, we studied +1/2 defects, and, in contrast to prior studies, we observed both tail-to-head and head-to-tail defect motion occurring at the same time in the same cell monolayer. We quantified the cell velocities, the tractions at the cell-substrate interface, and stresses within the cell layer near +1/2 defects. Results revealed that both traction and stress are sources of activity within the epithelial cell monolayer, with their competition defining whether the cells inject or dissipate energy and determining the direction of motion of +1/2 defects. Interestingly, patterns of motion, traction, stress, and energy injection near +1/2 defects existed before defect formation, suggesting that defects form as a result of spatially coordinated patterns in cell forces and motion. These findings reverse the current picture, from one in which defects define the cell forces and motion to one in which coordinated patterns of cell forces and motion cause defects to form and move. Copyright © 2025, The Authors. All rights reserved.

关键词： Monolayers

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A Lead-Time-Aware Decomposition Approach to Optimize Disruption Response in Supply Chains

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IEEE Transactions on Automation Science and engineering 2025年

作者： Estrada-Garcia, Juan-Alberto Bi, Mingjie Tilbury, Dawn M. Barton, Kira Shen, Siqian University of Michigan Department of Industrial and Operations Engineering Ann ArborMI48109 United States University of Michigan Robotics Department Ann ArborMI48109 United States University of Michigan Robotics Department and the Department of Mechanical Engineering Ann ArborMI48109 United States

Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time to process and transport products to downstream entities. In disruptive events, lead times and capacities may vary, which affects the overall performance of SC. There have been many studies on SC disruption mitigation, but often without considering lead time and the magnitude of lateness. In this paper, we formulate a mixed integer programming (MIP) model to optimize SC operations via a routing and scheduling approach, to model the delivery time of products at different entities as they flow throughout the SC network. We minimize a weighted sum of multiple objectives that involve costs related to transportation, shortages, and delivery lateness. We further develop a Benders decomposition algorithm for speeding up the computation of the NP-hard MIP model. We also develop a discrete-event simulation framework to evaluate the performance of solutions to the MIP model under lead time uncertainty. Through extensive numerical studies, we show how the attributes of SC entities affect the performance, so that we can improve the SC design and operations under various uncertainties. © 2004-2012 IEEE.

关键词： Mixed-integer linear programming

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Nanometer-scale wafer surface defect measurement based on state-space digital holographic microscopy 4

Nanometer-scale wafer surface defect measurement based on st...

引用

4th International Computational Imaging Conference, CITA 2024

作者： Geng, Shengcheng Ai, Lingyu Gao, Yujia Gan, Zhi Xiao, Yingjie Gong, Weixu Cho, Myungiin Won, Kanghee School of Integrated Circuits Jiangnan University Wuxi214122 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Hankyong National University School of ICT Robotics and Mechanical Engineering Anseong17579 Korea Republic of Kyung Hee University Department of Information Display Seoul02447 Korea Republic of

ISBN: (纸本)9781510688834

Non-destructive measurement of nanoscale surface defects plays a significant role in wafer production and processing. This paper proposes a method based on state-space digital holographic microscopy for measuring wafer surface defects with nanometer-scale accuracy. The angular spectrum method is used to numerically reconstruct the complex wavefront of the object, while reference holograms are applied for conjugate compensation, effectively reducing the effects of surface distortions and noise. To optimize phase estimation, the extended Kalman filter (EKF) method is employed, which can suppress noise, resolve phase unwrapping issues, and achieve accurate phase recovery from a single hologram, thereby extracting detailed defect information from wafer surfaces. Compared to traditional phase recovery methods, this approach demonstrates superior performance in high-noise environments, significantly improving defect measurement accuracy and achieving nanometer-level surface defect measurement. The proposed method has extensive application potential, especially in surface defect detection research for semiconductor manufacturing. © 2025 SPIE.

关键词： Extended Kalman filters

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Kinematic Modeling of a 7-DOF Tendon-Like-Driven Robot Based on Optimization and Deep Learning

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Journal of Field robotics 2025年第0期

作者： Chen, SaiXuan Mu, SaiHu Jiang, GuanWu Omar, Abdelaziz Zhu, Zina Niu, Fuzhou School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China School of Mechanical Engineering Tongji University Shanghai China School of Information Engineering Southwest University of Science and Technology Mianyang China Mechatronics and Robotics Department Egyptian Russian University Badr city Cairo Egypt School of Mechanical Engineering Suzhou University of Science and Technology Suzhou China

This paper proposes a novel 7-DOF tendon-like-driven redundant robot (TDR7) based on a weighted inverse kinematics (IK) optimization algorithm and a deep learning fine-tuning model. The robot features a modular design that enables highly flexible movements of the shoulder, elbow, and wrist joints. Its kinematic model is established using the Denavit-Hartenberg (D-H) parameter method. To address the complexity of solving IK for 7-DOF redundant robots, a weighted gradient projection method specialized for TDR7 (SWGPM-TDR7) is introduced. This algorithm integrates joint constraints, singularity avoidance, and minimum energy consumption into a multi-objective optimization framework, significantly improving joint motion continuity and trajectory planning efficiency while maintaining solution accuracy. To further accommodate complex trajectory planning requirements, a deep learning fine-tuning model (RWKV-TDR7) that combines recurrent networks with self-attention mechanisms is introduced. Through fine-tuning, RWKV-TDR7 achieves efficient trajectory fitting for TDR7, supports long-sequence outputs, and reduces computational complexity. Simulation and experimental validations demonstrate that the robot exhibits excellent performance in forward kinematics, inverse kinematics, and trajectory tracking in terms of accuracy, stability, and continuity. This work provides an effective solution for the design of high-performance robotic systems in medical and industrial applications. © 2025 Wiley Periodicals LLC.

关键词： Inverse kinematics

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Augmented Reality Based Navigation System for Endoscopic Transnasal Optic Canal Decompression

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 34-42页

作者： FU Hang XU Jiangchang LI Yinwei ZHOU Huifang CHEN Xiaojun Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory System and VibrationSchool of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Ophthalmology Shanghai Ninth People's HospitalShanghai Jiao Tong University School of MedicineShanghai 200011China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China

Endoscopic transnasal optic nerve decompression surgery plays a crucial role in minimal invasive treatment of complex traumatic optic ***,a major challenge faced during the procedure is the inability to visualize the optic nerve *** address this issue,an endoscopic image-based augmented reality surgical navigation system is developed in this *** system aims to virtually fuse the optic nerve onto the endoscopic images,assisting surgeons in determining the optic nerve’s position and reducing surgical ***,a calibration algorithm based on a checkerboard grid of immobile points is proposed,building upon existing calibration ***,to tackle accuracy issues associated with augmented reality technology,an optical navigation and visual fusion compensation algorithm is proposed to improve the intraoperative tracking *** evaluate the system’s performance,model experiments were meticulously designed and *** results confirm the accuracy and stability of the proposed system,with an average tracking error of(0.99±0.46)*** outcome demonstrates the effectiveness of the proposed algorithm in improving the augmented reality surgical navigation system’s ***,the system successfully displays hidden optic nerves and other deep tissues,thus showcasing the promising potential for future applications in orbital and maxillofacial surgery.

关键词： optic nerve decompression endoscopy augmented reality surgical navigation

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