IEEE Robotics and Automation Letters最新文献_第2页

Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition 利用三维视觉骨骼识别的移动辅助机器人近距离实时坐姿-站立相位分类

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527280

Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur

{"title":"Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition","authors":"Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur","doi":"10.1109/LRA.2025.3527280","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527280","url":null,"abstract":"Sit-to-stand (STS) transfer is a fundamental but challenging movement that plays a vital role in older adults' daily activities. The decline in muscular strength and coordination ability can result in difficulties performing STS and, therefore, the need for mobility assistance by humans or assistive devices. Robotics rollators are being developed to provide active mobility assistance to older adults, including STS assistance. In this paper, we consider the robotic walker SkyWalker, which can provide active STS assistance by moving the handles upwards and forward to bring the user to a standing configuration. In this context, it is crucial to monitor if the user is performing the STS and adapt the rollator's control accordingly. To achieve this, we utilized a standard vision-based method for estimating the human pose during the STS movement using Mediapipe pose tracking. Since estimating a user's state from extreme proximity to the camera is challenging, we compared the pose identification results from Mediapipe to ground truth data obtained from Vicon marker-based motion capture to assess accuracy and reliability of the STS motion. The fourteen kinematic features critical for accurate pose estimation were selected based on literature review and the specific requirements of our robot's STS method. By employing these features, we have implemented a phase classification system that enables the SkyWalker to classify the user's STS phase in real-time. The selected kinematics from vision-based human state estimation method and trained classifier can be furthermore generalized to other types of motion support, including adaptive STS path planning and emergency stops for safety insurance during STS.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2160-2167"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities 具有变化能力的机器人高水平协同任务的在线再合成

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527337

Amy Fang;Tenny Yin;Hadas Kress-Gazit

引用次数: 0

GelBelt: A Vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces GelBelt：用于大表面连续传感的基于视觉的触觉传感器

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527306

Mohammad Amin Mirzaee;Hung-Jui Huang;Wenzhen Yuan

引用次数: 0

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups 运动群非参数估计的调和指数滤波器

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527346

Miguel Saavedra-Ruiz;Steven A. Parkison;Ria Arora;James Richard Forbes;Liam Paull

{"title":"The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups","authors":"Miguel Saavedra-Ruiz;Steven A. Parkison;Ria Arora;James Richard Forbes;Liam Paull","doi":"10.1109/LRA.2025.3527346","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527346","url":null,"abstract":"Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This letter introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2096-2103"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empirically Compensated Setpoint Tracking for Spherical Robots With Pressurized Soft-Shells 带加压软壳球形机器人的经验补偿设定值跟踪

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527308

Derek J. Pravecek;Micah J. Oevermann;Gray C. Thomas;Robert O. Ambrose

{"title":"Empirically Compensated Setpoint Tracking for Spherical Robots With Pressurized Soft-Shells","authors":"Derek J. Pravecek;Micah J. Oevermann;Gray C. Thomas;Robert O. Ambrose","doi":"10.1109/LRA.2025.3527308","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527308","url":null,"abstract":"Replacing spherical robots' hard shells with soft, pressurized tires has the potential to improve their off-road practicality immensely. This change leverages spherical robots as a simple and rugged solution to problems currently addressed using wheeled or tracked vehicles. Though numerous prototypes have been launched over the last three decades, there has not been a spherical robot that poses a serious contender to tracked and wheeled systems. Most prototypes are built with a hard outer shell for ease of construction and control. Hard outer shells fail to absorb the impacts from uneven terrain. We addressed this issue by constructing a one-of-a-kind spherical robot with a durable pneumatic, soft outer shell. Although a soft-shell is more desirable for locomotion, it introduces complicated, nonlinear shell dynamics that cause a more challenging control problem. This article presents an empirical model of the steady-state torque induced by soft-shell dynamics, developed using system identification and a model based on tire dynamics. We show how this model, which fingerprints the robot's contact dynamics, is incorporated into RoboBall's steering control algorithm to compensate for soft-shell effects, enhancing setpoint tracking and improving control performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2136-2143"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Reinforcement Learning Method for Collision Avoidance with Dense Speed-Constrained Multi-UAV 密集速度约束多无人机避碰的深度强化学习方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527292

Jiale Han;Yi Zhu;Jian Yang

引用次数: 0

Stable-BC: Controlling Covariate Shift With Stable Behavior Cloning Stable- bc：用稳定行为克隆控制协变量移位

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-06 DOI: 10.1109/LRA.2025.3526439

Shaunak A. Mehta;Yusuf Umut Ciftci;Balamurugan Ramachandran;Somil Bansal;Dylan P. Losey

{"title":"Stable-BC: Controlling Covariate Shift With Stable Behavior Cloning","authors":"Shaunak A. Mehta;Yusuf Umut Ciftci;Balamurugan Ramachandran;Somil Bansal;Dylan P. Losey","doi":"10.1109/LRA.2025.3526439","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526439","url":null,"abstract":"Behavior cloning is a common imitation learning paradigm. Under behavior cloning the robot collects expert demonstrations, and then trains a policy to match the actions taken by the expert. This works well when the robot learner visits states where the expert has already demonstrated the correct action; but inevitably the robot will also encounter new states outside of its training dataset. If the robot learner takes the wrong action at these new states it could move farther from the training data, which in turn leads to increasingly incorrect actions and compounding errors. Existing works try to address this fundamental challenge by augmenting or enhancing the training data. By contrast, in our letter we develop the <italic>control theoretic</i> properties of behavior cloned policies. Specifically, we consider the error dynamics between the system's current state and the states in the expert dataset. From the error dynamics we derive model-based and model-free conditions for stability: under these conditions the robot shapes its policy so that its current behavior converges towards example behaviors in the expert dataset. In practice, this results in Stable-BC, an easy to implement extension of standard behavior cloning that is provably robust to covariate shift. We demonstrate the effectiveness of our algorithm in simulations with interactive, nonlinear, and visual environments. We also conduct experiments where a robot arm uses Stable-BC to play air hockey.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1952-1959"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modular Self-Reconfigurable Continuum Robot for General Purpose Loco-Manipulation 通用位置操作模块化自重构连续体机器人

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-06 DOI: 10.1109/LRA.2025.3526560

Yilin Cai;Haokai Xu;Yifan Wang;Desai Chen;Wojciech Matusik;Wan Shou;Yue Chen

{"title":"Modular Self-Reconfigurable Continuum Robot for General Purpose Loco-Manipulation","authors":"Yilin Cai;Haokai Xu;Yifan Wang;Desai Chen;Wojciech Matusik;Wan Shou;Yue Chen","doi":"10.1109/LRA.2025.3526560","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526560","url":null,"abstract":"Modular Self-Reconfigurable Robots offer exceptional adaptability and versatility through reconfiguration, but traditional rigid robot designs lack the compliance necessary for effective interaction with complex environments. Recent advancements in modular soft robots address this shortcoming with enhanced flexibility; however, their designs lack the capability of active self-reconfiguration and heavily rely on manual assembly. In this letter, we present a modular self-reconfigurable soft continuum robotic system featuring a continuum backbone and an omnidirectional docking mechanism. This design enables each module to independently perform loco-manipulation and self-reconfiguration. We then propose a kinetostatic model and conduct a geometrical docking range analysis to characterize the robot's performance. The reconfiguration process and the distinct motion gait for each configuration are also developed, including rolling, crawling, and snake-like undulation. Experimental demonstrations show that both single and multiple connected modules can achieve successful loco-manipulation, adapting effectively to various environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1976-1983"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deterioration-Aware Collaborative Energy-Efficient Batch Scheduling and Maintenance for Unrelated Parallel Machines Based on Improved MOEA/D 基于改进MOEA/D的无关联并行机退化感知协同节能批量调度与维护

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-06 DOI: 10.1109/LRA.2025.3526571

Haixuan Wang;Fei Qiao;Shengxi Jiang;Haibin Zhu;Junkai Wang

{"title":"Deterioration-Aware Collaborative Energy-Efficient Batch Scheduling and Maintenance for Unrelated Parallel Machines Based on Improved MOEA/D","authors":"Haixuan Wang;Fei Qiao;Shengxi Jiang;Haibin Zhu;Junkai Wang","doi":"10.1109/LRA.2025.3526571","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526571","url":null,"abstract":"The deterioration phenomenon is common and lasting as machines' service time increases within energy-intensive manufacturing processes such as heat treatment, which may bring about processes time extension or even the breakdown of a machine. It is crucial to collaboratively optimize batch scheduling and maintenance to ensure stable, efficient production, and achieve energy efficiency. This study takes into account preventive maintenance, where a maintenance activity is carried out after a certain number of batches are processed. A novel multi-objective mixed-integer programming model for unrelated parallel batching machines is proposed to minimize the makespan, total completion time and total energy consumption. The entire problem is broken down into four sub-issues: job division, job dispatching, batch formation and batch sequencing. Given the NP-hard nature of the problem, three heuristic algorithms based on several structural properties are designed according to the features of the latter three parts. Meanwhile, an integrated methodology, a Multi-Objective Evolutionary Algorithm based on Decomposition combined with Variable Neighborhood Search (MOEA/D-VNS), is put forward to handle job division and the multi-dimensional collaborative optimization problem. The performance of the proposed algorithms is compared with that of other typical dominance-based evolutionary algorithms. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and algorithms.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2056-2063"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian NeRF: Quantifying Uncertainty With Volume Density for Neural Implicit Fields 贝叶斯NeRF：用体积密度量化神经隐式场的不确定性

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-06 DOI: 10.1109/LRA.2025.3526572

Sibaek Lee;Kyeongsu Kang;Seongbo Ha;Hyeonwoo Yu

{"title":"Bayesian NeRF: Quantifying Uncertainty With Volume Density for Neural Implicit Fields","authors":"Sibaek Lee;Kyeongsu Kang;Seongbo Ha;Hyeonwoo Yu","doi":"10.1109/LRA.2025.3526572","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526572","url":null,"abstract":"We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF diverges from traditional geometric methods by providing an enriched scene representation, rendering color and density in 3D space from various viewpoints. However, NeRF encounters limitations in addressing uncertainties solely through geometric structure information, leading to inaccuracies when interpreting scenes with insufficient real-world observations. While previous efforts have relied on auxiliary networks, we propose a series of formulation extensions to NeRF that manage uncertainties in density, both color and density, and occupancy, all without the need for additional networks. In experiments, we show that our method significantly enhances performance on RGB and depth images in the comprehensive dataset. Given that uncertainty modeling aligns well with the inherently uncertain environments of Simultaneous Localization and Mapping (SLAM), we applied our approach to SLAM systems and observed notable improvements in mapping and tracking performance. These results confirm the effectiveness of our Bayesian NeRF approach in quantifying uncertainty based on geometric structure, making it a robust solution for challenging real-world scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2144-2151"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0