2023 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第2页

Collaborative Control Based on Payload- leading for the Multi-quadrotor Transportation Systems 基于载荷领先的多四旋翼运输系统协同控制

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161414

Yuan Ping, Mingming Wang, Juntong Qi, Chong Wu, Jinjin Guo

引用次数: 1

Differential Dynamic Programming based Hybrid Manipulation Strategy for Dynamic Grasping 基于差分动态规划的动态抓取混合操作策略

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160817

Cheng Zhou, Yanbo Long, Lei Shi, Longfei Zhao, Yu Zheng

{"title":"Differential Dynamic Programming based Hybrid Manipulation Strategy for Dynamic Grasping","authors":"Cheng Zhou, Yanbo Long, Lei Shi, Longfei Zhao, Yu Zheng","doi":"10.1109/ICRA48891.2023.10160817","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160817","url":null,"abstract":"To fully explore the potential of robots for dexterous manipulation, this paper presents a whole dynamic grasping process to achieve fluent grasping of a target object by the robot end-effector. The process starts from the phase of approaching the object over the phases of colliding with the object and letting it roll about the colliding point to the final phase of catching it by the palm or grasping it by the fingers of the end-effector. We derive a unified model for this hybrid dynamic manipulation process embodied as approaching-colliding-rolling-catching/grasping from the spatial vector based articulated body dynamics. Then, the whole process is formulated as a free-terminal constrained multi-phase optimal control problem (OCP). We extend the traditional differential dynamic programming (DDP) to solving this free-terminal OCP, where the backward pass of DDP involves constrained quadratic programming (QP) problems and we solve them by the primal-dual Augmented Lagrangian (PDAL) method. Simulations and real experiments are conducted to show the effectiveness of the proposed method for robotic dynamic grasping.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124922919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization 预测模型的概率不确定性量化及其在视觉定位中的应用

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160298

Junan Chen, Josephine Monica, Wei-Lun Chao, Mark E. Campbell

{"title":"Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization","authors":"Junan Chen, Josephine Monica, Wei-Lun Chao, Mark E. Campbell","doi":"10.1109/ICRA48891.2023.10160298","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160298","url":null,"abstract":"The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where we predict locations from images. Our proposed framework estimates probabilistic uncertainty by creating a sensor error model that maps an internal output of the prediction model to the uncertainty. The sensor error model is created using multiple image databases of visual localization, each with ground-truth location. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting, weather (sunny, snowy, night), and alignment errors between databases. We analyze both the predicted uncertainty and its incorporation into a Kalman-based localization filter. Our results show that prediction error variations increase with poor weather and lighting condition, leading to greater uncertainty and outliers, which can be predicted by our proposed uncertainty model. Additionally, our probabilistic error model enables the filter to remove ad hoc sensor gating, as the uncertainty automatically adjusts the model to the input data.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125091006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Plane Extraction and Modeling for Active Visual-Inertial Mapping 主动视觉惯性映射的概率平面提取与建模

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160792

Mitchell Usayiwevu, F. Sukkar, Teresa Vidal-Calleja

{"title":"Probabilistic Plane Extraction and Modeling for Active Visual-Inertial Mapping","authors":"Mitchell Usayiwevu, F. Sukkar, Teresa Vidal-Calleja","doi":"10.1109/ICRA48891.2023.10160792","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160792","url":null,"abstract":"This paper presents an active visual-inertial mapping framework with points and planes. The key aspect of the proposed framework is a novel probabilistic plane extraction with its associated model for estimation. The approach allows the extraction of plane parameters and their uncertainties based on a modified version of PlaneRCNN [1]. The extracted probabilistic plane features are fused with point features in order to increase the robustness of the estimation system in texture-less environments, where algorithms based on points alone would struggle. A visual-inertial framework based on Iterative Extended Kalman filter (IEKF) is used to demonstrate the approach. The IEKF equations are customized through a measurement extrapolation method, which enables the estimation to handle the delay introduced by the neural network inference time systematically. The system is encompassed within an active mapping framework, based on Informative Path Planning to find the most informative path for minimizing map uncertainty in visual-inertial systems. The results from the conducted experiments with a stereo/IMU system mounted on a robotic arm show that introducing planar features to the map, in order to complement the point features in the state estimation, improves robustness in texture-less environments.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125191323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Domain-specific languages for kinematic chains and their solver algorithms: lessons learned for composable models 运动链及其求解器算法的领域特定语言:可组合模型的经验教训

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160474

Sven Schneider, N. Hochgeschwender, H. Bruyninckx

{"title":"Domain-specific languages for kinematic chains and their solver algorithms: lessons learned for composable models","authors":"Sven Schneider, N. Hochgeschwender, H. Bruyninckx","doi":"10.1109/ICRA48891.2023.10160474","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160474","url":null,"abstract":"The Unified Robot Description Format (URDF) and, to a lesser extent, the COLLAborative Design Activity (COLLADA) format are two of the most popular domain-specific languages (DSLs) to represent kinematic chains in robotics with support in many tools including Gazebo, MoveIt!, KDL or IKFast. In this paper we analyse both DSLs with respect to their structure and semantics as seen by tools that produce or consume such representations. For the former, we notice a tight coupling of various unrelated domains like kinematics and dynamics with visualisation, control or even specific simulators. For the latter, a key insight is that both DSLs target human developers and leave important design decisions like the choice of joint attachment frames implicit or hidden in the documentation. The lessons learned from this analysis guide us to an improved interchange format by designing composable, loosely coupled models with complete metamodels that unambiguously define the model semantics. We substantiate our findings with concrete examples. Furthermore, we compose solver algorithms on top of the kinematic chain representation. As a consequence of the above analysis and decomposition we can systematically apply structure- and semantics-conserving model-to-code transformations to those algorithms.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125786500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Balance for Legged Robots Using Reaction Wheels 利用反作用轮增强有腿机器人的平衡能力

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160833

Chia-Yen Lee, Shuo Yang, Benjamin Bokser, Zachary Manchester

引用次数: 1

Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering 基于深度变分聚类的专家不可知超声图像质量评估

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160435

Deepak Raina, Dimitrios Ntentia, S. Chandrashekhara, R. Voyles, S. Saha

{"title":"Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering","authors":"Deepak Raina, Dimitrios Ntentia, S. Chandrashekhara, R. Voyles, S. Saha","doi":"10.1109/ICRA48891.2023.10160435","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160435","url":null,"abstract":"Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods. The project page with source codes is available at https://sites.google.com/view/US2QNet.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125826472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A congestion-aware path planning method considering crowd spatial-temporal anomalies for long-term autonomy of mobile robots 一种考虑人群时空异常的移动机器人长期自主的拥挤感知路径规划方法

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160252

Zijian Ge, Jingjing Jiang, M. Coombes

引用次数: 0

UPLIFT: Unsupervised Person Labeling and Identification via Cooperative Learning with Mobile Robots 隆起:通过与移动机器人合作学习的无监督人员标记和识别

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161103

Y. Tseng, Ting-Yuan Ke, Fang-jing Wu

引用次数: 0

Optimal Scheduling of Models and Horizons for Model Hierarchy Predictive Control 模型层次预测控制的模型最优调度与视野

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160528

C. Khazoom, Steve Heim, Daniel Gonzalez-Diaz, Sangbae Kim

{"title":"Optimal Scheduling of Models and Horizons for Model Hierarchy Predictive Control","authors":"C. Khazoom, Steve Heim, Daniel Gonzalez-Diaz, Sangbae Kim","doi":"10.1109/ICRA48891.2023.10160528","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160528","url":null,"abstract":"Model predictive control (MPC) is a powerful tool to control systems with non-linear dynamics and constraints, but its computational demands impose limitations on the dynamics model used for planning. Instead of using a single complex model along the MPC horizon, model hierarchy predictive control (MHPC) reduces solve times by planning over a sequence of models of varying complexity within a single horizon. Choosing this model sequence can become intractable when considering all possible combinations of reduced order models and prediction horizons. We propose a framework to systematically optimize a model schedule for MHPC. We leverage trajectory optimization (TO) to approximate the accumulated cost of the closed-loop controller. We trade off performance and solve times by minimizing the number of decision variables of the MHPC problem along the horizon while keeping the approximate closed-loop cost near optimal. The framework is validated in simulation with a planar humanoid robot as a proof of concept. We find that the approximated closed-loop cost matches the simulated one for most of the model schedules, and show that the proposed approach finds optimal model schedules that transfer directly to simulation, and with total horizons that vary between 1.1 and 1.6 walking steps.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123400715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1