检索结果-内蒙古大学图书馆

Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

CAAI Transactions on Intelligence Technology 2024年第3期9卷 738-755页

作者： Min Zhao Guoyu Zuo Shuangyue Yu Daoxiong Gong Zihao Wang Ouattara Sie Intelligent Robotics Laboratory Faculty of Information TechnologyBeijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent Systems BeijingChina Laboratory of Biomechatronics and Intelligent Robotics(BIRO) Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in *** effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in ***,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping *** addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered *** further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end *** that the authors’system can be robustly applied to real-world use and extended to novel *** material is available at https://***/NhG\_k5v3NnM}{https://***/NhG\_k5v3NnM.

关键词： deep learning deep neural networks intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers 46

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Needle Manipulation Controller Subject to Uncertainty of Tip Pose Feedback: a Simulation Study

Evaluation of Needle Manipulation Controller Subject to Unce...

引用

International Symposium on Medical robotics (ISMR)

作者： Junling Mei Chang Chang Yanzhou Wang Simon Leonard Iulian Iordachita Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Mechanical Engineering Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

In this paper, we evaluate the performance of our controller for flexible needle manipulation for percutaneous interventions in a finite element (FE) simulator. We investigate the use of electromagnetic (EM) tracking as needle tip pose feedback, and how artificial sensor noises can affect tracking performance of the controller. In our simulated study, the control system shows high targeting accuracy and robustness with an overall tip position error of 0.49 mm. The addition of needle tip orientation feedback further improves the targeting accuracy for deeper targets, with average error of 0.81 mm when only using position feedback, and 0.55 mm when using additional orientation feedback.

关键词： Target tracking Accuracy Uncertainty Medical robotics Noise Needles Robot sensing systems Robustness Finite element analysis Electromagnetics

来源：评论

学校读者我要写书评

暂无评论

An Industrial Bin Picking Framework for Assembly Tasks

An Industrial Bin Picking Framework for Assembly Tasks

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Liang, Jizhong Sun, Han Chen, Xinhao Gu, Yuanze Cao, Qixin Shanghai Jiaotong University School of Mechanical Engineering Shanghai200240 China Johns Hopkins University Laboratory for Computational Sensing and Robotics BaltimoreMD21218 United States

ISBN: (纸本)9798350325706

The majority of current bin picking systems, designed for industrial parts, cannot be directly oriented to the downstream task after grasping. This research presents a grasping framework that addresses this challenge by incorporating pose estimation of parts in cluttered bin environments and the targeted design of robot end-effector grippers. This approach ensures that the pose of the part on the gripper is known and fixed, enabling successful assembly tasks in various scenarios. To train an object pose estimation network, we propose a system for generating a dataset of industrial parts using model rendering within a physics engine. We analyze the geometric features of the parts, and further design a gripper, to achieve the grasping strategy. Results demonstrate that for a single known industrial part, the minimum grasping success rate is 91.4% in simulated robot experiments, and the assembly success rates in different scenarios based on this framework exceed 80%. Our framework offers valuable guidance for the deployment of robotic grasping. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning

Non-Prehensile Aerial Manipulation using Model-Based Deep Re...

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： Cora A. Dimmig Marin Kobilarov Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics (LCSR) Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

With the continual adoption of Uncrewed Aerial Vehicles (UAVs) across a wide-variety of application spaces, robust aerial manipulation remains a key research challenge. Aerial manipulation tasks require interacting with objects in the environment, often without knowing their dynamical properties like mass and friction a priori. Additionally, interacting with these objects can have a significant impact on the control and stability of the vehicle. We investigated an approach for robust control and non-prehensile aerial manipulation in unknown environments. In particular, we use model-based Deep Reinforcement Learning (DRL) to learn a world model of the environment while simultaneously learning a policy for interaction with the environment. We evaluated our approach on a series of push tasks by moving an object between goal locations and demonstrated repeatable behaviors across a range of friction values.

关键词： Space vehicles Robust control Friction Geometric modeling Deep reinforcement learning Stability analysis Robustness Planning Standards Robots

来源：评论

学校读者我要写书评

暂无评论

Minimally Invasive Flexible Needle Manipulation Based on Finite Element Simulation and Cross Entropy Method

arXiv

引用

arXiv 2024年

作者： Wang, Yanzhou Chang, Chang Mei, Junling Leonard, Simon Iordachita, Iulian Department of Mechanical Engineering The Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Department of Computer Science The Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement the control framework for better tracking performance. We show how electromagnetic (EM) tracking can be readily incorporated into the framework to provide controller feedback. Tissue phantom experiment with EM tracking shows the average targeting error is 0.16 ± 0.29mm. © 2024, CC BY-NC-ND.

关键词： Needles

来源：评论

学校读者我要写书评

暂无评论

Design and Evaluation of a Needle Manipulation System with EM Tracking for CT-Guided Spinal Injections

Design and Evaluation of a Needle Manipulation System with E...

引用

International Symposium on Medical robotics (ISMR)

作者： Chang Chang Junling Mei Yanzhou Wang Lidia Al-Zogbi Simon Leonard Amit Jain Iulian Iordachita Department of Mechanical Engineering Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Orthopaedic Surgery Johns Hopkins University School of Medicine Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

This paper presents a flexible needle guidance system and its workflow that enables registration of computed tomography (CT) and electromagnetic (EM) tracking systems with a finite element (FE) simulator for needle-based percutaneous spinal injections. CT is used only pre- and postoperatively for surgical planning and confirmation, while EM tracking is combined intraoperatively with an FE-based needle controller to track the planned needle trajectory and avoid obstacles. Evaluation of the proposed system using a multi-layer soft tissue phantom shows an average targeting accuracy of 0.4 mm.

关键词： Target tracking Medical robotics Computed tomography Biological tissues Phantoms Needles Trajectory Planning Finite element analysis Electromagnetics

来源：评论

学校读者我要写书评

暂无评论

Automatic search for photoacoustic marker using automated transrectal ultrasound

引用

Biomedical Optics Express 2023年第11期14卷 6016-6030页

作者： Wu, Zijian Moradi, Hamid Yang, Shuojue Song, Hyunwoo Boctor, Emad M. Salcudean, Septimiu E. Johns Hopkins University Laboratory for Computational Sensing and Robotics BaltimoreMD United States University of British Columbia Dept. of Electrical and Computer Engineering VancouverBC Canada University of Texas at Austin Walker Department of Mechanical Engineering AustinTX United States Johns Hopkins University Dept. of Computer Science BaltimoreMD United States

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [IEEE Robot. Autom. Lett 8, 1287 (2023).]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53°±0.30° average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29◦±0.31◦ and 0.48°±0.32° average errors after 28 data acquisitions. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Urology

来源：评论

学校读者我要写书评

暂无评论

Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues

Bevel-Tip Needle Deflection Modeling, Simulation, and Valida...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Yanzhou Wang Lidia Al-Zogbi Guanyun Liu Jiawei Liu Junichi Tokuda Axel Krieger Iulian Iordachita Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Mechanical and Aerospace Engineering University of Florida Ganesville USA Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes as an effective alternative to more invasive surgical procedures. However, the outcome of needle-based approaches relies heavily on the accuracy of needle placement, which remains a challenge even with robot assistance and medical imaging guidance due to needle deflection caused by contact with soft tissues. In this paper, we present a novel mechanics-based 2D bevel-tip needle model that can account for the effect of nonlinear strain-dependent behavior of biological soft tissues under compression. Real-time finite element simulation allows multiple control inputs along the length of the needle with full three-degree-of-freedom (DOF) planar needle motions. Cross-validation studies using custom-designed multi-layer tissue phantoms as well as heterogeneous chicken breast tissues result in less than 1mm in-plane errors for insertions reaching depths of up to 61 mm, demonstrating the validity and generalizability of the proposed method.

关键词： Accuracy Biological system modeling Biological tissues Phantoms Breast Needles Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Model Predictive Path Integral Control of I2RIS Robot Using RBF Identifier and Extended Kalman Filter

arXiv

引用

arXiv 2025年

作者： Esfandiari, Mojtaba Du, Pengyuan Wei, Haochen Gehlbach, Peter Munawar, Adnan Kazanzides, Peter Iordachita, Iulian Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States

Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (I2RIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models’ challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models’ performance during snake robots’ interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the I2RIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm. © 2025, CC BY.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：