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Bridging theory and practice: A comprehensive algorithm for imageless total knee arthroplasty

Computers in Biology and Medicine 2024年 177卷 108662-108662页

作者： Sohail, Muhammad Kim, Heung Soo Department of Mechanical Robotics and Energy Engineering Dongguk University-Seoul 30 Pildong-ro 1-gil Jung-gu Seoul04620 Korea Republic of

Total knee arthroplasty (TKA) is a surgical procedure to treat severe knee osteoarthritis. Among several techniques available for performing TKA, imageless TKA is known for achieving precise alignment while minimizing invasiveness. This work proposes a comprehensive algorithm for imageless TKA device to calculate the varus/valgus and flexion/extension angles, as well as resection depths for cutting planes at distal femur and proximal tibia. Moreover, the algorithm calculates the hip-knee-ankle (HKA) and flexion angles of the leg. Initially, the proposed algorithm was validated in a virtual environment using a CT-scanned bone model in Solidworks. Subsequently, for the real-world validation, a SoftBone model was resected with conventional intra and extramedullary rods and cross-checked with the proposed algorithm. For the third validation, another SoftBone model was resected with the proposed algorithm and cuts were measured with a vernier caliper. During this experiment, there was an error of approximately 1 mm for both femoral and tibial resection cases when using an infrared camera with an accuracy of ±0.5 mm. However, this error could be reduced using an infrared camera with higher accuracy. © 2024 Elsevier Ltd

关键词： Bone

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Conservatism-Relaxed Disturbance Observer-based Composite Control for Wheeled Mobile Robots Subject to Abrupt Disturbance

Conservatism-Relaxed Disturbance Observer-based Composite Co...

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Chinese Control and Decision Conference, CCDC

作者： Hao Wu Shuting Wang Hu Li Hongyang Zhang Tifan Xiong Yuanlong Xie School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Mobile Robotics Research and Technology Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the robust and accurate control problem of wheeled mobile robot (WMR) systems with uncertain disturbance, especially exhibits abrupt changes. To end this, a conservatism-relaxed disturbance observer (CRDOB)-based sliding-mode controller is proposed under a composite control structure. To address the uncertainties of the abrupt disturbance, a CRDOB is designed without requiring any assumptions about disturbance boundary information. With aiding from a continuous gain-switching mechanism with reducing chattering of the SMC, a composite controller is developed to compensate for the residual-disturbance estimation error of the CRDOB, and then avoid large errors when disturbances instantaneous change. Simulations and experiments demonstrate the improvement of the tracking control performance and system robustness.

关键词： Couplings Adaptation models Estimation error Uncertainty Upper bound Prototypes Switches

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Anomalous properties of spark plasma sintered boron nitride solids

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Materials Today 2025年 86卷 42-51页

作者： Biswas, Abhijit Serles, Peter Alvarez, Gustavo A. Schimpf, Jesse Hache, Michel Kong, Jonathan Demingos, Pedro Guerra Yuan, Bo Pieshkov, Tymofii S. Li, Chenxi Puthirath, Anand B. Gao, Bin Gray, Tia Zhang, Xiang Murukeshan, Jishnu Vajtai, Robert Dai, Pengcheng Singh, Chandra Veer Howe, Jane Zou, Yu Martin, Lane W. Clancy, James Patrick Tian, Zhiting Filleter, Tobin Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Department of Mechanical & Industrial Engineering University of Toronto 5 King's College Road TorontoM5S 3G8 Canada Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Department of Materials Science and Engineering University of California Berkeley94720 United States Department of Materials Science and Engineering University of Toronto 184 College St TorontoM5S 3E4 Canada Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley94720 United States

Hexagonal boron nitride (h-BN) is a brittle ceramic with a layered structure, however, recent experiments have suggested that inter-layer structural engineering could be key to new structural and functional properties. Here we report the scalable bulk synthesis of high-density crystalline h-BN solids, by using high-temperature spark plasma sintering (SPS) of h-BN powders, which show high values of mechanical strength, ductility, dielectric constant, thermal conductivity, and exceptional neutron radiation shielding capability. Through exhaustive characterizations we reveal that SPS induces non-basal plane crystallinity, twisting of layers, and facilitates inter-grain fusion with a high degree of in-plane alignment across macroscale dimensions, resulting in near-theoretical density and improved properties. Our findings highlight the importance of material design, via new approaches such as layer twisting and interlayer interconnections, to create novel ceramics with properties that could go beyond their intrinsic limits. © 2025

关键词： Ductility

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Control of Continuum Manipulators with Shape Constraints via Deep Reinforcement Learning

Control of Continuum Manipulators with Shape Constraints via...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Nima Maghooli Omid Mahdizadeh Mohammad Bajelani S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Continuum robots, while versatile for handling complex tasks, present significant challenges in control system design. This paper introduces a novel framework that integrates position and orientation control laws to address shape constraints effectively. Specifically, a Deep Reinforcement Learning (DRL) strategy is proposed to facilitate trajectory tracking within the desired orientation. To ensure safety, the framework directly controls the end-effector's position and orientation in the workspace, avoiding unsafe zones. A centralized control law, the Modified Transpose Jacobian (MTJ), is employed to resolve the robot's redundancy without relying on inverse kinematics. The proposed approach is validated through simulations on a Tendon-Driven Continuum Robot (TDCR), demonstrating superior performance compared to similar learning-based controllers.

关键词： Meters Mechatronics Shape Trajectory tracking Termination of employment Position control Kinematics Deep reinforcement learning Safety Continuum robots

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Experimental study on gas production and solution composition during the interaction of femtosecond laser pulse and liquid

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Chinese Physics B 2022年第7期31卷 17-22页

作者： Yichun Wang Han Wu Wenkang Lu Meng Li Ling Tao Xiuquan Ma The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and Technology(HUST)Wuhan 430074China Guangdong Intelligent Robotics Institute Dongguan 523808China Department of Cardiology Xijing HospitalThe Fourth Military Medical UniversityXi’an 710032China

The process of ionizing normal saline induced by femtosecond laser is studied from the perspective of gas production rate and *** the repetition rate is less than 1000 Hz,each laser pulse independently generates ionization *** the same time,we discovered the inhibitory effect of meglumini diatrizoici on the ionization process and explained the reasons for this ***,the gas composition proved that the primary gas production mechanism of the femtosecond laser is the decomposition of water molecular,and the composition of the solution after the reaction proved the decomposition effect of the laser on meglumine.

关键词： femtosecond laser ionization gas production rate meglumini diatrizoici

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Data-Driven Control of Highly Deformable Robotic Endoscopes Using Proprioceptive Shape Feedback

Data-Driven Control of Highly Deformable Robotic Endoscopes ...

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IEEE International Conference on robotics and Biomimetics

作者： Yiang Lu Jixuan Zheng Yun-Hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this paper, we introduce a versatile data-driven approach for servo-controlling the highly deformable robotic endoscope equipped with Draw Tower Gratings (DTGs). The advancement of perception and decision-making technologies is pivotal for enabling flexible robots to execute automatic procedures. However, modeling these robots presents a significant challenge due to their large deformations and susceptibility to environmental disturbances. To address this issue, we propose a robust adaptive control framework that leverages DTG-based proprioceptive shape feedback. This automatic paradigm can online estimate the unknown deformation model of the flexible robot based on function approximation (FA), while also accounting for unknown perturbations without prior identification. By employing the proposed FA-based adaptive algorithm, we can ensure the convergence of the shape control error. Experimental results validating a highly deformable flexible robot embedded with DTG sensors showcase the efficiency of the proposed control strategy in unknown scenarios.

关键词： Deformable models Adaptation models Optical fiber sensors Shape control Shape Endoscopes Deformation Perturbation methods Propioception Sensors

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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 ...

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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

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Fleet Sizing for the Flash Delivery Problem from Multiple Depots a Case Study in Amsterdam

arXiv

引用

arXiv 2023年

作者： Kronmueller, Maximilian Fielbaum, Andres Alonso-Mora, Javier Department of Cognitive Robotics the Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands

In this paper, we present a novel approach for fleet sizing in the context of flash delivery, a time-sensitive delivery service that requires the fulfilment of customer requests in minutes. Our approach effectively combines individual delivery requests into groups and generates optimized operational plans that can be executed by a single vehicle or autonomous robot. The groups are formed using a modified routing approach for the flash delivery problem. Combining the groups into operational plans is done by solving an integer linear problem. To evaluate the effectiveness of our approach, we compare it against three alternative methods: fixed vehicle routing, non-pooled deliveries and a strategy encouraging the pooling of requests. The results demonstrate the value of our proposed approach, showcasing its ability to optimize the fleet and improve operational efficiency. Our experimental analysis is based on a real-world dataset provided by a Dutch retailer, allowing us to gain valuable insights into the design of flash delivery operations and to analyze the effect of the maximum allowed delay, the number of stores to pick up goods from and the employed cost functions. © 2023, CC BY-NC-ND.

关键词： Vehicle routing

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Learning Vehicle Dynamics from Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains

arXiv

引用

arXiv 2023年

作者： Lee, Jeong Hyun Choi, Jinhyeok Ryu, Simo Oh, Hyunsik Choi, Suyoung Hwangbo, Jemin Robotics and Artificial Intelligence Lab The Department of Mechanical Engineering KAIST Daejeon34141 Korea Republic of

In the realm of autonomous mobile robots, safe navigation through unpaved outdoor environments remains a challenging task. Due to the high-dimensional nature of sensor data, extracting relevant information becomes a complex problem, which hinders adequate perception and path planning. Previous works have shown promising performances in extracting global features from full-sized images. However, they often face challenges in capturing essential local information. In this paper, we propose Crop-LSTM, which iteratively takes cropped image patches around the current robot’s position and predicts the future position, orientation, and bumpiness. Our method performs local feature extraction by paying attention to corresponding image patches along the predicted robot trajectory in the 2D image plane. This enables more accurate predictions of the robot’s future trajectory. With our wheeled mobile robot platform Raicart, we demonstrated the effectiveness of Crop-LSTM for point-goal navigation in an unpaved outdoor environment. Our method enabled safe and robust navigation using RGBD images in challenging unpaved outdoor terrains. The summary video is available at https://***/iIGNZ8ignk0. Copyright © 2023, The Authors. All rights reserved.

关键词： Crops

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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