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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Milliken, Lauren Hollinger, Geoffrey A. Robotics Program School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR United States

ISBN: (纸本)9781509046331

In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Process (POMDP) to represent a model of the user's expertise in controlling the robot. The POMDP uses observations from the user's actions and from the environment to update the belief of the user's skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. A user study was run to test the performance of our formulation. Users drive a simulated robot through an obstacle-filled map while the POMDP model chooses appropriate macro-action controllers based on the belief state of the user's skill level. The results of the user study show that our model can encapsulate user skill. The results also show that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill. © 2017 IEEE.

关键词： Controllers

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Development of a Transformable Self-Balancing Mobile Robot

Development of a Transformable Self-Balancing Mobile Robot

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International Conference on Control, Automation and Systems

作者： Jinkwang Kim Geonhoo Kim Taewoo Han Seungwon Heo Yongjae Kim Hyun Myung Department of the Robotics Program KAIST Daejeon Korea Department of Electronics Engineering Korea University of Technology and Education(Koreatech) Cheonan Korea Department of Civil and Environmental Engineering KAIST Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

This paper reports the design, construction, and control of a mobile robot that can be transformed from the four-wheel mobile robot into two-wheel self-balancing robot and vice versa. The hardware of the robot utilizes the mechanism of the three-link manipulator. This robot is composed of three components;the body part, the middle link, and the top part. The system architecture comprises a pair of DC motor controllers to move the wheel, two servo motors to move the middle link and the top part, and an Arduino microcontroller board, etc. When the robot is transformed to the two-wheel self-balancing robot, the COM (Center of Mass) equation, and the IMU (Inertial Measurement Unit) sensor are employed for attitude determination. The method of the control is based on a proportional-integral-differential (PID) control. The results show the possibility of performing both functions of the four-wheel mobile robot and two-wheel self-balancing robot.

关键词： Three-link manipulator Two-wheel self-balancing robot Center of mass

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Spike-inspired Deep Neural Network Design Using Binary Weight

Spike-inspired Deep Neural Network Design Using Binary Weigh...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Seunghee Lee Kyukwang Kim Jinki Kim Yeeun Kim Hyun Myung Department of Civil and Environmental Engineering Korean Advanced Institute for Science and Technology Daejeon Korea Robotics Program Korean Advanced Institute for Science and Technology Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

Recently, deep learning has achieved great results in many fields such as image classification. However, most general artificial neural networks with deep learning require graphics processing unit (GPUs) because of hard workload and it requires a large amount of power consumption. When we think about humans, we can do a lot of things without consuming a lot of power compared to computers or electric appliances. Human beings or organisms transmit and recognize information through signal transmission between neurons. This study aims to develop a novel deep neural network architecture which simulates the signaling system between biological neurons, unlike conventional neural networks. We propose a novel spike-inspired deep neural network structure with the spike-inspired block using binary weight motivated by spike's on and off mechanism. We have also designed a modified DenseNet architecture consisting of spike-inspired blocks. Our proposed method was tested and validated with MNIST datasets. The obtained results show the potential of a spike-inspired deep neural network.

关键词： Biological neural networks Computer architecture Training Power demand Neurons

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Towards a soft robotic skin for autonomous tissue palpation

Towards a soft robotic skin for autonomous tissue palpation

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Campisano, Federico Ozel, Selim Ramakrishnan, Anand Dwivedi, Anany Gkotsis, Nikolaos Onal, Cagdas D. Valdastri, Pietro STORM Lab Department of Mechanical Engineering Vanderbilt University NashvilleTN United States WPI Soft Robotics Lab Department of Mechanical Engineering and Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States Department of Robotics and Autonomous System University of Leeds Leeds United Kingdom

ISBN: (纸本)9781509046331

Manual palpation is commonly used to localize tumors and other features buried deep inside organs during open surgery. This approach is not feasible in minimally invasive or robotic surgery, as the contact with the tissue is mediated by instruments. To address this problem, we propose a soft robotic skin (SRS) that can be deployed from a small incision and create a stiffness map in a single step. Such a skin is composed of a matrix of soft robotic tactile elements (SRTEs), each one able to expand and record the tissue response during expansion. In this paper, we firstly prove the feasibility of palpation using a single SRTE. Then, we present and test a soft-suction based anchoring mechanism able to keep the SRS in the desired position in contact with the tissue, allowing surgeons to palpate different sides of the organ. Finally, we detail a calibration method for the SRTE, and assess the feasibility of identifying lumps buried inside a soft tissue phantom, and then inside a chicken liver during an ex-vivo trial. Experimental results show that the SRTE was able to differentiate simulated lumps (up to 3.25 mm deep) from healthy tissue in both the phantom and the ex-vivo trials. These results, added to the ability of the suction gripper to compensate for the expansion forces of each SRTE, are paving the way for soft robotic autonomous tools that can be used for intraoperative mapping of tissue cancers. © 2017 IEEE.

关键词： Tissue

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Scalar field reconstruction based on the Gaussian process and adaptive sampling 14

Scalar field reconstruction based on the Gaussian process an...

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14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017

作者： Jonsson, Porsteinn B. Wang, Jeonghyeon Kim, Jinwhan Laboratory Department of Mechanical Engineering KAIST Daejeon34141 Korea Republic of Robotics Program KAIST Daejeon34141 Korea Republic of

ISBN: (纸本)9781509030552

This paper proposes a method of reconstructing a scalar field by adaptively choosing sampling locations and using the measurements obtained from those locations to reconstruct an estimate of the underlying field using Gaussian process regression. Spreading sampling points evenly over the field may not always be effective if the field is not uniformly distributed and the maximum number of measurements is limited. Taking more measurements in regions of large changes in the field than in regions of small changes can give a better estimate than spreading the same number of measurements evenly over the space. The proposed algorithm was tested on a synthetic scalar field and compared to two popular methods of determining sensor placement based on entropy and mutual information from information theory. © 2017 IEEE.

关键词： Gaussian noise (electronic)

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WE-173. Combining excitability and connectivity measures from interictal invasive-EEG estimates the epileptogenic zone, even in the absence of frank epileptiform activity

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Clinical Neurophysiology 2022年 141卷 S81-S82页

作者： Billardello, Roberto Ntolkeras, Georgios Pesce, Matthew Harmon, Joseph Hadjinicolaou, Aristides Madsen, Joseph Taffoni, Fabrizio Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Harvard Medical School Boston USA Research Unit of Advanced Robotics and Human-Centred Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Baystate Children's Hospital Springfield USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Harvard Medical School Boston USA Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Harvard Medical School Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA

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Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Quantification of Extent of Muscle-skin Shifting by Traversa...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Shotaro Okajima Eduardo Iáñez Hiroshi Yamasaki Álvaro Costa García Fady S. Alnajjar Noriaki Hattori Shingo Shimoda Man-Machine Collaboration Research Team Robotics Project Baton Zone Program Science Technology and Innovation Hub RIKEN Nagoya Japan Brain-Machine Interface Systems Lab Miguel Hernández University of Elche Spain Intelligent Behavior Control Unit BTCC CBS RIKEN Nagoya Japan College of IT (CIT) UAE University (UAEU) Global Center for Medical Engineering and Informatics Osaka University

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm.

关键词：

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CWave: High-performance single-source any-angle path planning on a grid

CWave: High-performance single-source any-angle path plannin...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Sinyukov, Dmitry A. Padir, Taskin Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781509046331

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. 'Any-angle' attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A © 2017 IEEE.

关键词： Motion planning

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WE-184. From “irritative zone” to “irritative network”: a novel interictal estimate of the epileptogenic zone using invasive EEG

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Clinical Neurophysiology 2022年 141卷 S86-S86页

作者： Pesce, Matthew Billardello, Roberto Pace, Lavinia Madsen, Joseph Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston USA Research Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Boston USA Harvard Medical School Boston USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Boston USA

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