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arXiv 2024年

作者： Gupta, Hrishikesh Thalhammer, Stefan Weibel, Jean-Baptiste Haberl, Alexander Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department UAS Technikum Vienna TU Wien Austria

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estimation that harnesses the refractive-intermediate representation. Unlike conventional approaches, our method leverages a feature space impervious to changes in RGB image space and independent of depth information. Drawing inspiration from image matting, we model the deformation of the light path through transparent objects, yielding a unique object-specific intermediate representation guided by light refraction that is independent of the environment in which objects are observed. By integrating these intermediate features into the pose estimation network, we show that ReFlow6D achieves precise 6D pose estimation of transparent objects, using only RGB images as input. Our method further introduces a novel transparent object compositing loss, fostering the generation of superior refractive-intermediate features. Empirical evaluations show that our approach significantly outperforms state-of-the-art methods on TOD and Trans32K-6D datasets. Robot grasping experiments further demonstrate that ReFlow6D’s pose estimation accuracy effectively translates to real-world robotics task. The source code is available at: https://***/StoicGilgamesh/ReFlow6D and https://***/StoicGilgamesh/matting *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Light refraction

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Asad Ali Sarah Gul Tallat Mahmood Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) Rawalpindi Pakistan Department of Electrical Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.

关键词： Deep learning Location awareness Industries Uncertainty Service robots Computational modeling Reinforcement learning

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Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

arXiv

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arXiv 2024年

作者： Dai, Wei Liu, Rui Wu, Zixuan Wu, Tianyi Wang, Min Zhou, Junxian Yuan, Yixuan Liu, Jun The Centre for Robotics and Automation City University of Hong Kong Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong

Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medical objects, analyzing small areas in medical images remains challenging. This difficulty arises due to information losses and compression defects from convolution and pooling operations in CNNs, which become more pronounced as the network deepens, especially for small medical objects. To address these challenges, we propose a novel scale-variant attention-based network (SvANet) for accurately segmenting small-scale objects in medical images. The SvANet consists of scale-variant attention, cross-scale guidance, Monte Carlo attention, and vision transformer, which incorporates cross-scale features and alleviates compression artifacts for enhancing the discrimination of small medical objects. Quantitative experimental results demonstrate the superior performance of SvANet, achieving 96.12%, 96.11%, 89.79%, 84.15%, 80.25%, 73.05%, and 72.58% in mean Dice coefficient for segmenting kidney tumors, skin lesions, hepatic tumors, polyps, surgical excision cells, retinal vasculatures, and sperms, which occupy less than 1% of the image areas in KiTS23, ISIC 2018, ATLAS, PolypGen, TissueNet, FIVES, and SpermHealth datasets, respectively. © 2024, CC BY.

关键词： Convolutional neural networks

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Integrated Planning and Control of Robotic Surgical Instruments for Tasks Autonomy

arXiv

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arXiv 2023年

作者： Zhong, Fangxun Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instruments for pipeline automation of surgical tasks. We leverage the geometry of a robotic instrument and propose the nodal state space (NSS) to represent the robot state in SE(3) space. Each elementary robot motion could be encoded by regulation of the state parameters via a dynamical system. This theoretically ensures that every in-process trajectory is globally feasible and stably reached to an admissible target, and the controller is of closed-form without computing 6-DoF inverse kinematics. Then, to plan the motion steps reliably, we propose an interactive (instant) goal state of the robot that transforms manipulation planning through desired path constraints into a goal-varying manipulation (GVM) problem. We detail how GVM could adaptively and smoothly plan the procedure (could proceed or rewind the process as needed) based on on-the-fly situations under dynamic or disturbed environment. Finally, we extend the above policy to characterize complete pipelines of various surgical tasks. Simulations show that our framework could smoothly solve twisted maneuvers while avoiding collisions. Physical experiments using the da Vinci Research Kit (dVRK) validates the capability of automating individual tasks including tissue debridement, dissection, and wound suturing. The results confirm good task-level consistency and reliability compared to state-of-the-art automation algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic surgery

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Making Biometric Identification with EMG Easier: Using a Single Channel for Specific Purposes

Making Biometric Identification with EMG Easier: Using a Sin...

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International Conference on Advanced Electrical Engineering (ICAEE)

作者： Mehdi Chames Eddinne Layes Safia Benahmed Omar Kermia Electronics department Ecole National Polytechnique d'Alger Algiers Algeria Robotics and industrial automation Center for development of advanced technologies Algiers Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

EMG has become a powerful method that was first used to identify muscle disorders in medical settings. Now, EMG signals are being used in a new way to identify people. Most biometric methods use complex systems with many channels, which makes them hard to use in real life. This study challenges this idea by suggesting that identification can be done with just one EMG channel for a smaller group of people. We explain how to collect, preprocess, and enhance data for EMG-based identification. We use a stacked Long Short-Term Memory (LSTM) neural network to classify EMG signals accurately and effectively. Our results show high accuracy and F1 scores equal to 96.4, which means our approach works well. This research not only improves EMG-based identification systems but also has potential uses in biometrics and other areas.

关键词： Electrical engineering Accuracy Biological system modeling Neural networks Muscles Biometric identification Electromyography Complex systems Long short term memory

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Deep Learning Based Neovascularization Detection in Color Fundus Photography Image 23

Deep Learning Based Neovascularization Detection in Color Fu...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Institute of Image Processing and Pattern Recognition Department of Automation School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

ISBN: (纸本)9781450399029

Diabetic retinopathy (DR) has already been one of the leading causes of vision loss. A large number of researches about deep learning-based DR screening using color retinal photography images have been proposed in recent years. However, existing works mainly concentrated on non-proliferative diabetic retinopathy (NPDR) lesions. The exploration of proliferative diabetic retinopathy (PDR) lesion, which is more serious, is still insufficient. In this paper, we explored into one typical PDR lesion named neovascularization (NV). We collected and annotated retinal images from public and private datasets to construct and release a new dataset for NV detection. Three tasks including segmentation, detection, and grading were conducted on our dataset by using a set of state-of-the-art deep learning models. The results show that introducing NV detection can benefit DR grading. However, the segmentation and detection of NV are still challenging and have considerable room for improvement. © 2023 Copyright held by the owner/author(s).

关键词： Color

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Teleoperated Navigation of a Robotically Steerable Guidewire under Fluoroscopy in a Flow Environment

Teleoperated Navigation of a Robotically Steerable Guidewire...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Timothy A. Brumfiel Sharan R. Ravigopal Namrata U. Nayar Mesum Zaidi Jaydev P. Desai Wallace H. Coulter Department of Biomedical Engineering Medical Robotics and Automation (RoboMed) Laboratory Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

A chronic total occlusion (CTO) is a complete blockage that impedes blood flow in the coronary artery. Minimally invasive procedures for treating CTOs typically involve manually steering a guidewire to the occlusion. However, navigating the guidewire throughout arteries can be challenging due to bifurcations and narrow vessels. This paper introduces a novel robotic guidewire design that is specifically engineered to navigate the aortic bifurcation, into peripheral branches of the right common iliac artery. We present a detailed description of the design and outline the procedure for determining the notch parameters to reduce motion coupling between the flexible joints. Lastly, we demonstrate remote control of the guidewire as it traverses through a phantom vasculature under different pulsatile flow rates to validate the feasibility of the guidewire.

关键词： Couplings Minimally invasive surgery Navigation Phantoms Bifurcation Engineering in medicine and biology Arteries Robots Remote control Blood flow

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Performance Analysis of Probabilistic Roadmaps in Simulation for Mobile Robot Path Planning

SSRN

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SSRN 2023年

作者： Kondapally, Karthik Krishna Srivatsava Deepak, Hari N. Anbarasi, M.P. PSG Institue of Advanced Studies Coimbatore India Department of Robotics and Automation PSG College of Technology Coimbatore India

Many challenges, including path-planning for a mobile robot, revolve around finding the lowest-cost path through a graph. A quick path-planning algorithm for a mobile robot in a dynamic environment is proposed by integrating Dijkstra's algorithm. Dijkstra's method and Probabilistic Roadmaps are used to determine an initial path from the starting point to the goal. This method searches for an optimal path from the robot's current location to this local target, and as the number of nodes increases, a new path that brings the robot from the current place to the goal is obtained. Meanwhile, the re-planning time is substantially reduced. The simulation results validate the suggested algorithm's feasibility and effectiveness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Quantitative Progress Evaluation of Post-stroke Patients Using a Novel Bimanual Cable-driven Robot

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Journal of Bionic Engineering 2021年第6期18卷 1331-1343页

作者： Thiago Alves Rogério Sales Gonçalves Giuseppe Carbone Laboratory of Automation and Robotics School of Mechanical EngineeringFederal University of UberlândiaUberlândia38400-902Brazil Department of Mechanical Energy and Management EngineeringUniversity of Calabria87036RendeItaly

Rehabilitation is the most effective way to reduce motor impairments in post-stroke *** process demands several hours with a specialized *** resources and personnel shortages,the literature reports a high interest in robotic assisted rehabilitation ***,cable-driven robotic architectures are attracting significant research interest for post-stroke ***,the existing cable-driven robots are mostly unilateral devices allowing the rehabilitation only of the most affected *** leaves unaddressed the rehabilitation of bimanual activities,which are predominant within the common Activities of Daily Living(ADL).Thus,this paper presents a specific novel design to achieve bimanual rehabilitation tasks that has been named as BiCAR ***,this paper provides a full insight on the BiCAR system as well as on its dedicated developed software *** particular,BiEval software has been developed as based on a serious game strategy and a virtual reality environment to track the patient exercising duration,motion ranges,speeds,and forces over time for achieving a quantitative assessment of the rehabilitation ***,the paper presents the BiCAR/BiEval capabilities by referring to a pilot Randomized Controlled Trial(RCT).The clinical trials have been used to validate the BiCAR/BiEval in terms of engineering feasibility and user acceptance to achieve an innovative cost-oriented integrated hardware/software device for the bimanual assistive rehabilitation of post-stroke patients.

关键词： Cable-driven robots Low cost Stroke rehabilitation Bimanual Progress tracking Serious games

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BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration

arXiv

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arXiv 2023年

作者： Liu, Junjia Sim, Hengyi Li, Chenzui Chen, Fei The Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

Human bimanual manipulation can perform more complex tasks than a simple combination of two single arms, which is credited to the spatio-temporal coordination between the arms. However, the description of bimanual coordination is still an open topic in robotics. This makes it difficult to give an explainable coordination paradigm, let alone applied to robotics. In this work, we divide the main bimanual tasks in human daily activities into two types: leader-follower and synergistic coordination. Then we propose a relative parameterization method to learn these types of coordination from human demonstration. It represents coordination as Gaussian mixture models from bimanual demonstration to describe the change in the importance of coordination throughout the motions by probability. The learned coordinated representation can be generalized to new task parameters while ensuring spatio-temporal coordination. We demonstrate the method using synthetic motions and human demonstration data and deploy it to a humanoid robot to perform a generalized bimanual coordination motion. We believe that this easy-to-use bimanual learning from demonstration (LfD) method has the potential to be used as a data augmentation plugin for robot large manipulation model training. The corresponding codes are open-sourced in https://***/Skylark0924/Rofunc. Copyright © 2023, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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