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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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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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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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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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Investigation on corrosion and tensile Characteristics: Friction stir welding of AA7075 and AA2014

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Materials Today: Proceedings 2022年 66卷 743-748页

作者： Haribalaji, V. Venkatesan, G. Asif.M, Mohammed Pandian, Mani Subbiah, Ram Boopathi, S. Department of Mechanical Engineering Narasu's Sarathy Institute of Technology Tamil Nadu Salem 636455 India Department of Mechanical Engineering Haramaya Institute of Technology Haramaya University Ethiopia Department of Mechanical Engineering Vignan's Lara Institute of Technology and science Guntur- Andrapradesh 522213 India Department of Mechanical Engineering Erode Sengunthar Engineering College Tamil Nadu Perundurai Erode 638 057 India Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Telangana Hyderabad 500090 India Department of Robotics and Automation Muthayammal College of Engineering Tamil Nadu Rasipuram Namakkal 637408 India

In this article, the corrosion and tensile strength characteristics of Friction-stir-Welding(FSW) of AA7075 and AA2014 have been investigated. These Aluminium alloy materials have widely been used in making automotive, aerospace, and nautical industrial components. The nine specimens have been made by tool rotational speed and welding speed using full factorial design. The optimum tool rotational speed and welding speed were predicted using statistical analysis for both maximum tensile strength and minimum corrosion rate. The minimum corrosion rate (805.3 miles/year) was obtained from maximum speed of tool rotation (1400 rpm) and welding speed (60 mm/min);maximum tensile strength (194. 6 MPa) has been attained by moderate tool rotational speed (1200 rpm) and welding speed (45 mm/min). The specimens for the tensile and corrosion tests were prepared with optimum settings. The microstructure of corroded surface had been illustrated using scanning electron microscopy (SEM) images. © 2022

关键词： Microstructure

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Investigations on the Design and Simulation of Smart Irrigation Robot for Nurseries

Investigations on the Design and Simulation of Smart Irrigat...

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Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), International Conference on

作者： B. Abhiram Swati Jha Y. Yogendra Sai Prasad Joshuva Arockia Dhanraj V. Ramkumar Department of Mechatronics Engineering Centre for Automation & Robotics Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The irrigation systems in nurseries can be the cause of various water supply errors- ranging from lack of sufficient water supply to over-watering and imbalance in the water pH levels. Based on the statistics recorded by the department of agriculture and farmers welfare, we have conducted we concluded that the water supply management and plant life maintenance are the major problems. Considering all these real-time errors, we have designed a smart irrigation robot functioning on the inputs from a group of soil moisture sensors which helps eliminate the monotonous task of monitoring and keeping records of the change in soil moisture.

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A dexterous origami-inspired soft (DOIS) robot for objects reorientation and overturn

A dexterous origami-inspired soft (DOIS) robot for objects r...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Chen, Zhi Siu, Shipan Tin, Sinyee Liu, Yukhei Cao, Hanwen Gao, Yuan Zhou, Jianshu Liu, Yunhui Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Center of Logistics Robotics

ISBN: (纸本)9781665436786

Soft robots have presented their potential for dexterous and smooth motion with inherent compliance, compact structure, and easy control over the traditional rigid robots. This paper presents a 3D printed origami-inspired soft robot (DOIS) robot, which is inspired by the zig-zag origami structure, capable of bi-lateral bending and linear extension/contraction in a compact structure. The motion range and dimension of DOIS robot are fully customizable. The actuation and control of DOIS robot are realized by two drive cables. Mounting a dedicated end-effector at the tip, the DOIS robot is capable of tackling challenges where robots need flexible motion such as object reorientation. We present two scenarios, egg reorientation and envelope flipping, to show the superiority of DOIS robot. The design, modeling, and control of DOIS robot are discussed in detail in this paper with related experimental validation. The proposed DOIS robot provides a practical solution for dexterous soft robot design. © 2021 IEEE.

关键词： End effectors

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Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

arXiv

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arXiv 2024年

作者： Liu, Junjia Li, Zhuo Yu, Minghao Dong, Zhipeng Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodimentspecific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

ZS6D: Zero-shot 6D Object Pose Estimation using Vision Trans...

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IEEE International Conference on robotics and automation (ICRA)

作者： Philipp Ausserlechner David Haberger Stefan Thalhammer Jean-Baptiste Weibel Markus Vincze Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department University of Applied Sciences Technikum Vienna Austria

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC- based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to MegaPose, we improve the Average Recall on all three datasets and compared to OSOP we improve on two datasets. The code is available at https://***/PhilippAuss/ZS6D.

关键词： Training Computer vision Visualization Codes Computational modeling Pose estimation Transformers

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Recognition of Dynamic Grasping Motion through Feature Extraction for Teleoperation 5

Recognition of Dynamic Grasping Motion through Feature Extra...

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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Dai, Yanping Chen, Wenyuan Xi, Ning Wang, Wenxue Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Emerging Technologies Institute University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong

ISBN: (纸本)9798350307320

Detecting human movement intentions and transmitting human operational capabilities to robots is of great significance for teleoperations. However, the difference in kinematic structure between human hands and robotic manipulators brings difficulties to teleoperation mapping. In this paper, we present an approach to hand grasp recognition and mapping based on feature extraction to drive heterogeneous remote manipulator. The hand grasp data set HUST recorded with a data glove was used for this study. The data were preprocessed and their features were extracted by PCA. A neural network-based classifier was obtained through offline training of human grasping activities. We compared different network models and selected the network with the best performance based on the training time and the task requirements of teleoperation. The experimental results showed that the recognition accuracy of dynamic grasping motion with feature extraction is higher than that without feature extraction and can be up to 97.47%. Finally, we validated the feasibility of this method to tele-operate an Barrett hand through online experiments. Our proposed method provides an effective approach of mapping natural human grasping operations to heterogeneous robotic teleoperation systems. © 2023 IEEE.

关键词： Feature extraction

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