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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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A Parameter Identification Method for Coupled Dynamics of Robotic Manipulator with Differential Modular Joints

A Parameter Identification Method for Coupled Dynamics of Ro...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： He, Minghui Luo, Ruiqing Liu, Yukun Du, Liang Bao, Sheng Yuan, Jianjun Wan, Weiwei Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai China Shanghai Robotics Institute Shanghai University Shanghai China Osaka University Graduate School of Engineering Science Department of System Innovation Osaka Toyonaka Japan

ISBN: (纸本)9781665481090

In the daily nursing care for people with disabilities, lifting is the most basic and common action. A robotic manipulator with differential modular joints (DMJ) is designed to accomplish this task. To better implement force-control applications of the robotic manipulator with DMJs, this paper proposes a coupled dynamics identification method based on the multi-input, multi-output (MIMO) feature. Considering the frictions of transmission parts and links, the dynamics of the DMJ is analyzed based on the transformation of energy, and then the parameter-linearized dynamic model of the robotic manipulator with n DMJs is proposed according to the mapping relationship between transmission part level and link level. The identification and verification experiments show that the presented method can obtain the accurate dynamic model parameters. This work lays a foundation for the force-control research of the robotic manipulator with DMJs in the future. © 2022 IEEE.

关键词： Dynamic models

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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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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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Dynamic modeling and analysis for a differential modular robot joint with the friction model

Dynamic modeling and analysis for a differential modular rob...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Liu, Yukun Luo, Ruiqing He, Minghui Du, Liang Bao, Sheng Yuan, Jianjun Wan, Weiwei Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai Robotics Institute Shanghai University Shanghai China Department of System Innovation Graduate School of Engineering Science Osaka University Osaka Toyonaka Japan

ISBN: (纸本)9781665481090

The study of friction has long been popular with scientists. Considering the coupling characteristic of multi-input-multi-output (MIMO) system, the coupling friction model was established, which was based on Coulomb-Viscous friction model for the differential modular robot joint (DMRJ) according to the law of conservation of energy. Then, we identified the coefficients of the friction model through the experiment. In order to verify the accuracy of the established friction model, we regarded the DMRJ as a 2-DoF linkage and built the inertial dynamic model based on Lie theory, whose inertial parameters were estimated by computer aided design (CAD). Besides, we chose the trajectory based on the Fourier series, which has good performance in anti-interference ability, as the verification trajectory. From the generated trajectories, we used a trajectory that is able to change as much as possible in speed, position and acceleration to estimate the accuracy of the model comprehensively. Finally, the result indicated the model had good accuracy. © 2022 IEEE.

关键词： Modular robots

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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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Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning

arXiv

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arXiv 2024年

作者： Wu, Zheng Li, Yichuan Zhan, Wei Liu, Changliu Liu, Yun-Hui Tomizuka, Masayoshi The Department of Mechanical Engineering University of California BerkeleyCA United States The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Robotics Institute Carnegie Mellon University PA United States

— The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain management. This paper investigates the application of Reinforcement Learning (RL) in enhancing task planning for such robotic systems. Confronted with the substantial challenge of a vast action space, which is a significant impediment to efficiently apply out-of-the-shelf RL methods, our study introduces a novel method of utilizing supervised learning to iteratively prune and manage the action space effectively. By reducing the complexity of the action space, our approach not only accelerates the learning phase but also ensures the effectiveness and reliability of the task planning in robotic palletization. The experiemental results underscore the efficacy of this method, highlighting its potential in improving the performance of RL applications in complex and high-dimensional environments like logistics palletization. © 2024, CC BY.

关键词： Reinforcement learning

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Teacher-Student Reinforcement Learning for Mapless Navigation using a Planetary Space Rover

arXiv

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arXiv 2023年

作者： Mortensen, Anton Bjørndahl Pedersen, Emil Tribler Benedicto, Laia Vives Burg, Lionel Madsen, Mads Rossen Bøgh, Simon Robotics & Automation Group Department of Materials and Production Aalborg University Denmark AAU SPACE Aalborg University Denmark

We address the challenge of enhancing navigation autonomy for planetary space rovers using reinforcement learning (RL). The ambition of future space missions necessitates advanced autonomous navigation capabilities for rovers to meet mission objectives. RL’s potential in robotic autonomy is evident, but its reliance on simulations poses a challenge. Transferring policies to real-world scenarios often encounters the "reality gap", disrupting the transition from virtual to physical environments. The reality gap is exacerbated in the context of mapless navigation on Mars and Moon-like terrains, where unpredictable terrains and environmental factors play a significant role. Effective navigation requires a method attuned to these complexities and real-world data noise. We introduce a novel two-stage RL approach using offline noisy data. Our approach employs a teacher-student policy learning paradigm, inspired by the "learning by cheating" method. The teacher policy is trained in simulation. Subsequently, the student policy is trained on noisy data, aiming to mimic the teacher’s behaviors while being more robust to real-world uncertainties. Our policies are transferred to a custom-designed rover for real-world testing. Comparative analyses between the teacher and student policies reveal that our approach offers improved behavioral performance, heightened noise resilience, and more effective sim-to-real transfer. © 2023, CC BY-NC-ND.

关键词： Students

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