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IEEE International Conference on robotics and Automation (ICRA)

作者： Nam Gyun Kim Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Inflatable structures have attracted considerable research attention in many fields owing to their numerous advantages, such as being light and able to engage in interactions safely. However, in most cases, the inflatable structure can only have one stable configuration, which is undesirable for robotic arms. This study proposes a novel inflatable structure that can be easily reconfigured into multiple stable configurations, even with single-body inflation. In the proposed mechanism, the structure length can be freely adjusted, and its respective joints can be set in the desired directions to facilitate the reconfiguration of its pose. An additional advantage of the proposed mechanism is that it can withstand external forces as well as its own weight. This study analyzes and experimentally validates the shape locking and load-carrying properties of the proposed mechanism. Further, the fabrication process and design guidelines for the proposed mechanism are presented. Through a suitable demonstration, the proposed mechanism is shown to exhibit multiple stable configurations and lock its poses.

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Enhancing Contact Stability in Admittance-Type Haptic Interaction Using Bidirectional Time-Domain Passivity Control

Enhancing Contact Stability in Admittance-Type Haptic Intera...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Seong-Su Park Huseyin Tugcan Dinc Kwang-Hyun Lee Jee-Hwan Ryu Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

The present paper proposes a novel strategy to enhance admittance-type haptic interaction using bidirectional time-domain passivity control. While admittance-type haptic interaction is widely employed in human-robot collaboration, its ability to render low virtual inertia can be limited, leading to unstable interactions with rigid environments. The proposed strategy seeks to stabilize a lower range of virtual inertia while maintaining responsive behavior in free space. The Franka Emika Collaborative robot was utilized in various experiments to test the approach, and the results indicate that the bidirectional time-domain passivity controller can improve interaction performance relative to the conventional unidirectional time-domain passivity approach. This technique may aid in reducing operator fatigue during the manipulation of heavy, non-back drivable industrial robots while preserving a lightweight feel.

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Task Planning and Motion Control with Temporal Logic Specifications

Task Planning and Motion Control with Temporal Logic Specifi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Marcos S. Pereira Luciano C. A. Pimenta Bruno V. Adorno Graduate Program in Electrical Engineering Department of Electronic Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Manchester Centre for Robotics and AI The University of Manchester Manchester UK

This paper proposes a task planning and motion control framework that generates task plans for a linear temporal logic specification (LTL), which are then executed using a task-space constrained motion controller and a local task planner that overcomes local minima. We propose a new encoding for task specifications, directly in the task-space, as constraints of a mixed-integer linear program that can be used with off-the-shelf LTL linear encoding. We apply our framework to plan and execute trajectories for a free-flying robot and show that the task plan is accomplished without collisions, even in the presence of unexpected moving obstacles that are not considered in the planning phase, while control signal constraints are satisfied. To evaluate the local minima avoidance, we compare the local task planner with a sampling-based motion planner, and the results show a smoother trajectory with a faster execution and less total planning time when using our framework. Last, our framework scaled well with a longer LTL specification, as opposed to automata-based frameworks that usually suffer with the curse of the dimensionality.

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Optimal Vision-Based Orientation Steering Control for a 3D Printed Dexterous Snake-Like Manipulator to Assist Teleoperation

TechRxiv

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TechRxiv 2023年

作者： Razjigaev, Andrew Pandey, Ajay K. Howard, David Roberts, Jonathan Jaiprakash, Anjali Crawford, Ross Wu, Liao The School of Electrical Engineering and Robotics The QUT Centre for Robotics Brisbane Australia The Cyber Physical Systems Program Data61 CSIRO Australia The Department of Orthopaedic Surgery Prince Charles Hospital ChermsideQLD Australia The School of Mechanical and Manufacturing Engineering University of New South Wales Sydney Australia

Endoscopic cameras attached to miniaturised snakelike continuum robotic arms can improve dexterity, accessibility, and visibility in minimally invasive surgical tasks. This steerable camera can expand the field of view and enhance the surgical experience with additional degrees of freedom. However, it also creates more control options that complicate human-machine interaction. This challenge presents an opportunity to develop novel human-machine control strategies using visual sensing to complement the dexterous actuation of a steerable surgical manipulator. This study presents a semi-autonomous controller to assist teleoperation by steering a camera such that it keeps an arbitrary target in the centre of the field of view, thus assisting in surveying different orientations about the target with image-based information. Two controllers are proposed using classical image-based visual servoing techniques and optimal visual predictive control techniques. These techniques are simulated and validated on our robot SnakeRaven: a 3D-printed patient-specific end-effector attached to the RAVEN II surgical platform. Both systems, most notably the visual predictive approach, operated successfully with robustness to a lack of information about the target. A video demonstrating the main results of this paper can be found via https://***/fiUM9qYdl1U © 2023, CC BY.

关键词： Remote control

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A Machine Learning-Based Routing Technique for Off-chain Transactions in Payment Channel Networks 5

A Machine Learning-Based Routing Technique for Off-chain Tra...

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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Kadry, Heba Gadallah, Yasser Robotics Control and Smart Systems Program American University in Cairo New Cairo11835 Egypt Department of Electronics and Communications Engineering American University in Cairo New Cairo11835 Egypt

ISBN: (纸本)9781665445115

Blockchain is a foundational technology that has the potential to create new prospects for our economic and social systems. However, the lack of scalability limits the capability to deliver a target throughput and latency compared to the traditional financial systems. Layer-two is a collective term for solutions designed to enhance scalability by handling transactions off the main chain. For example, bidirectional payment channels allow the execution of fast transactions between two parties, thus forming the so-called payment channel networks (PCNs). Therefore, efficient routing protocols are needed to find a payment path from the sender to the receiver, with the lowest transaction fees. These protocols have to consider, among other factors, the unexpected online/offline behavior of the constituent payment nodes and the payment channel imbalance. This study proposes a novel machine learning-based technique for off-chain transaction routing. It is a fully distributed approach to be used within PCNs. For this purpose, the effect of the offline nodes and channel imbalance on the payment channels network are modeled. The simulation results demonstrate that the proposed technique strikes a good balance when considering the success ratio, transaction fees, routing efficiency, transaction overhead, and transaction maintenance overhead compared to other techniques that have previously been proposed for the same purpose. © 2021 IEEE.

关键词： Scalability

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High-Curvature Consecutive Tip Steering of a Soft Growing Robot for Improved Target Reachability

High-Curvature Consecutive Tip Steering of a Soft Growing Ro...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dong-Geol Lee Nam Gyun Kim Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Tech-nology (KAIST) Daejeon Republic of Korea Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Over the years, soft growing robots that allow the feeding of new materials at their tips have attracted considerable attention owing to their unique locomotion characteristics. However, accessing targets over highly curved passages by steering compliant continuum bodies remains challenging. To this end, this study proposes a new tip steering mechanism that imparts soft growing robots with consecutive high-curvature steering ability. We place a hyper-redundant rolling contact joint and twisted string actuator (TSA) at the tip of a soft growing robot to provide high-curvature continuous steering with a smaller scale factor, allowing consecutive turns. The proposed small form factor continuous curvature tip steering mechanism allows the steering mechanism to stay at the tip during steering, improving accessibility by enabling consecutive turns. The improved accessibility of the proposed mechanism is demonstrated based on better confined space reachability compared with that of conventional whole-body steering soft growing robots, along with a consecutive high-curvature pipe navigation demonstration.

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Mechatronics Design of Wheeled Mobile Robotic System for Gutter Maintenance

Mechatronics Design of Wheeled Mobile Robotic System for Gut...

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IEEE International Congress on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Mario Arbulu Geyni Arias Brayan Iñiguez Sofia Dussan Ricardo Barreto José Cornejo Mechatronics Engineering Program CORHUILA Colombia Industrial Engineering Program CORHUILA Colombia R&D Department Robotics 4.0 Colombia Center for Space Emerging Technologies Universidad Nacional de Ingeniería Lima Peru Universidad Tecnológica del Perú Lima Peru

This paper deals with the challenges involved on roof building gutter maintenance robot design, from the problem statement of secure teleoperated semiautonomous navigation on confined gutter spaces, and the best selection of right mechatronics brush to remove the waste on gutter surface; Two concept design have been tested: Passive Brush with Parallel locomotion (PBP), and Active Brush Axial (ABA). The results on our development are shown, as following: secure robot lifting regulation, simulation environment, HMI to teleoperate, mechatronics design. Finally, some tests on simulations and current prototypes are shown, to validate our proposal, and the conclusion that the ABA design allows to remove until 85% gutter waste, instead of PBP design; furthermore, the ABA allows independent brush rotation velocity, instead of PBP design.

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kevin Braathen de Carvalho Iure Rosa L. de Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Nucleus of Specialization in Robotics Universidade Federal de Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Viçosa Viçosa MG Brazil

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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A Novel Adaptive Actor-Critic Reinforcement Learning Controller for Constrained Robots

A Novel Adaptive Actor-Critic Reinforcement Learning Control...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis Pantoja-Garcia Rodolfo Garcia-Rodriguez Vicente Parra-Vega Robotics and Advanced Manufacturing Department Research Center for Advanced Studies (CINVESTAV) Saltillo Coah Mexico Aeronautical Engineering Program and Postgraduate Program in Aerospacial Engineering Universidad Politecnica Metropolitana de Hidalgo Tolcayuca Mexico

ISBN: (纸本)9781665494090

Force-position control for robot manipulators has become a basic control regime for advanced applications such as interaction or cooperative tasks that imply contact to environment or external object. Thus, for many applications environmental information is evaluated continuously to generate adequate control actions to complete the task. There has been proposed techniques such as adaptive or intelligent control to cover some of these requirements, however it seems insufficient given the uncertainty involved when dealing with the environment. The emergence of deep learning has recently renewed interest in these tasks from the perspective of reinforcement learning algorithms. In this paper, an actor-critic scheme is proposed for simultaneous control of contact force and joint positions while moving along an environmental rigid surface. The critic stage evaluates information of the environment through an extended error manifold, trying to minimize the cumulative future rewards by a neural network. The actor stage, which receives environment information from the critic stage, is used to approximate inverse robot dynamics. Together with two continuous orthogonal sliding PID controllers gives rise to simultaneous tracking of position and force, with stability analysis. Numerical simulations show the performance of the proposed approach under several conditions.

关键词： Upper bound Uncertainty Force Reinforcement learning Robot sensing systems Stability analysis Trajectory

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