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IEEE International Conference on robotics and Biomimetics

作者： Hao Chen Kai Hu Longlong Hu Ziqi Zhou Yao Li Bing Li Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Compared with large robots, micro-robots have the advantages of small size, low power consumption, and high concealment. For complex terrain, the crawling performance of micro-robots will be significantly reduced. In this study, the mesh surface was used as the crawling base of the robot, and a variable stiffness tarsus was designed with the beetle as a reference. This tarsus composed of multiple bending parts achieves a large range of stiffness changes, 0.07~0.495 N/mm. We have achieved stiffness control of the tarsus in a single actuation by trajectory planning of the claw ends. The proposed robot can achieve a big stiffness of the tarsus to resist the deformation of the tarsus due to external forces, which means that the robot can increase the adhesion ability when crawling at the stance phase. Therefore, even when the load was increased from 10g to 40g, the average crawling speed of the robot was not significantly reduced. This new tarsus can improve the crawling performance of the micro-robot on the mesh.

关键词： Legged locomotion Power demand Trajectory planning Force Kinematics Resists Bending Robots Substrates Trajectory optimization

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EEG-Based Hand Movement Recognition: Feature Domain and Level of Decomposition 1

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13th National Conference on Industrial Problems on Machines and Mechanism, IPRoMM 2020

作者： Phukan, Nabasmita M. Kakoty, Nayan Gupta, Nipun Baruah, Neelanjana Jorhat Engineering College Jorhat India Embedded Systems and Robotics Laboratory Tezpur University Tezpur India Madhav Institute of Technology and Science Gwalior India

ISBN: (数字)9789811617690

ISBN: (纸本)9789811617683

This manuscript reports feature domains for the recognition of right and left hand movements using Electroencephalogram (EEG). A 21-channel EEG dataset of seven subjects during right and left hand fist open and close movements was collected from PhysioNet of the BCI2000 Instrumentation system. Features in time, frequency, and time-frequency domains have been explored. Support vector machine with radial basis function kernel was used for the recognition of right and left hand fist open and close movements. The recognition with time, frequency, and time-frequency domain features resulted in an accuracy of 90%, 92%, 97.5%, respectively. Time-frequency domain features obtained through discrete wavelet transform (DWT) at four decomposition levels have resulted in maximum recognition rate. The highest recognition rate of 98.6 ± 0.6 % has resulted in DWT features at level 2. This was substantiated by the fact that DWT features at level 2 establish maximum correlation with pre-processed EEG. Experimental result shows that time-frequency is the best performing feature domain among the three. Further, correlation measure of time-frequency domain features with EEG are established as a benchmark for selecting DWT decomposition level. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Real-Time Pose Estimation of Rats Based on Stereo Vision Embedded in a Robotic Rat

Real-Time Pose Estimation of Rats Based on Stereo Vision Emb...

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

作者： Xiaowen Guo Guanglu Jia Mohamed Al-Khulaqui Zhe Chen Toshio Fukuda Qing Shi Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

In this paper, we propose a system for real-time rat pose estimation based on stereo vision. The system is dedicated to robot-rat interaction research. First, we design a lightweight, high-resolution network (RRKDNet) for keypoint detection of the rat. The network is trained on a dataset of rat images, which are captured by the robotic rat in first-person view. Second, based on the keypoint detection results, the pose of the rat is obtained by stereo vision model calculation and robot coordinate transformation. At last, we complete a real-time simulation experiment to reproduce the pose of the rat and the robotic rat. The system has been subjected to a series of experiments and the results demonstrate that our network performs better in speed and performance than similar networks. Compared to similar networks, our network has about one-third the number of parameters, while the detection rate increases by 45.25% (the detection rate is 71.57%). The inference speed (34.42 FPS with dual model simultaneous inference) is also faster. The validation error is only 13.85 pixels on the homemade dataset, which is lower than all backbones in Deeplabcut (a toolbox more frequently used for rat keypoint detection). Thus, this work is a significant step in the autonomous intelligent interaction between robots and rats.

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Toward Process Controlled Medical Robotic System

arXiv

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arXiv 2023年

作者： Liu, Yihao Kheradmand, Amir Armand, Mehran The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Neurology Department of Neuroscience Laboratory for Computational Sensing & Robotics Johns Hopkins University BaltimoreMD21218 United States The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Orthopaedic Surgery Department of Mechanical Engineering Department of Computer Science Applied Physics Laboratory Johns Hopkins University BaltimoreMD21218 United States

Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide

关键词： Navigation systems

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Object Detection Methods for a Robot Soccer

Object Detection Methods for a Robot Soccer

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Ramir Sultanov Roman Lavrenov Shifa Sulaiman Yang Bai Mikhail Svinin Evgeni Magid Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Information Science and Engineering Department Ritsumeikan University Biwako-Kusatsu Campus Shiga Japan

Soccer tournaments between robot teams are being held around the world to encourage robotic research. An accurate detection and tracking of a ball position, an identification of robots and objects during a game are crucial for robot teams. In this paper, object detection methods for identifying robots and a ball during a robot soccer game are presented. Standard OpenCV library functions were adjusted for a ball and robots' detection. Simulation studies were carried out in Gazebo environment to compare a performance of various methods discussed in this paper.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Efficient Learning and Control of String- Type Artificial Muscle Driven Robotic systems

Efficient Learning and Control of String- Type Artificial Mu...

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American Control Conference (ACC)

作者： Jiyue Tao Sunil Kumar Rajendran Yunsong Zhang Feitian Zhang Dexin Zhao Tongsheng Shen The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The National Innovation Institute of Defense Technology Beijing China The BSS Technologies Inc. Takoma Park MD USA The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates the learning-based control of robotic systems driven by string-type artificial muscles. Due to the highly nonlinear dynamics of the actuators and the complicated mechanical structure, it is typically very challenging to design traditional model-based controllers that exhibit desired control performances. With the rapid development of machine learning techniques, deep reinforcement learning (DRL) algorithms have been utilized to control a variety of complex robotic systems. However, these DRL algorithms usually require a huge amount of training data and iterations to converge, which is generally unacceptable for the robotic systems of interest. Therefore, this paper designs an efficient learning-based control algorithm, aiming to improve the training efficiency for robotic systems driven by string-type artificial muscle actuators. Specially, two training improvements are proposed including imitation learning and data augmentation. This paper applies the proposed learning methods to three popular DRL algorithms and tests the control performances in three case studies using three string-type artificial muscle-driven robots, including a parallel robotic wrist, a two degrees-of-freedom (DOF) robotic eye and a robotic finger. Simulation results show that the proposed learning-based control methods significantly accelerate the convergence speed and improve the data efficiency in all the three case studies.

关键词： Artificial muscles Wrist Training Actuators Machine learning algorithms Imitation learning Fingers

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Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible Coupling Suction Disc Design for Adhesion Enhancement 16th

Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tan, Wenjun Qin, Hengshen Zhang, Chuang Wang, Ruiqian Zhang, Yiwei Yang, Lianchao Chen, Qin Wang, Feifei Xi, Ning Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Sichuan Chengdu610042 China Department of Electrical and Electronic Engineering University of Hong Kong 999077 Hong Kong Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (纸本)9789819964888

Underwater high-performance biological adhesion systems have attracted increasing research interest to inspire engineered adhesion systems. Switchable underwater adhesion systems typically employ suction adhesion. However, achieving low pre-pressure negative pressure adhesion is still a challenge. An inspiring nature mode is provided by the rock-climbing fish, which needs little preload and can resist a pull-off force of 1000 times its own body weight. The pectoral and abdominal fins of rock-climbing fish have evolved to almost a flat suction cup. Through micro CT, we found that a unique skeletal network is embedded in the suction cup. This unique structure increases the stiffness of the suction cup and enhances its adhesion performance. Through numerical simulation, it was revealed that this rigid, flexible coupling structure enhances the ability of the suction cup to resist lateral pressure and increases the energy dissipation of the edge detachment of the suction cup. This work can shed light on the design of novel underwater adhesion systems and inspire underwater adhesion crawling robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Adhesion

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MrDMD-Based Sensor Placement in Distributed Flow Estimation for the Design of the Artificial Lateral Line of an Underwater Robot

MrDMD-Based Sensor Placement in Distributed Flow Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Wang Tongsheng Shen Dexin Zhao Feitian Zhang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China National Innovation Institute of Defense Technology Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

An artificial lateral line (ALL) is a sensing system that imitates the distributed perception organs of fish and plays a major role in enhancing the flow estimation capability of underwater robots. Whereas various ALLs have been designed and developed, it is still an open question how to better place ALL sensors on underwater robots, especially for those with complex shapes and working in dynamic flow and robot operating conditions. Aiming to answer this question, this paper presents a novel data-driven sensor placement method for ALLs of underwater robots. This method adopts distributed pressure sensors to measure the flow field along the profile or the outermost boundary of an underwater robot, and quantifies the dynamic information embedded within these measurements using multi-resolution dynamic mode decomposition (mrDMD). The sensors are then positioned by optimizing the dynamic flow information to enhance the perception. Compared with existing sensor placement methods, such as observability maximization and exhaustive experimental search, the proposed method focuses on the modes of dynamics variability at various spatio-temporal scales, thus leading to improved sensing ability especially in complex and dynamic flows. In addition, comprehensively considering the sensor placement under different flow and robot operating conditions, the proposed method is expected to provide an optimal solution for the overall sensing performance of the ALL system. To demonstrate the effectiveness of the proposed method, a case study of background flow speed estimation of oscillating underwater robots of different shapes in a uniform flow is presented.

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Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots 74

Multi-Agent 3D Map Reconstruction and Change Detection in Mi...

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74th International Astronautical Congress, IAC 2023

作者： Dinkel, Holly Di, Julia Santos, Jamie Albee, Keenan Borges, Paulo Moreira, Marina Alexandrov, Oleg Coltin, Brian Smith, Trey Department of Aerospace Engineering The University of Illinois Urbana-Champaign UrbanaIL United States Department of Mechanical Engineering Stanford University StanfordCA United States Department of Physics Chalmers University of Technology Gothenburg Sweden Maritime and Multi-Agent Autonomy Group The Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Robotics and Autonomous Systems Group CSIRO Data61 BrisbaneQLD Australia Intelligent Robotics Group The NASA Ames Research Center Moffett FieldCA United States

Assistive free-flyer robots autonomously caring for future crewed outposts-such as NASA's Astrobee robots on the International Space Station (ISS)-must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： 3D Reconstruction Artemis Change Detection Mapping Microgravity robotics

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