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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Amorim, Thulio Nascimento, Tiago Petracek, Pavel De Masi, Giulia Ferrante, Eliseo Saska, Martin Laboratory of Systems Engineering and Robotics Universidade Federal da Paraíba Czech Technical University in Prague Vrije Universiteit Amsterdam Czech Technical University in Prague

ISBN: (纸本)9780738131153

In this work, we address the problem of achieving cohesive and aligned flocking (collective motion) with a swarm of unmanned aerial vehicles (UAVs). We propose a method that requires only onboard sensing of the relative range and bearing of neighboring UAVs, and therefore requires only proximal control for achieving formation. Our method efficiently achieves flocking in the absence of any explicit orientation information exchange (alignment control), and achieves flocking in a random direction without externally provided directional information. To implement proximal control, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions. Our approach may be used independently from any external positioning system such as GNSS or Motion Capture, and can therefore be used in GNSS-denied environments. The performance of the approach was tested in real-world conditions by experiments with UAVs that rely only on a relative visual localization system called UVDAR, proposed by our group. To evaluate the degree of alignment and cohesiveness, we used the order metric and the steady-state value. © 2021 IEEE.

关键词： Global positioning system

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yukang Qiu Yaozhen Hou Haotian Yang Yigao Gao Hen-Wei Huang Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Laboratory for Translational Engineering Harvard Medical School Cambridge MA USA Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method.

关键词： Training Navigation Magnetic cores Heuristic algorithms Magnetic domains Propulsion Deep reinforcement learning

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

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IEEE International Conference on robotics and Biomimetics

作者： Zhihui Xu Tanyong Wei Zongze Li Ding Huang Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare.

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Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

Sample Efficient Dynamics Learning for Symmetrical Legged Ro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jee-eun Lee Jaemin Lee Tirthankar Bandyopadhyay Luis Sentis Department of Aerospace Engineering and Engineering Mechanics Human Centered Robotics Laboratory The University of Texas at Austin USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Robotics and Autonomous Systems Group Data61 CSIRO QLD Australia Faculty of Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin USA

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot, such as leg symmetry, rotational symmetry, and physics invariance. As a result, these schemes require vast amounts of training data to learn the system's redundant elements because they are learned independently. Instead, we propose considering the geometric prior by representing the system in symmetrical object groups and designing neural network architecture to assess invariance and equivariance between the objects. Finally, we demonstrate the effectiveness of our approach by comparing the generalization to unseen data of the proposed model and the existing models. We also implement a controller of a climbing robot based on learned inverse dynamics models. The results show that our method generates accurate control inputs that help the robot reach the desired state while requiring less training data than existing methods.

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A New Virtual Human Model Based on AR-601M Humanoid Robot for a Collaborative HRI Simulation in the Gazebo Environment

A New Virtual Human Model Based on AR-601M Humanoid Robot fo...

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

作者： Aidar Zagirov Elvira Chebotareva Tatyana Tsoy Edgar A. Martínez-García Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technologies and Intelligent Systems Kazan Federal University Kazan Russian Federation Institute of Engineering and Technology Universidad Autonoma de Ciudad Juárez Chihuahua Mexico

Human-robot collaboration (HRC) involves a cooperative and combined work of humans and robots to achieve specific goals. Collaborative robots provide additional support to humans by automating arduous tasks. This enhances efficiency, quality, and optimization of production processes. Prior to conducting real-world experiments, it is crucial to verify a collaboration safety through a simulation. In this paper, we explore a potential of simulating HRC in the Gazebo simulator by using an anthropomorphic robot AR-601M model as a human model and propose key requirements for a human virtual model. Our findings contribute to the development of safe and efficient HRC systems.

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Autonomous, Mobile Manipulation in a Wall-building Scenario: Team LARICS at MBZIRC 2020

arXiv

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arXiv 2022年

作者： Vatavuk, Ivo Polic, Marsela Hrabar, Ivan Petric, Frano Orsag, Matko Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object’s global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick’s magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms. © 2022, CC BY-SA.

关键词： Brick

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Learning to Turn: Deformation Control of a Novel Flexible Fishtail Actuated by Artificial Muscles

Learning to Turn: Deformation Control of a Novel Flexible Fi...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Junwen Gu Yukai Feng Zhengxing Wu Jian Wang Junzhi Yu Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Achieving agile, fish-like turning remains a critical challenge in the development of robotic fish. The flexible deformation of a fish’s body significantly contributes to its turning ability. Consequently, researchers have devoted substantial efforts to enhancing the compliance and agility of robotic fish. This paper presents a novel flexible fishtail, inspired by the musculoskeletal structure of real fish, which can control the deformation of its flexible components during turns, improving the turning performance of robotic fish. The fishtail primarily consists of a carbon fiber plate with attached artificial muscles. These muscles can contract and stretch, causing the carbon fiber tail to bend and deform. To enable precise deformation control, a dynamic model of the fishtail is established based on Hamilton’s principle. Subsequently, deep reinforcement learning methods such as deep deterministic policy gradient and soft actor-critic are employed to develop a deformation controller under joint deflection. Cross-validation ensures the controller’s accuracy and robustness. The experimental results demonstrate that this controller facilitates the deformation control of the flexible tail across various turning amplitudes and speeds, allowing the fishtail to exhibit adjustable compliance or resistance to fluids during turning motion. Consequently, this enhances the agility of the robotic fish.

关键词： Artificial muscles Deformation Tail Learning (artificial intelligence) Muscles Turning Fish Robustness Carbon Robots

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Flapping-Wing Robot Achieves Multi-Domain Locomotion Using Transformable Wheel Mechanism

Flapping-Wing Robot Achieves Multi-Domain Locomotion Using T...

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IEEE International Conference on robotics and Biomimetics

作者： Xiangxiao Chai Shihua Wang Erzhen Pan Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The difficulty of moving from aquatic to aerial environment is one of the major challenges limiting the development of trans-medium multi-domain locomotion vehicles. In this paper, the concept of the transformable wheel, a feature commonly found in autonomous land vehicles (AL V s), is introduced into the design. Additionally, a flapping-wing robot equipped with multi-domain locomotion capability, which can not only realize aerial flight and surface navigation on water, but also ascend from water to land for subsequent takeoff is presented. The paper first introduces the system design of the robot and proposes the corresponding multi-domain locomotion strategy. Subsequently, the design of the transformable wheel mechanism is described in detail and the force analysis for the water-exit is conducted. The feasibility of the proposed strategy is experimentally validated, confirming the prototype's multi-domain locomotion performance finally. This research explores the feasibility of introducing the transformable wheel mechanism into trans-medium multi-domain vehicle platforms and provides a design example.

关键词： Legged locomotion Torque Limiting Navigation Propellers Roads Wheels Prototypes Motors System analysis and design

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