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

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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Transparent Objects: A Corner Case in Stereo Matching

Transparent Objects: A Corner Case in Stereo Matching

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhiyuan Wu Shuai Su Qijun Chen Rui Fan The Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems Machine Intelligence & Autonomous Systems (MIAS) Group the College of Electronic & Information Engineering Tongji University Frontiers Science Center for Intelligent Autonomous Systems Shanghai P. R. China

Stereo matching is a common technique used in 3D perception, but transparent objects such as reflective and penetrable glass pose a challenge as their disparities are often estimated inaccurately. In this paper, we propose transparency-aware stereo (TA-Stereo), an effective solution to tackle this issue. TA-Stereo first utilizes a semantic segmentation or salient object detection network to identify transparent objects, and then homogenizes them to enable stereo matching algorithms to handle them as non-transparent objects. To validate the effectiveness of our proposed TA-Stereo strategy, we collect 260 images containing transparent objects from the KITTI Stereo 2012 and 2015 datasets and manually label pixel-level ground truth. We evaluate our strategy with six deep stereo networks and two types of transparent object detection methods. Our experiments demonstrate that TA-Stereo significantly improves the disparity accuracy of transparent objects. Our project webpage can be accessed at ***/TA-Stereo.

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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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Changed Extended State Observer based Position Control for Autonomous Underwater Vehicle

Changed Extended State Observer based Position Control for A...

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Data Driven Control and Learning systems (DDCLS)

作者： Wanping Song Chen Zheng Zengqiang Chen Mingwei Sun Qinglin Sun College of Artificial Intelligence Nankai University Tianjin China Beijing Institute of Astronautical Systems Engineering Beijing China Key Laboratory of Intelligent Robotics of Tianjin Tianjin China

Extended state observer (ESO) has been used to estimate the unknown nonlinear function of the system using input and output data. Recently, a new observer, the compensation function observer (CFO), has a significant improvement over ESO in estimating accuracy. This paper refers to the theory of CFO of fully using the available information and the innovation of the new definition of disturbance, designs a new observer, and verifies the availability of the new observer on the depth control simulation of the autonomous underwater vehicle (AUV). The simulation results demonstrate that the new observer is capable of estimating the unknown nonlinear function more quickly and accurately.

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IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using Wheel-Encoder and Inertial-RGB-Depth Measurements

IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using ...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yuanhao Liu Wei Liang Chu Wang Xin Zhang Huanbing Gao Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences School of Information and Electrical Engineering Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Safe & smart move (SSM) aims to help industrial vehicular robot (IVR), such as warehousing robots, running safely and smartly by detecting the obstacles in front and maintaining a safe distance & attitude. However, the traditional SSM methods suffer the drawbacks of limited ranges, inaccurate regression of safety distance & orientation between the robot and the forward unexpected obstacles, and the inappropriate IVR control strategies. To solve these issues, this paper designs an innovative real-time safety-aware framework using the complementary Wheel-Encoder and Inertial-RGB-Depth measurements to ensure the IVR running safely, reasonably and reliably. To estimate the robot running states, we take advantages of the complementary wheel encoder, monocular RGB images and inertial measurements to obtain the robot positions, orientations, angular and linear velocities in a tightly-coupled manner. To improve the safety relative distance & attitude estimation accuracy, we exploit the RGB and depth measurements in a task-oriented fashion. The forward obstacles with the specific contours are detected using the Mask-RCNN model, followed by the Gaussian Mixture Model to obtain the forward obstacle depth. Eventually, an IVR control strategy is designed to maintain the robot safety distance and orientation, and to keep the minimum damage to the robot mechanical system. The presented method has been evaluated on the developed industrial vehicular robot safety perception platform. The experimental results prove the competitiveness against the state-of-the-art methods.

关键词： Service robots Warehousing Robot vision systems Wheels Reliability engineering Safety Mobile robots Mechanical systems Velocity measurement Gaussian mixture model

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IIPC: Intra-Inter Patch Correlations for Garment Collision Handling

IIPC: Intra-Inter Patch Correlations for Garment Collision H...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ruisheng Yuan Minzhe Tang Dongliang Kou Mingyang Sun Dingkang Yang Xiao Zhao Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Ministry of Education Engineering Research Center of AI and Robotics Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Realistic garment simulation is critical for digital humans. However, noticeable penetrations still exist in current learning-based garment simulation techniques. To reduce penetrations in predicted garments, we resort to the garment geometry and neural Signed Distance Fields (SDFs) for effective collision handling. The key idea of our method is that we divide the garment into patches and model the local and global garment geometry through Intra- and Inter-Patch Correlations (IIPC), which can be easily learned through the powerful context-understanding ability of Transformers. The geometry information is then utilized to predict a per-vertex moving offset, according to which we move the penetrating vertices along the SDF’s gradient directions to solve collisions. Our module can be coupled with learning-based backbones to effectively solve penetrations while retaining real-time performance. Extensive experiments show that the proposed method excels the prior works significantly.

关键词： Geometry Correlation Art Clothing Predictive models Transformers Real-time systems Digital humans Context modeling

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Exploring Neural Networks for Forward Kinematics of the Robotic Arm with Different Length Configurations: A Comparative Analysis

Exploring Neural Networks for Forward Kinematics of the Robo...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia

Our work investigates the utilization of Artificial Neural Networks (ANNs) to address the complexities associated with Forward Kinematics (FK) problems within the field of robotics. We undertake an extensive comparative analysis to assess how ANNs perform under different circumstances with robotic arms of varying lengths and impact the overall system’s functionality. The training, testing, and validation of ANNs are carried out using MATLAB for a simulated 2-DoF serial robotic arm involving three distinct datasets: fixed step size, random step size, and sinusoidal step size. Three training optimizers, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Stochastic Conjugate Gradient (SCG), are considered within the ANN architecture. Based on Mean Square Error (MSE) values, the numerical findings reveal the potential of ANN in estimating forward kinematic solutions of complex robotic manipulators with different arm lengths and reducing computational complexity.

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