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Dynamics and control of a Reconfigurable Multi-Arm Robot for In-Orbit Assembly

IFAC-PapersOnLine 2022年第20期55卷 235-240页

作者： Hrishik Mishra Marco De Stefano Christian Ott Institute of Robotics and Mechatronics German Aerospace Center (DLR) 82234 Weßling Germany Automation and Control Institute (ACIN) Technical University of Vienna 1040 Vienna Austria

In this paper, a passivity-based controller is proposed for a reconfigurable multi-arm system, which is employed for in-orbit assembly. The reconfigurable system is composed of multiple anthropomorphic arms which can be connected to a torso or operate independently. The proposed control is applicable for all the morphologies of the multi-arm system while performing several operations required for the assembly task, such as walking on a telescope structure through dedicated mechanical interfaces. The constrained dynamics is exploited to derive a unified joint actuation control law for all the considered operations and morphologies. A Co-simulation framework was developed to simulate the operations for control prototyping, and simulation results prove the effectiveness of the proposed unified controller.

关键词： Motion control Robots manipulators Passivity-based control

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Dynamic Processing Neural Network Architecture For Hearing Loss Compensation

arXiv

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arXiv 2023年

作者： Drgas, Szymon Bramsløw, Lars Politis, Archontis Naithani, Gaurav Virtanen, Tuomas Institute of Automatic Control and Robotics Poznan Uni-versity of Technology Poznan Poland Eriksholm Research Center Denmark Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland

This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech intelligibility after further processing by a person with a hearing impairment, which is modeled by a hearing loss model. We propose an interpretable model called dynamic processing network, which has a structure similar to band-wise dynamic compressor. The network is differentiable, and therefore allows to learn its parameters to maximize speech intelligibility. More generic models based on convolutional layers were tested as well. The performance of the tested architectures was assessed using spectro-temporal objective index (STOI) with hearing-threshold noise and hearing aid speech intelligibility (HASPI) metrics. The dynamic processing network gave a significant improvement of STOI and HASPI in comparison to popular compressive gain prescription rule Camfit. A large enough convolutional network could outperform the interpretable model with the cost of larger computational load. Finally, a combination of the dynamic processing network with convolutional neural network gave the best results in terms of STOI and HASPI. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse Optimal control 13

Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse O...

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13th Asian control Conference, ASCC 2022

作者： Qiu, Tianyu Zhang, Han Wang, Jingchuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9788993215236

In this paper, a traditional one v.s. one pursuit and evasion scenario is considered. The evader aims to reach her target and avoid being captured by the pursuer, while the pursuer, without the knowledge of the evader's cost function, intends to capture the evader before she successfully escapes. We formulate this problem as a Model Predictive control Decoupled Pursuit and Evasion Game (MPC-DPEG) model with incomplete information. We apply Inverse Optimal control (IOC) technique to estimate the evader's cost function and give a Nash pursuit strategy for MPC-DPEG as the optimal strategy for the pursuer. Numerical examples reveal the improvement of pursuit performance of the Nash pursuit strategy based on the estimated cost function over naive pursuit strategies. © 2022 ACA.

关键词： Simulation Optimal control Games Predictive models Cost function Numerical models Predictive control

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Resilient Event-Triggered Model Predictive control for Adaptive Cruise control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Pedestrain Detection in Infrared Images with Improved YOLOv2 Network

Pedestrain Detection in Infrared Images with Improved YOLOv2...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Du, Chenshi Song, Peng Ma, Xin Center for Robotics School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9781728152240

With the progress of unmanned driving technology and the attention of security problems, pedestrian detection is getting more and more researchers' participation. Since 2012, the application of deep convolutional network has greatly improved the accuracy and detection speed of pedestrian detection. However, it is difficult to detect pedestrians at night, the detection accuracy of small objects and obstructed pedestrians is poor, which are problems that researchers are solving. This paper presents infrared image detection of pedestrain based on improved YOLOv2 detection network. It improves the detection accuracy of small volume and blocked pedestrian in the night. © 2020 IEEE.

关键词： Image enhancement

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Agile and versatile bipedal robot tracking control through reinforcement learning

arXiv

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arXiv 2024年

作者： Li, Jiayi Ye, Linqi Cheng, Yi Liu, Houde Liang, Bin The Center for Artificial Intelligence and Robotics Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Institute of Artificial Intelligence Collaborative Innovation Center for the Marine Artificial Intelligence Shanghai University Shanghai200444 China The Navigation and Control Research Center Department of Automation Tsinghua University Beijing100084 China

The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. This decoupling allows for the execution of both learned and unlearned movements under certain constraints while maintaining balance through minor whole-body coordination. To replicate this balance ability and body agility, this paper proposes a versatile controller for bipedal robots. This controller achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network, which is based on a model-based IK solver and reinforcement learning. We consider a single step as the smallest control unit and design a universally applicable control input form suitable for any single-step variation. Highly flexible gait control can be achieved by combining these minimal control units with high-level policy through our extensible control interface. To enhance the trajectory tracking capability of our controller, we utilize a three-stage training curriculum. After training, the robot can move freely between target footholds at varying distances and heights. The robot can also maintain static balance without repeated stepping to adjust posture. Finally, we evaluate the tracking accuracy of our controller on various bipedal tasks, and the effectiveness of our control framework is verified in the simulation environment. © 2024, CC BY.

关键词： controllers

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无人飞行器在不确定扰动下的动态目标跟踪

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Engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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NeRF in robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

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Transformer-Based Multiview Deep Feature Learning for Action Recognition in Depth Videos

SSRN

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SSRN 2022年

作者： Wu, Hanbo Ma, Xin Li, Yibin Shandong University School of Control Science and Engineering 17923 Jingshi Road Shandong Jinan China Center for Robotics School of Control Science and Engineering Shandong University Jinan China

Transformer architectures have recently attracted increasing attention and achieved remarkable success in video action recognition field. However, almost all existing transformer-based methods are applied to RGB video data from a single view, which limits the generalization performance of the transformer model. Human action recognition in depth videos is an important research direction since depth data is not only invariant to illumination and color variations, but also provides reliable 3D geometric information of the body silhouette. In this paper, we extend the transformer architecture to depth video action recognition and propose a transformer-based multiview deep feature learning framework. It mainly consists of an intra-view self-attention encoder module (ISEM) and a cross-view feature fusion module (CFFM). Specially, a depth video is first projected into three orthogonal views to construct multiview depth dynamic volumes that can describe the 3D spatiotemporal evolution of human actions. We feed multiview depth dynamic volumes into 3D CNN for spatiotemporal feature modeling. Based on deep convolutional feature maps of three views, the ISEM learns long-range spatiotemporal dependencies within each view. The CFFM performs inter-view interactions and then integrates cross-view features together to generate a global feature representation, which is finally used for action recognition. Extensive experiments conducted on two large-scale datasets demonstrate the effectiveness of our method to improve the recognition performance in depth videos. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

Assisted Physical Interaction: Autonomous Aerial Robots with...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andrea Berra Viswa Narayanan Sankaranarayanan Achilleas Santi Seisa Julien Mellet Udayanga G.W.K.N. Gamage Sumeet Gajanan Satpute Fabio Ruggiero Vincenzo Lippiello Silvia Tolu Matteo Fumagalli George Nikolakopoulos Miguel Ángel Trujillo Soto Guillermo Heredia CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Visualization Pose estimation Neural networks Lighting Object detection Autonomous aerial vehicles Aircraft navigation

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