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arXiv 2024年

作者： Zhang, Xinglong Pan, Wei Li, Cong Xu, Xin Wang, Xiangke Zhang, Ronghua Hu, Dewen College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Department of Cognitive Robotics Delft University of Technology Netherlands Chair of Automatic Control Engineering Technical University of Munich Munich80333 Germany

Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on numerical optimization tools to periodically calculate local control sequences online. This process is computationally demanding and lacks scalability for large-scale, nonlinear MRS. This article proposes a novel distributed learning-based predictive control (DLPC) framework for scalable multirobot control. Unlike conventional DMPC methods that calculate open-loop control sequences, our approach centers around a computationally fast and efficient distributed policy learning algorithm that generates explicit closed-loop DMPC policies for MRS without using numerical solvers. The policy learning is executed incrementally and forward in time in each prediction interval through an online distributed actor-critic implementation. The control policies are successively updated in a receding-horizon manner, enabling fast and efficient policy learning with the closed-loop stability guarantee. The learned control policies could be deployed online to MRS with varying robot scales, enhancing scalability and transferability for large-scale MRS. Furthermore, we extend our methodology to address the multirobot safe learning challenge through a force field-inspired policy learning approach. We validate our approach’s effectiveness, scalability, and efficiency through extensive experiments on cooperative tasks of large-scale wheeled robots and multirotor drones. Our results demonstrate the rapid learning and deployment of DMPC policies for MRS with scales up to 10,000 units. Source codes and multimedia materials are available online at https://***/view/pldpc/. © 2024, CC BY.

关键词： Scalability

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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Transfer Learning Based Multi-Perception Safety Strategy for Human-Robot Collaboration

Transfer Learning Based Multi-Perception Safety Strategy for...

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

作者： Chongyi Wei Changcong Wang Shaoping Bai Yibin Li Xincheng Tian Lelai Zhou Center for Robotics School of Control Science and Engineering Shandong University Jinan China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China Department of Materials and Production Aalborg University Aalborg Denmark

Human-robot collaboration (HRC) is vital to adapt to the significant change in manufacturers’ demands for automation, and safety is a primary and major challenge that must be addressed for it. In this paper, a muti-perception safety strategy and framework are introduced to ensure human safety while trying to avoid reducing the work efficiency of the robot by taking human activity intentions and human-robot distance into account. To realize the safety strategy, an LSTM-CNN based neural network is built for human activity classification. To improve the generalization ability and performance of the network with data scarcity for existing deep learning-based human activity recognition methods, transfer learning-enabled activity recognition is proposed. Based on the studies, a feasible security system is implemented in the human-robot collaboration scenario.

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GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization

arXiv

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arXiv 2023年

作者： Zhang, Haoming Chen, Chih-Chun Vallery, Heike Barfoot, Timothy D. Institute of Automatic Control Faculty of Mechanical Engineering RWTH Aachen University Aachen Germany Department of BioMechanical Engineering Delft University of Technology Department for Rehabilitation Medicine Erasmus MC Rotterdam Netherlands University of Toronto Robotics Institute Toronto Canada

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces gnssFGO, a global and online trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In gnssFGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps without strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Düsseldorf, and Cologne and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas where a classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17 km route through Aachen, the proposed method results in a mean 2-D positioning error 0.48 m while fusing raw GNSS observations with lidar odometry in a tight coupling. © 2023, CC BY.

关键词： Continuous time systems

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Systematic Study of Optical Turbulence in Rayleigh-Bénard Underwater Convection

Systematic Study of Optical Turbulence in Rayleigh-Bénard U...

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2023 Propagation Through and Characterization of Atmospheric and Oceanic Phenomena, pcAOP 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： O'Malley, Owen Avramov-Zamurovic, Svetlana Ferlic, Nathaniel A. Judd, K. Peter Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd. Hopper Hall AnnapolisMD21402 United States 22347 Cedar Point Road Patuxent RiverMD20670 United States United States Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC20375 United States

Synchronized optical wavefront and intensity measurements are used to characterize the optical turbulence generated by Rayleigh-Bénard convection. We find refractive index structure constant and the shape of the ... 详细信息

ISBN: (纸本)9781957171289

关键词： Refractive index

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SDA-SNE: Spatial Discontinuity-Aware Surface Normal Estimation via Multi-Directional Dynamic Programming

arXiv

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arXiv 2022年

作者： Ming, Nan Feng, Yi Fan, Rui MIAS Research Group Robotics & Artificial Intelligence Laboratory Department of Control Science & Engineering Tongji University Shanghai201804 China

The state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion. Although such SNEs have greatly minimized the trade-off between efficiency and accuracy, their performance on spatial discontinuities, e.g., edges and ridges, is still unsatisfactory. To address this issue, this paper first introduces a novel multi-directional dynamic programming strategy to adaptively determine inliers (co-planar 3D points) by minimizing a (path) smoothness energy. The depth gradients can then be refined iteratively using a novel recursive polynomial interpolation algorithm, which helps yield more reasonable surface normals. Our introduced spatial discontinuity-aware (SDA) depth gradient refinement strategy is compatible with any depth-to-normal SNEs. Our proposed SDA-SNE achieves much greater performance than all other SoTA approaches, especially near/on spatial discontinuities. We further evaluate the performance of SDA-SNE with respect to different iterations, and the results suggest that it converges fast after only a few iterations. This ensures its high efficiency in various robotics and computer vision applications requiring realtime performance. Additional experiments on the datasets with different extents of random noise further validate our SDA-SNE's robustness and environmental adaptability. Our source code, demo video, and supplementary material are publicly available at ***/SDA-SNE. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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Process Assurance for Object Detection Through Deep Neural Networks to Accomplish the Autonomous Aerial Refueling Task

Process Assurance for Object Detection Through Deep Neural N...

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Automated Vehicle Validation Conference (IAVVC), IEEE International

作者： Danielle Clement Sarah Mottino Donald Costello Aeronautics Division Lockheed Martin Corporation Fort Worth Texas United States Weapons Robotics and Control Engineering Department United States Naval Academy Annapolis Maryland United States

This paper presents a high level discussion on certifying of a deep neural network to build situational awareness for an autonomous vehicle to complete the aerial refueling task through object detection verification a...

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Time-Optimal Path Tracking for Cooperative Manipulators: A Convex Optimization Approach

arXiv

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arXiv 2023年

作者： Haghshenas, Hamed Hansson, Anders Norrlöf, Mikael The Division of Automatic Control Department of Electrical Engineering Linköping University Linköping581-83 Sweden ABB Robotics Division Västerås721-36 Sweden

This paper studies the time-optimal path tracking problem for a team of cooperating robotic manipulators carrying an object. Considering the problem for rigidly grasped objects, we show that it can be cast as a convex optimization problem and solved efficiently with a guarantee of optimality. When formulating the problem, we avoid using a particular wrench distribution and exploit the full actuation available to the system. Then, we consider the problem for grasps using frictional forces and show that this problem also, under a force-closure grasp assumption, can be formulated as a convex optimization problem and solved efficiently and to optimality. To ensure a firm grasp, internal forces have been taken into account in this approach. © 2023, CC BY.

关键词： Convex optimization

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Event-Triggered Attitude controller Design for Unmanned Aerial Vehicles under Cyber Attacks

Event-Triggered Attitude Controller Design for Unmanned Aeri...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Seungyong Han Xuyang Guo Yongsik Jin Yongseob Lim Suneel Kumar Kommuri Korea Atomic Energy Research Institute Daejeon Republic of Korea Department of Control Science and Engineering Tongji University Shanghai China Electronics and Telecommunications Research Institute Daegu Republic of Korea Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea Department of Electrical and Electronic Engineering Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper proposes an event-triggered attitude controller design method for unmanned aerial vehicles (UAVs) under cyber attacks. Versatile UAVs are capable of transmitting the measurement or receiving control signals through network channels. In the controller design for such UAVs, it is required to consider two distinctive features. The first one is that the strategy of updating the control signal is important for saving the limited energy consumption. The other one is that the UAVs are vulnerably exposed to threats of malicious attacks through network channels. For enhanced energy efficiency and improved stability, the event-triggered mechanism (ETM) and the attitude controller are simultaneously designed. By utilizing Lyapunov- Krasovskii functionals (LKFs), the sufficient conditions for the co-design are derived in terms of linear matrix inequalities (LMIs). For a 3-degrees of freedom (DOF) quadcopter system, the simulation results are presented to validate the proposed attitude control method.

关键词： Sufficient conditions Attitude control Simulation Autonomous aerial vehicles Stability analysis Numerical models Resource management

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