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An efficient 2-opt operator for the robotic task sequencing problem

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An efficient 2-opt operator for the robotic task sequencing ...

2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Li, Hong Liu, Si-Yang Huang, Yan-Wei Chen, Yong-Quan Fu, Zhang-Hua College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of Robotics and Intelligent Manufacturing Chinese University of HongKong Shenzhen China School of Software Engineering Xi’an Jiao Tong University Xi’an China

ISBN: (纸本)9781728137261

Nowadays, automated guided vehicles (AGVs) are widely used in many real life applications, such as in modern factories and logistics systems. Generally, an AGV is assigned a number of random tasks, and different execution orders of the tasks correspond to different moving distances. Therefore, it is important to determine the task execution order. This problem is known as the robotic task sequencing problem, which could be transformed to an equivalent asymmetric travelling salesman problem (ATSP). In the field of TSP, the 2-opt movements is a basic operator which has been widely used by local-search based heuristics. However, for the ATSP, given an incumbent solution with N tasks, the current best method requires a complexity of O(N3) to evaluate all the O(N2) possible 2-opt based movements, and thus, the computational cost is too expensive, especially for large-scale instances. This paper develops a series of data structures, to reduce the above time complexity from O(N3) to O(N2). Experimental results based on 60 task sets indicate that, the proposed method can significantly improve the efficiency of 2-opt based local search. © 2018 IEEE.

关键词： Local search (optimization)

Chattering-free fixed-time sliding mode control for bilateral teleoperation under unknown time-varying delay via disturbance and state observers

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Advanced Control for Applications: Engineering and Industria...

Advanced Control for Applications: engineering and Industrial Systems 2020年第4期2卷

作者： Abadi, Ali Soltani Sharif Hosseinabadi, Pooyan Alinaghi Mekhilef, Saad Ordys, Andrzej Department of Electrical Engineering Faculty of Engineering Yazd University Yazd Iran Faculty of Engineering University of Malaya Kuala Lumpur Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia The Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland

This paper proposes a new chattering-free Fixed-time Observer-Based Sliding Mode Control (FOBSMC) scheme for synchronization of two bilateral teleoperations under unknown time-varying delay. The system is subjected to unknown disturbances which are estimated in a fixed time by utilizing the Disturbance Observer-based SMC scheme. The fixed-time State Observer-based SMC is designed to estimate the unmeasured velocity state, while the position state is assumed to be available. Fixed-time stability method is used to provide the system convergence time independently of initial states. A FOBSMC scheme is first designed for master system of bilateral teleoperation under unknown time-varying delay to track the environmental torque by the human operating torque in the presence of time delay in the communication channel, as well as estimating the unknown disturbances and unmeasured states. A FOBSMC scheme is designed to synchronize slave system to master system in the presence of time delay in the communication channel, as well as estimating disturbances and states. The stability analysis is performed for closed-loop system of combined controller-observer by using Lyapunov theory. The efficacy of the proposed method to fulfill a good transparency and stability under time-varying communication delays is tested by performing numerical simulation in Simulink/MATLAB. © 2020 John Wiley & Sons Ltd.

关键词： Time delay

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

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arXiv 2021年

作者： Kong, Zhe Zhang, Wentian Liu, Feng Luo, Wenhan Liu, Haozhe Shen, Linlin Ramachandra, Raghavendra College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Norwegian University of Science and Technology Gjøvik2818 Norway

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://***/kongzhecn/dfdm. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

Precise iterative closest point algorithm with corner point constraint for isotropic scaling registration

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Precise iterative closest point algorithm with corner point ...

作者： Du, Shaoyi Cui, Wenting Wu, Liyang Zhang, Sirui Zhang, Xuetao Xu, Guanglin Xu, Meifeng Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’anShaanxi710049 China School of Software Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China Air Force Engineering University Xi’anShaanxi710038 China The Second Affiliated Hospital of Xi’an Jiaotong University Xi’anShaanxi710004 China

The traditional iterative closest point (ICP) algorithm could register two point sets well, but it is easily affected by local dissimilarity. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First of all, because the corner points can preserve the similarity of the whole shapes, an objective function based on least square error is proposed under the guidance of the corner points. Second, a new ICP algorithm is proposed to complete the isotropic scaling registration. At each iterative step of this new algorithm, the correspondence is built based on the closest point searching, and then a closed-form solution of the transformation is computed. This new algorithm converges monotonically to a local minimum from any given initial scaling transformation. To obtain the expected minimum, the traditional scaling ICP algorithm is applied to compute the initial transformation. The experimental results demonstrate that our algorithm can prevent the influence of the local dissimilarity and improve the registration precision compared with the traditional ICP algorithm. © 2017, Springer-Verlag GmbH Germany.

关键词： Iterative methods

Research and development of ISA-DAC-instrumentation software application and data acquisition system for the foundation industry

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AIP Conference Proceedings 2022年第1期2670卷

作者： Naveen Gopal Rajalakshmi P. Anantha Christu Raj R. Jegan 1Software Developer Automation AEM Cores Ltd Adelaide South Australia 5B Bedford St Gillman SA 5013 2Department of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu 641114 India 3Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu 641114 India

The ISA-DAC system is an application development project used for fully instrumented computerized pile load testing. The implementation of the new system will help the foundation Industries to process digitized raw data's and the results could be compared against design methods and other published evidence for the ageing of pile. The system interfaces with the latest fourth generation technology of Jack Cell testing with Instrumentation software and Data Acquisition Systems. The system supports different types of engineering units and reports for the research studies. The system will relate to high resolution geotechnical instruments for the maximum accuracy. Processing the data at various engineering units, remote monitoring using the web services, online monitoring, Telemetry alert information and graphical representation of the data are unique features of the system. In addition, the new system is expected to have an increased scope, time saving, functionality and achieves higher degree of automation.

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A Robot's expressive language affects human strategy and perceptions in a competitive game

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arXiv 2019年

作者： Roth, Aaron M. Reig, Samantha Bhatt, Umang Shulgach, Jonathan Amin, Tamara Doryab, Afsaneh Fang, Fei Veloso, Manuela Robotics Institute Human-Computer Interaction Institute College of Engineering Institute for Software Research Machine Learning Department Carnegie Mellon University PittsburghPA United States

As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of "encouraging" or "discouraging" verbal commentary) and measure the impact on participants' rationality, strategy prioritization, mood, and perceptions of the robot. We learn that a robot opponent that makes discouraging comments causes a human to play a game less rationally and to perceive the robot more negatively. We also contribute a simple open source Natural Language Processing framework for generating expressive sentences, which was used to generate the speech of our autonomous social robot. Copyright © 2019, The Authors. All rights reserved.

关键词： Anthropomorphic robots

Regularization with multilevel non-stationary tight framelets for image restoration

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

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arXiv 2025年

作者： Lei, Mengqi Wu, Yihong Li, Siqi Zheng, Xinhu Wang, Juan Gao, Yue Du, Shaoyi BNRist THUIBCS BLBCI School of Software Tsinghua University Beijing100084 China Department of Mechanical Engineering Taiyuan University of Technology Taiyuan030024 China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China Department of Ultrasound the Second Affiliated Hospital Xi’an Jiaotong University China State Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Visual recognition relies on understanding both the semantics of image tokens and the complex interactions among them. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics. To overcome these issues, we present Soft Hypergraph Neural Networks (SoftHGNNs), which extend the methodology of hypergraph computation, to make it truly efficient and versatile in visual recognition tasks. Our framework introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous participation weights rather than hard binary assignments. This dynamic and differentiable association is achieved by using the learnable hyperedge prototype. Through similarity measurements between token features and the prototype, the model generates semantically rich soft hyperedges. By using these soft hyperedges as the medium for message aggregation and dissemination, SoftHGNN models various high-order semantic relationships within visual features. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure adequate and balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements. The source code of our framework is availa

关键词： Computer vision

P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

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arXiv 2020年

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Baráth, Dániel Department of Computer Science & Software Engineering Cal Poly San Luis Obispo United States Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Computer Vision and Geometry Group ETH Zürich Switzerland

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/. © 2020, CC BY.

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