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IEEE Conference on Decision and Control

作者： Lukas Brunke Siqi Zhou Angela P. Schoellig Dynamic Systems Lab Institute for Aerospace Studies University of Toronto Canada University of Toronto Robotics Institute and the Vector Institute for Artificial Intelligence Toronto Department of Electrical and Computer Engineering Technical University of Munich Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties. These algorithms often make assumptions about the underlying unknown dynamics and, as a result, can provide safety guarantees. This is more challenging for other widely used learning-based decision making algorithms such as reinforcement learning. Furthermore, the majority of existing approaches assume access to state measurements, which can be restrictive in practice. In this paper, inspired by the literature on safety filters and robust output-feedback control, we present a robust predictive output-feedback safety filter (RPOF-SF) framework that provides safety certification to an arbitrary controller applied to an uncertain nonlinear control system. The proposed RPOF-SF combines a robustly stable observer that estimates the system state from noisy measurement data and a predictive safety filter that renders an arbitrary controller safe by (possibly) minimally modifying the controller input to guarantee safety. We show in theory that the proposed RPOF-SF guarantees constraint satisfaction despite disturbances applied to the system. We demonstrate the efficacy of the proposed RPOF-SF algorithm using an uncertain mass-spring-damper system.

关键词： Uncertainty Heuristic algorithms Decision making Reinforcement learning Filtering algorithms Nonlinear control systems Prediction algorithms

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Sparse Gaussian Process Regression using Progressively Growing Learning Representations

Sparse Gaussian Process Regression using Progressively Growi...

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IEEE Conference on Decision and Control

作者： Christos N. Mavridis George P. Kontoudis John S. Baras Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park USA Dept. of Aerospace Engineering Maryland Robotics Center Inst. of Systems Research University of Maryland College Park MD USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. A series of entropy-regularized optimization problems is solved sequentially, introducing a bifurcation phenomenon, according to which, pseudo-inputs are gradually generated. This results in an active learning approach, which, in contrast to most existing works, can modify already selected pseudo-inputs and is trained using a recursive gradient-free stochastic approximation algorithm. Finally, the proposed algorithm is able to incorporate prior knowledge in the form of a probability density, according to which new observations are sampled. Experimental results showcase the efficacy and potential advantages of the proposed methodology.

关键词： Annealing Gaussian processes Bifurcation Approximation algorithms Optimization

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Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control systems

arXiv

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arXiv 2022年

作者： Brunke, Lukas Zhou, Siqi Schoellig, Angela P. The Dynamic Systems Lab Institute for Aerospace Studies University of Toronto Canada The University of Toronto Robotics Institute The Vector Institute for Artificial Intelligence Toronto Canada The Department of Electrical and Computer Engineering Technical University of Munich Germany

关键词： Controllers

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

arXiv

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arXiv 2023年

作者： Sihite, Eric Slezak, Filip Mandralis, Ioannis Salagame, Adarsh Ramezani, Milad Kalantari, Arash Ramezani, Alireza Gharib, Morteza The Department of Aerospace Engineering California Institute of Technology Pasadena United States The Silicon Synapse Labs Department of Electrical and Computer Engineering Northeastern University Boston United States The Robotics and Autonomous Systems DATA61 Csiro BrisbaneQLD4069 Australia The Jet Propulsion Laboratory Pasadena United States

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets 6

Creating Own AI Datasets from Different Language Sources Eff...

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6th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2023

作者： Molnar, Gyorgy Cserko, Jazsef Nagy, Eniko Balogh, Zoltan Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University János Csere Apáczai Faculty Győr Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Győr Hungary Institute of Cyberphysical Systems Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary Constantine the Philosopher University in Nitra Faculty of Natural Sciences and Informatics Department of Informatics Nitra94901 Slovakia

ISBN: (纸本)9798350328752

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation. © 2023 IEEE.

关键词： Artificial intelligence

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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Research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Solving Colored Traveling Salesman Problem via Multi-neighborhood Simulated Annealing Search 18

Solving Colored Traveling Salesman Problem via Multi-neighbo...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Yangming Xu, Wenqiang Fu, Zhang-Hua Zhou, Mengchu East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen518172 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Department of Cyber-Physical Systems St. Petersburg State Marine Technical University St. Petersburg198262 Russia

ISBN: (纸本)9781665440486

A colored traveling salesman problem (CTSP) is an important variant of the well-known multiple traveling salesman problem, which uses colors to differentiate salesmen's accessibility to individual cities to be visited. As a highly useful model for some complex scheduling problems, CTSP is NP-hard. A Multi-neighborhood Simulated Annealing Search (MSAS) approach is proposed to solve it in this paper. Starting from an initial solution, it iterates through two complementary neighborhoods: intra-route and inter-route neighborhoods. Experiments on three groups of 60 widely-used benchmark instances show that it achieves highly competitive performance compared to state-of-the-art algorithms. Moreover, MSAS can be integrated into other search methods to further improve performance, which is demonstrated by using a recently proposed iterated two-phase local search. © 2021 IEEE.

关键词： Traveling salesman problem

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BYTECounter: Improving Vehicle Turning-Movement Counting

BYTECounter: Improving Vehicle Turning-Movement Counting

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International Conference on Intelligent Transportation

作者： Mateus Líbano Monteiro Alexandre Bernardino H. Sofia Pinto Computer Science: Artificial Intelligence and Cybersecurity from Instituto Superior Técnico Universidade de Lisboa Portugal Department of Electrical and Computer Engineering Institute for and Systems and Robotics of Instituto Superior Técnico Universidade de Lisboa Portugal INESC-ID and Department of Computer Science and Artificial Intelligence Instituto Superior Técnico Universidade de Lisboa Portugal

This paper proposes a methodology to enhance the accuracy of vehicle turning-movement counting in video footage. The methodology uses a three-component system comprising object detection, object tracking, and object turning-movement counting. To improve vehicle detection, a custom vehicle neural network called YOLOv7-vehicles-0.1 was trained using a unique vehicle dataset comprising COCO, KITTI, and OpenImages datasets. Additionally, a novel tracker named BYTECounter was developed, using two virtual drawing objects, Regions and Lines, to count vehicles' turning movements based on trajectory information. The performance of our system was evaluated using the UA-DETRAC dataset, achieving an overall Movement Counting Accuracy (MCAcc) of 93.5% with a processing rate of 82.86 frames per second. This paper demonstrates that BYTECounter outperforms the state-of-the-art tracker, BYTE, and highlights the superiority, in terms of counting metrics, of YOLOv7-vehicles-0.1 over YOLOv7 models trained on the COCO dataset.

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Crossing Domains for AU Coding: Perspectives, Approaches, and Measures

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 158-171页

作者： Ertugrul, Itir Onal Cohn, Jeffrey F. Jeni, Laszlo A. Zhang, Zheng Yin, Lijun Ji, Qiang Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Pittsburgh PittsburghPA15260 United States Robotics Institute Carnegie Mellon University PittsburghPA13902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

Facial action unit (AU) detectors have performed well when trained and tested within the same domain. How well do AU detectors transfer to domains in which they have not been trained? We review literature on cross-domain transfer and conduct experiments to address limitations of prior research. We evaluate generalizability in four publicly available databases. EB+ (an expanded version of BP4D+), Sayette GFT, DISFA and UNBC Shoulder Pain (SP). The databases differ in observational scenarios, context, participant diversity, range of head pose, video resolution, and AU base rates. In most cases performance decreased with change in domain, often to below the threshold needed for behavioral research. However, exceptions were noted. Deep and shallow approaches generally performed similarly and average results were slightly better for deep model compared to shallow one. Occlusion sensitivity maps revealed that local specificity was greater for AU detection within than cross domains. The findings suggest that more varied domains and deep learning approaches may be better suited for generalizability and suggest the need for more attention to characteristics that vary between domains. Until further improvement is realized, caution is warranted when applying AU classifiers from one domain to another. © 2019 IEEE.

关键词： Behavioral research

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