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Distributed strategies for pursuit-evasion of high-order integrators

Autonomous Intelligent systems 2024年第1期4卷 8-20页

作者： Panpan Zhou Yueyue Xu Bo Wahlberg Xiaoming Hu Department of Mathematics KTH Royal Institute of TechnologySE 10044 StockholmSweden Division of Decision and Control Systems KTH Royal Institute of TechnologySE 10044 StockholmSweden Department of Automation Shanghai Jiao Tong University201100 ShanghaiChina

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition *** error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities,accelerations and higher order information of ***,our approach only requires agents to exchange position information or to measure the relative positions of the *** distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the ***,after establishing a sufficient and necessary condition for a class of high-order integrators,we present conditions for capture and formation control with exponential convergence for three scenarios:one-pursuer-one-evader,multiple-pursuer-one-evader,and *** is shown that the conditions depend on the structure of the communication graph,the weights in the control law,and the expected formation ***,the effectiveness of the proposed algorithm is demonstrated through simulation results.

关键词： Multi-agent systems Pursuit-evasion problems High-order integrators Distributed control

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Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream 11

Real-time HOG+SVM based object detection using SoC FPGA for ...

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11th Mediterranean Conference on Embedded Computing, MECO 2022

作者： Wasala, Mateusz Kryjak, Tomasz Agh University of Science and Technology Department of Automatic Control and Robotics Krakow Poland

ISBN: (纸本)9781665468282

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors (e.g. the YOLO - You Only Look Once - family) use deep convolutional neural networks. However, due to their high computational complexity, they are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on {AMD} {Xilinx Zynq UltraScale}+ {MPSoC} (Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition, 3840 times 2160 {pixels}) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Remote Driving of Road Vehicles: A Survey of Driving Feedback, Latency, Support control, and Real Applications

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-22页

作者： Zhao, Lin Nybacka, Mikael Aramrattana, Maytheewat Rothhamel, Malte Habibovic, Azra Drugge, Lars Jiang, Frank Department of Engineering Mechanics KTH Royal Institute of Technology Teknikringen 8 Stockholm Sweden Linköping Sweden Scania CV AB Stockholm Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases. IEEE

关键词： Vehicles

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Diploma Projects for LAB Equipment Rental - How Students Can Help University in the Covid-19 Era 9

Diploma Projects for LAB Equipment Rental - How Students Can...

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9th IEEE International Conference on e-Learning in Industrial Electronics, ICELIE 2022

作者： Bauer, Waldemar Grobler-Debska, Katarzyna Kucharska, Edyta Baranowski, Jerzy AGH University of Science and Technology Department of Automatic Control Robotics Krakow Poland

ISBN: (数字)9781665489904

ISBN: (纸本)9781665489904

Covid-19 pandemic hit a giant blow on in-person education. This was especially difficult when teaching laboratory related exercises. In this paper we show that we can treat this hardship as an opportunity, and engage students into creating tools that help their colleagues. In this particular case we present diploma projects that constituted equipment rental apps. We show that such form of diploma increases motivation and leads to nice results. © 2022 IEEE.

关键词： Students

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Stability and Performance Analysis of Model Predictive control of Uncertain Linear systems 63

Stability and Performance Analysis of Model Predictive Contr...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Changrui Shi, Shengling De Schutter, Bart Delft University of Technology Delft Center for Systems and Control Delft Netherlands Massachusetts Institute of Technology Department of Chemical Engineering Cambridge United States

ISBN: (纸本)9798350316339

Model mismatch often presents significant challenges in model-based controller design. This paper investigates model predictive control (MPC) for uncertain linear systems with input constraints, where the uncertainty is characterized by a parametric mismatch between the true system and its estimated model. The main contributions of this work are twofold. First, a theoretical performance bound is derived using relaxed dynamic programming. This bound provides a novel insight into how the prediction horizon and modeling errors affect the suboptimality of the MPC controller to the oracle infinite-horizon optimal controller, which has complete knowledge of the true system. Second, sufficient conditions are established under which the nominal MPC controller, which relies solely on the estimated system model, can stabilize the true system despite model mismatch. Numerical simulations are presented to validate these theoretical results, demonstrating the practical applicability of the derived conditions and bounds. These findings offer practical guidelines for achieving desired modeling accuracy and selecting an appropriate prediction horizon in designing certainty-equivalence MPC controllers for uncertain linear systems. © 2024 IEEE.

关键词： Predictive control systems

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Game theory system optimization in a supplier-customer transport network 10

Game theory system optimization in a supplier-customer trans...

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Bilbiie, Radu Popescu, Dumitru Dimon, Catalin Teme, Marius Borne, Pierre Department of Automatic Control and Systems Engineering University Politehnica of Bucharest Bucharest Romania Ecole Centrale de Lille Villeneuve d'Ascq France

The development of modern IT technologies and resources, offer in many economic-industrial applications, efficient solutions for planning and managing of specific activities. New research directions in the field of management and optimization of supply-customer (SC) systems are defined and evaluated. The paper proposes a management model based on a causal graph structure, which expresses the connection and interdependence between the two entities involved in the specific activities of the field, the suppliers of goods and services and the consumers of these goods and products. Information related to production and storage capacity, up-to-date requirements for customer needs, delivery planning, production and consumption dynamics are considered. The transport between the actors involved in the system is an important component with weight in the costs of supply-sale activities. The optimal solution for the supply-customer transport system is proposed based on different strategies of game theory. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Game theory

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System 4

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender systems Scholarly Research Serendipity

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning 35

Microservice Deployment for Satellite Edge AI Inference via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yu, Zhiyong Cheng, Hei Victor Yang, Zhanpeng Liu, Xin Jiang, Yuning Zhou, Yong Shi, Yuanming ShanghaiTech University School of Information Science and Technology Shanghai201210 China Aarhus University Department of Electrical and Computer Engineering Denmark Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350362244

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices' communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time. © 2024 IEEE.

关键词： Markov processes

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