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Cooperative Reinforcement Learning for Military Drones over Large-Scale Battlefields

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles 2024年

作者： Kim, Gyu Seon Lee, Sungjoon Woo, Taejin Park, Soohyun Department of Electrical and Computer Engineering Korea University Seoul Korea Republic of LIG Nex1 Ground Control System Team 3 Korea Republic of Division of Computer Science Sookmyung Women’s University Seoul Korea Republic of

The development of autonomous systems in military drone operations signifies a paradigm shift in modern warfare strategies, and modern warfare emphasizes the crucial role of military drones to the extent that it is called drone war. Despite the importance of these military drones, the efficiency of such operations hinges on the swarm military drones’ ability to operate cooperatively in complex battlefield environments where the target changes dynamically. The proposed algorithm for robust and reliable military drones needs to consider a total of three objectives, i.e., (i) cooperative swarm flight maintenance, (ii) target bombing zone maximization, and (iii) energy efficiency optimization. For this objective, multi-agent deep reinforcement learning (MARL) is utilized for the cooperation of military drones to achieve these objectives optimally. Lastly, this paper considers ‘Shahed-136’ which is one of well-known military drones for realistic battlefield evaluation. Additionally, the military drones employing the proposed algorithm are evaluated in a large-scale battlefield environment spanning 4×104 km3. This paper assesses the efficacy of the proposed approach through simulations that evaluate the swarm flight, success rate of bombing, and energy efficiency of drones, particularly under dynamic target conditions. © 2024 IEEE.

关键词： Deep reinforcement learning

A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

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A network-based bioinformatic analysis for identifying poten...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Martina Brunetti Giulia Fiscon Alessia Di Costanzo Marcello Arca Paola Paci Department of Experimental Medicine Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" National Research Council of Rome Sapienza University of Rome Institute for Systems Analysis and Computer Science "A. Ruberti" Rome Italy Department of Translational and Precision Medicine Policlinico Umberto I Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis.

关键词： Drugs Hypertension Obesity Pathology Epidemics Medical treatment Fats Proposals Bioinformatics Diseases

Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

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Machine Learning-Based Underwater Communication System Using...

CLEO: Science and Innovations, S and I 2022

作者： Jarrett, William A. Avramov-Zamurovic, Svetlana Esposito, Joel M. Nelson, Charles Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States Electrical and Computer Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States

ISBN: (纸本)9781557528209

An underwater communication system using light carrying orbital angular momentum is evaluated using a convolutional neural network through simulated and experimental thermally-generated underwater optical turbulence. 100% classification is demonstrated in no or weak turbulence. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Optical communication

Modelling, Analysis and control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

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arXiv 2023年

作者： Aboudorra, Youssef Gabellieri, Chiara Brantjes, Ralph Sablé, Quentin Franchi, Antonio Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

This paper introduces for the first time the design, modelling, and control of a novel morphing multirotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers' mutual aerodynamic interference. © 2023, CC BY.

关键词： Maneuverability

Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant Systems

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arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

Industrial Scenario of Renewable Energy-Based Electromobility 3rd

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Industrial Scenario of Renewable Energy-Based Electromobilit...

3rd International Conference on Energy Systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

A survey on efficient vision transformers: Algorithms, techniques, and performance benchmarking

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arXiv 2023年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene Zhou, Luping The Department of Computer Control and Management Engineering Sapienza University of Rome 00185 Italy The School of Electrical and Information Engineering Faculty of Engineering The University of Sydney NSW2006 Australia

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution. Generally speaking, it is challenging to employ these architectures in real-world applications due to many hardware and environmental restrictions, such as processing and computational capabilities. Therefore, this survey investigates the most efficient methodologies to ensure sub-optimal estimation performances. More in detail, four efficient categories will be analyzed: compact architecture, pruning, knowledge distillation, and quantization strategies. Moreover, a new metric called Efficient Error Rate has been introduced in order to normalize and compare models' features that affect hardware devices at inference time, such as the number of parameters, bits, FLOPs, and model size. Summarizing, this paper firstly mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios. Toward the end of this paper, we also discuss open challenges and promising research directions. © 2023, CC BY.

关键词： computer vision

ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE SYSTEM CONSIDERING UNCERTAIN FREQUENCY CHANGE

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ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE SYS...

CIRED 2022 Shanghai Workshop

作者： Cao, Yongji Zhang, Hengxu Zhang, Xiaoning Li, Changgang Qin, Hao Gao, Zhimin Centre for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781839538391

With the rapid development of distributed renewable energies, the number and severity of the active power disturbances that affect frequency stability have increased. The distributed energy storage system (DESS) characterized by high flexibility and dispatchability can provide distribution network with frequency regulation services after disturbances. This paper proposes a robust optimal scheduling approach for DESSs considering uncertain frequency changes. The concept of the probabilistic transient frequency deviation security margin (PTFDSM) is presented to quantify the power deficit that the system can tolerate. And an extended system frequency response model is employed to obtain the PTFDSM. Then, a robust optimization model is established to minimize the network loss and maintain the operating point of DESSs within the PTFDSM for frequency stability. The AC power flow constraints are introduced into the optimization model. And the second-order cone relaxation method is used to deal with the nonlinear constraints through relaxation variables. Based on the column-and-constraint generation method, the relaxed second-order cone optimization model is solved, and scheduled DESS power is attained. A case study is carried out to verify the effectiveness of the proposed approach. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Energy storage