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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Dynamic Current control of Rare Earth Free Biaxial Excitation Synchronous Machines

Dynamic Current Control of Rare Earth Free Biaxial Excitatio...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Krishna MPK Namburi Prerit Pramod Ion Boldea Iqbal Husain Research and Development Nexteer Automotive Corporation Auburn Hills USA Control Systems Engineering MicroVision Inc. Redmond USA Electrical Engineering Politehnica University Timisoara Timisoara Romania Electrical and Computer Engineering North Carolina State University Raleigh USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Rare-earth free biaxial excited synchronous machines (BESM) are ideally suited for achieving unity power factor at their base speed which enables the characteristic of high-power density and lower cost for a traction motor drive system. Due to the presence of dual excitation sources, the current control design for two coupled subsystems, one consisting of the stator winding currents in synchronous reference frame and the other consisting of rotor field current is proposed. A novel two degree of freedom dynamic current control architecture is proposed, featuring closed-loop frequency response shaping and state feedback decoupling. The performance characteristics of the current control system, including command tracking, disturbance rejection, and robustness, are evaluated through detailed analytical modeling and validated via extensive simulations on a practical BESM.

关键词： Current control Analytical models State feedback Stator windings Rotors 2-DOF Traction motors Robustness Frequency response Synchronous machines

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An Actor-Critic Reinforcement Learning Scheme for Reactive 3D Optimal Motion Planning Based on Fluid Dynamics

An Actor-Critic Reinforcement Learning Scheme for Reactive 3...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Marios Malliaropoulos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT ∗ , highlighting its contribution to the field of 3D optimal motion planning.

关键词： Solid modeling Three-dimensional displays Navigation Reinforcement learning Mathematical models Stability analysis Robustness Planning Safety Optimization

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Observer-Based control of Second-Order Multi-vehicle systems in Bearing-Persistently Exciting Formations

arXiv

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arXiv 2024年

作者： Tang, Zhiqi Fidan, Baris Johansson, Karl H. Mårtensson, Jonas Hamel, Tarek Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden Mechanical and Mechatronics Engineering Department University of Waterloo ON Canada France

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers. © 2024, CC BY-NC-ND.

关键词： Acceleration measurement

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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A Comparative Study of PCA and LDA for Dimensionality Reduction in a 4-Way Classification Framework

Research Square

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Research Square 2024年

作者： Mabrouk, Besma Jazzar, Nessrine Sallemi, Lamia Hamida, Ahmed Ben Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax University Tunisia Networked Objects Control and Communication Systems National Engineering School Sfax Tunisia

Alzheimer’s disease (AD), recognized as the second-most impactful neurological disorder and currently incurable, stands as the leading cause of dementia. An imperative research focus is efficiently diagnosing the stages of patients, distinguishing early or late Mild Cognitive Impairment and AD from those with normal cognitive function. Advancements in anatomical and diffusion-weighted imaging, coupled with machine learning techniques, have significantly progressed in this predictive domain. However, in real-world trials, datasets often contain numerous features, and the curse of dimensionality can introduce challenges such as increased computational complexity, overfitting, and diminished model interpretability. To address these issues, the present study explores the efficacy of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) as dimensionality reduction techniques. LDA, a supervised technique emphasizing class separability, surpasses PCA, particularly in selecting features that significantly contribute to discriminating between classes. The 3D-LDA features obtained were subsequently assessed across various machine learning algorithms, leading to the establishment of a 4-way classification framework that utilized the K-Nearest Neighbors model. The outcome of this evaluation yielded an impressive accuracy rate of 87% in predicting the four different classes. © 2024, CC BY.

关键词： Nearest neighbor search

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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Model Predictive control for Autonomous Driving: Comparing Kinematic and Dynamic Models of Tractor-Trailer systems

Model Predictive Control for Autonomous Driving: Comparing K...

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International Conference on Intelligent Transportation

作者： Gustav Vallinder Jonas Mårtensson Pedro F. Lima Sriharsha Bhat Scania CV AB Södertälje Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tractor-trailer models for model predictive control (MPC). We show in open-loop simulations that a kinematic and a dynamic model are equivalent at low speeds and short discretization time steps. A zero speed singularity and stiff dynamics prevents the usage of the dynamic model in control design, where discretization time steps are longer. A method of discretization is proposed to resolve the low speed feasibility of the dynamic model. In closed-loop simulations, the real-time applicability of the kinematic and dynamic models in a nonlinear MPC is verified.

关键词： control design Optimal control Kinematics Predictive models Real-time systems Stability analysis State estimation Intelligent transportation systems Autonomous vehicles Predictive control

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Energy-Aware Task Allocation for Teams of Multi-mode Robots

arXiv

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arXiv 2025年

作者： Ito, Takumi Funada, Riku Sampei, Mitsuji Notomista, Gennaro Department of Systems and Control Engineering Institute of Science Tokyo Tokyo152-8550 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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