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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Safarpour Motealegh Mahalegi, Homayoun Farhadi, Amirfarhad Molnar, Gyorgy Nagy, Eniko Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Obuda University Hungary Szechenyi University Apaczai Csere János Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Gyor Hungary Obuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350385601

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems. © 2024 IEEE.

关键词： Convolutional neural networks

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The Effect of Coil Geometry and Winding Method on the Electromagnetic Launcher Performance 48

The Effect of Coil Geometry and Winding Method on the Electr...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Mohamed Abdo, Mohamed Magdy Fanni, Mohamed Miyashita, Tomoyuki Ahmed, Sabah Mohamed Egypt-Japan University of Science and Technology Mechatronics and Robotics Department Alexandria Egypt Ain Shams Univeristy Faculty of Engineering Engineering Physics and Mathematics Department Cairo Egypt Mansoura University Production Engineering and Mechanical Design Department Mansoura Egypt Waseda University School of Creative Science and Engineering Department of Modern Mechanical Engineering Tokyo Japan Assuit University Electrical Engineering Department Assuit71515 Egypt

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Electromagnetic launchers become spread in many applications. This type of launcher is used to accelerate a slug made of ferromagnetic material. Many researchers compete to enhance the performance of this type. The launcher consists of simple components such as a capacitor bank, electronic switches, launcher coil, and charging circuit. In this paper, the authors have physically built the launcher system and reinforced the launcher performance by changing the winding method and making the trigger coil in a conical shape to reduce the deceleration effect of the last half of the trigger, this modification has proven its superiority over the straight coil. © 2022 IEEE.

关键词： Electromagnetic launchers

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

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Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots

arXiv

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arXiv 2024年

作者： Pustina, Pietro Calzolari, Davide Albu-Schäffer, Alin De Luca, Alessandro Della Santina, Cosimo Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Institute of Robotics and Mechatronics German Aerospace Center Oberpfaffenhofen82234 Germany Department of Informatics Technical University of Munich Garching85748 Germany Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory –called eigenmanifolds– as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model. © 2024, CC BY.

关键词： Structural dynamics

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Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

Real-Time Plant Recognition and Crop Row Navigation for Auto...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Fazal Nasir Muhammad Haris Bilawal Khan Muhammad Tufail Muhammad Tahir Khan Zhang Dong Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments.

关键词： Service robots Plants (biology) Spraying Crops Wheels Robot sensing systems Visual servoing

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Study of the Automated Process-Induced Residual Stresses in Multi-Axis Laminates

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Procedia Structural Integrity 2022年 42卷 1328-1335页

作者： Mohamed M.A. Ammar Bijan Shirinzadeh Robotics and Mechatronics Research Laboratory Department of Mechanical and Aerospace Engineering Monash University Clayton 3800 Australia Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria 11432 Egypt

The integrity of the composite structures is affected by the manufacturing conditions. Controlling the ply orientation during the manufacturing process plays an important role in defining the induced residual stresses inside the laminate. This work evaluates the generated residual stresses inside multi-axis laminates of carbon fiber reinforced polymers. The samples are manufactured using the robotic fiber placement technique. The residual stresses are measured through the incremental hole-drilling method. Finite element modeling is used to provide the calibration process of the strain measurements. Additionally, microstructure analyses have been performed to show the configuration of the manufactured samples. The effect of the laminate configurations and plies orientations are investigated and compared. The layers’ orientations have shown a considerable effect on the generated stresses.

关键词： Multi-axis composites Residual stresses Carbon fiber Robotic fiber placement

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QWriter System for Robot-Assisted Alphabet Acquisition

QWriter System for Robot-Assisted Alphabet Acquisition

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Aida Amirova Nurziya Oralbayeva Zhansaule Telisheva Aida Zhanatkyzy Aidar Shakerimov Shamil Sarmonov Arna Aimysheva Anara Sandygulova Graduate School of Education Nazarbayev University Astana Kazakhstan Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan

The present study applies a novel Reinforcement Learning-based (RL) alphabet learning system named QWriter for the acquisition of the Kazakh Latin alphabet. We conducted a between-subject design experiment with 108 Kazakh children aged 6-8 years old in a public school and compared their learning rates across the two conditions: an RL-based QWriter robot and a human tutor (HT) as a baseline. The results show that children learned significantly more letters with the HT compared to the QWriter robot, showing that the RL-based robot is not effective for learning in the short term. Yet, we observe some interesting results by children’s age and gender. The results need further investigation comparing the QWriter with other robot baselines with different roles and across various learning tasks.

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The development and evaluation of a combined initial alignment algorithm for strapdown inertial navigation system 3

The development and evaluation of a combined initial alignme...

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3rf International Scientific Conference on Metrological Support of InnovativeTechnologies, ICMSIT 2022

作者： Afonin, A.A. Sulakov, A.S. Maamo, M.S. Shapovalov, N.A. Moscow University for Industry and Finance "synergy" Faculty of Robotics and Mechatronics 9/14 street Meshchanskaya Moscow129090 Russia Department of Control Systems Informatics and Power Engineering 4 Volokolamskoe shosse Moscow125993 Russia

This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the aircraft is not moving. This method of initial alignment has the advantages of being conducted based on the Earth's equatorial (Greenwich) coordinate system, which is also used by the aircraft onboard navigation system. In the first stage, the coarse initial alignment is carried out, and this is done according to the orientation parameters using the vector matching method. Then, in the second stage, a fine procedure is carried out, which may be looked at as an integrated system implemented using optimal Kalman filter. In this case, the initial parameters of orientation and navigation are refined, as well as the systematic errors of the inertial sensors are estimated and corrected. The simulation results show that the required level of initial alignment accuracy could be achieved by applying coarse and fine initial alignment procedures not sequentially but in a combination. © Published under licence by IOP Publishing Ltd.

关键词： Inertial navigation systems

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Robot Control [TC Spotlight]

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IEEE robotics & Automation Magazine 2025年第2期32卷 197-198页

作者： Cosimo Della Santina Sylvia Herbert Manuel Keppler Kaoru Yamamoto TU Delft Faculty of Mechanical Engineering Delft The Netherlands University of California at San Diego Faculty of Mechanical and Aerospace Engineering San Diego CA USA German Aerospace Center Robotics and Mechatronics Department Oberpfaffenoffen Germany Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

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Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer

Design and Implementation of Model Predictive Control (MPC) ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Adrees Khan Fazal Nasir Muhammad Tufail Muhammad Haris Muhammad Tahir Khan Zhang Dong Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Model Predictive Control (MPC) is a control scheme that involves predicting the future behavior of a system and optimizing control actions to accomplish the desired objective. In this study, we develop an intelligent control algorithm, based on MPC to regulate the target pressure in variable-rate agriculture sprayer robots. Modeling and simulation steps of the spraying system are developed using MATLAB/Simulink environment, before passing to the description of the MPC algorithm. Real-time implementation of the MPC algorithm was conducted on an Arduino Mega 2560 controller board by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink to experimentally validate the preliminary results of simulations. The work evaluates MPC to regulate the pressure in the system and compares the results with a traditional PID control system. Moreover, MPC is a novel method for nonlinear system control that achieves zero steady-state error, low transient response, and reduces peak overshoot compared to the results obtained with a PID controller, thereby reducing the waste of chemicals, and minimizing the toxicology and environmental risk.

关键词： Transient response Toxicology Software packages Spraying Agriculture Regulation Real-time systems

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