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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation

Robot Trajectron: Trajectory Prediction-based Shared Control...

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IEEE International Conference on robotics and automation (ICRA)

作者： Pinhao Song Pengteng Li Erwin Aertbeliën Renaud Detry Dept. Mechanical Engineering Research Unit Robotics Automation and Mechatronics KU Leuven Leuven Belgium College of Computer Science and Software Engineering Shenzhen University Shenzhen China Flanders Make@KU Leuven Dept. Electrical Engineering Research Unit Processing Speech and Images KU Leuven Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion’s onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, by reducing the operator’s cognitive load through assistance in their anticipated direction of motion. Our novel intent estimator, dubbed the Robot Trajectron (RT), produces a probabilistic representation of the robot’s anticipated trajectory based on its recent position, velocity and acceleration history. By taking arm dynamics into account, RT can capture the operator’s intent better than other SOTA models that only use the arm’s position, making it particularly well-suited to assist in tasks where the operator’s intent is susceptible to change. We derive a novel shared-control solution that combines RT’s predictive capacity to a representation of the locations of potential reaching targets. Our experiments demonstrate RT’s effectiveness in both intent estimation and shared-control tasks. We will make the code and data supporting our experiments publicly available at https://***/detry-lab/public/robot-trajectron

关键词： Codes Dynamics Estimation Grasping Probabilistic logic Cognitive load Trajectory

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Robot-Assisted Navigation for Visually Impaired through Adaptive Impedance and Path Planning

arXiv

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arXiv 2023年

作者： Balatti, Pietro Ozdamar, Idil Sirintuna, Doganay Fortini, Luca Leonori, Mattia Gandarias, Juan M. Ajoudani, Arash HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Robotics and Mechatronics lab Systems Engineering and Automation Department University of Malaga Malaga Spain

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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Study of Computer Vision for Manipulators

Study of Computer Vision for Manipulators

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Perizat Rakhmetova Aldabergen Bektilevov Bakhtiyar Kopenov Makabbat Zhamuratova Saltanat Yussupova Tolkyn Toktamyssova Dept. of Robotics and technical means of automation Satbayev University Almaty Kazakhstan Dept. of Transport engineering and logistics named after M.Tynyshpayev Satbayev University Almaty Kazakhstan Dept. of Motor vehicles and life-safety Academy Logistics and Transport Almaty Kazakhstan Dept. of Electronic Engineering Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan

In recent years, computer vision technology has made significant progress, expanding its application from simple image recognition tasks to complex real-world problems. One such area where computer vision promises to be is the control of manipulators in various fields such as manufacturing, healthcare and robotics. Manipulators, mechanical arms designed to perform tasks, are traditionally controlled using manual input or a preprogrammed sequence. The purpose of this manuscript is to research and analyze computer vision applicable to improve the control and efficiency of manipulators.

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A surgical dataset from the da Vinci Research Kit for task automation and recognition

A surgical dataset from the da Vinci Research Kit for task a...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Irene Rivas-Blanco Carlos J. Pérez Del-Pulgar Andrea Mariani Giuseppe Tortora Antonio J. Reina Dept. Systems Engineering and Automation University of Malaga Malaga Spain Dept. Excellence in Robotics & AI The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Smart Medical Theatre Lab ABzero srl Pisa Italy

The use of large datasets is essential in surgical robotics to advance in the field of recognition and automation of surgical tasks. Furthermore, public datasets allow the comparison of different algorithms and methods to evaluate their performance. The objective of this work is to provide a complete dataset of three common training surgical tasks performed with the da Vinci Research Kit (dVRK). The dataset contains a total of 206 trials performed by twelve different subjects. For each trial, the dataset includes 154 kinematic variables from the dVRK (both master and slave sides) together with the associated video recorded with a camera. All the data has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided.

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Collaborative Learning in Bilateral Academic Training on robotics Design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

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STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing engineering students Programming profession

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Mathematical Modeling of a Mobile Robot's Motion Control System based on LQR

Mathematical Modeling of a Mobile Robot's Motion Control Sys...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Aldabergen Bektilevov Lazzat Kurmangalieva Zhanibek Issabekov Makhabbat Zhamuratova Vinera Baiturganova Perizat Rakhmetova School of Transport Engineering and Logistics Named After M. Tynyshpayev Satbayev University Almaty Kazakhstan Dept. of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Abay Myrzakhmetov Kokshetau University Kokshetau Kazakhstan

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Now, the development of mobile robots is a promising industry, as these types of robots are acquiring more and more functionality for solving various tasks and in most cases are developed as universal robots that help perform different tasks simultaneously. In this regard, this article discusses the problem of developing a mathematical model of a motion control system for a helicopter-type mobile robot. A mathematical model of a mobile robot motion control system has been developed, using an unmanned aerial vehicle (UAV) as an example, which allows you to track the flight movement in state space. Simulation model of UAV control system based on a PD-controller has been developed using Matlab/Simulink. The simulation model controls the spatial motion of the UAV through four control channels (roll, pitch, yaw, and altitude). The calculations were performed based on the optimal method using the integral quadratic criterion for a nonstationary linear control system. The simulation results show that the developed mathematical model is an optimal simulation for stabilizing a mobile robot (UAV) in desired positions and orientations.

关键词： Space vehicles Service robots Tracking Aerospace electronics Autonomous aerial vehicles Control systems Mathematical models Mobile robots Motion control Vehicle dynamics

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Analysis of ECG Noise Cancellation and Abnormality Classification using Various Adaptive Algorithms

Analysis of ECG Noise Cancellation and Abnormality Classific...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： S M Ramesh S Nithya V. V. Teresa V Uma Maheswari M A Raja S Krishnakumar Department of ECE KPR Institute of Engineering and Technology Coimbatore India Dept. of ECE Sri Eshwar College of Engineering Coimbatore India Dept. of ECE Kumaraguru College of Technology Coimbatore India Department of ECE Nehru Institute of Engineering and Technology Coimbatore India Department of Robotics and Automation Sri Ramkrishna Engineering College Coimbatore India

Noise cancellation and abnormality detection in ECG signal is a tedious task in real time environment. These noises arise due to power line interference and human movements. There are various digital filters readily available to reduce noise in ECG signal but the computational cost and complexity is high. To overcome this drawback, adaptive filters are introduced and abnormal ECG signal is also detected using adaptive algorithms. This paper talks about multilevel pipelining and cutest retiming techniques in adaptive filters, for example, LMS (Least mean square), BLMS (Block LMS), NLMS (Normalized LMS), BNLMS (Block NLMS), VSLMS (Variable advance size LMS) and BVSLMS (Block VSLMS) calculations. This paper also classifies various abnormal ECG signal and detects the type of disease. It is observed that there is 15% increase in SNR and 3% reduction in MSE compared to the conventional algorithms.

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Design and Evaluation of a Flexible Sensorized Robotic OCT Neuroendoscope

Design and Evaluation of a Flexible Sensorized Robotic OCT N...

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International Symposium on Medical robotics (ISMR)

作者： Junyan Yan Peng Chen Jibiao Chen Jiaqi Xue Chao Xu Yufu Qiu Haiyang Fang Yiang Lu George Kwok Chu Wong Yun-Hui Liu Wu Yuan Shing Shin Cheng Dept. of Mechanical and Automation Engineering Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Dept. of Biomedical Engineering Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Walker Dept. of Mechanical Engineering University of Texas at Austin United States Department of Surgery Division of Neurosurgery The Chinese University of Hong Kong Hong Kong Shun Hing Institute of Advanced Engineering and Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong

Endoscopic optical coherence tomography (OCT) has demonstrated its capability to visualize the fine microstructures and subtle lesions inside human organs in vivo. However, the limited imaging dept. and the lack of distal dexterity of the current rigid OCT endoscope prohibits its proactive assessment and clinical utilities in the confined, sensitive, and yet relatively large (a few centimeters) surgical space, such as the lesion in deep brain. In this work, we developed a flexible sensorized robotic OCT neuroendoscope, which combines a 2-degree-of-freedom (DOF) cable-driven continuum manipulator (CM) with an ultrahigh-resolution 800-nm OCT probe and a multi-core fiber Bragg grating (MCFBG) fiber sensor. The MCFBG measurements of the bending posture of the endoscope was validated through experiments. By leveraging the OCT A-line signals, axial distance was precisely measured between the OCT probe tip and the surrounding tissue boundary. The feasibility of OCT neuroendoscope was further demonstrated to navigate and steer inside a porcine brain-simulated lesion phantom in an ex-vivo experiment. Our OCT neuroendoscope offers distal maneuverability and ultrahigh-resolution imaging capability at an axial resolution of about 2.4 μm (in air), suggesting its clinical potential for minimally-invasive imaging-guided diagnosis and treatment in deep brain in vivo.

关键词： Optical fiber sensors In vivo Endoscopes Optical fiber cables Imaging Bending Robot sensing systems

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