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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47828 条记录，以下是971-980 订阅

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Multi-Task Learning for Tomato Crop Disease Detection and Severity Estimation using CNN Framework

Multi-Task Learning for Tomato Crop Disease Detection and Se...

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Raghul, P. Kavitha, A. Daniel Madan Raja, S. Rathiya, R. Krithik, C.S. Pranesh, K.R. Dr. N.G.P Institute Of Technology Department Of Information Technology Coimbatore India School Of Computer Science And Technology Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350364828

The cultivation of tomatoes holds immense significance in the agricultural landscape of the Indian region, also it plays a substantial role in economic and nutritional contributions. Nonetheless, the widespread occurrence of diseases poses a notable risk to both the quantity and quality of tomato yields. This study endeavors to create a real-time application employing a deep learning mechanism in conjunction with a multi-task method. In this study, the process utilizes convolutional neural networks (CNN) and VGG16 architecture. The determination of disease severity and the stage of the disease is conducted through multi-task learning methods integrated with CNN, providing a comprehensive approach to address the challenges associated with disease threats in tomato cultivation. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Hyperspectral Image Enhancement with FPGA Hardware Accelerators

Optimizing Hyperspectral Image Enhancement with FPGA Hardwar...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Rajesh, N. Naveen, I.G. Shankari, N. Kumthekar, Parth Sheetal Khan, Sufiyan Ahmed Mohammed, Rayyan Nite Meenakshi Institute of Technology Department of Ece Bengaluru India Nmam Institute of Technology Karnataka Nitte India

ISBN: (纸本)9798350388602

Hyperspectral image processing plays a crucial role in various applications, including remote sensing and agricultural monitoring, yet it poses significant computational challenges due to the high dimensionality and intricate data structures involved. Traditional software-based methods often suffer from high processing time and latency, limiting their effectiveness in real-time applications. To address these challenges, we propose a hardware accelerator implemented on the Genesys 2 Kintex-7 FPGA Development Board, designed specifically for hyperspectral image enhancement. This FPGA-based solution employs parallel processing and optimized algorithms, including interpolation and super-resolution techniques, to significantly improve processing efficiency. We evaluate the performance of our proposed method using the Indian Pines dataset, a standard benchmark for hyperspectral imaging. Experimental results demonstrate that our FPGA implementation achieves a processing time of 50 ms, significantly faster than CPU and GPU solutions, while also reducing power consumption and improving energy efficiency. These findings underscore the potential of FPGA technology for enhancing hyperspectral image processing in resource-constrained environments. © 2024 IEEE.

关键词： Image enhancement

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Swarm Intelligence in robotics: Optimizing PSO Parameters for Target Search in Single and Multiple Scenarios 11

Swarm Intelligence in Robotics: Optimizing PSO Parameters fo...

引用

11th international conference on Mechatronics and robotics engineering, ICMRE 2025

作者： Sharma, Vikas Gupta, Rahul Sharma, Kapil Delhi Technological University Department of Information Technology India

ISBN: (纸本)9798331509293

Target-tracing applications have attracted much interest in unmanned vehicles and mobile robots, especially where human access is limited or dangerous. Several computational techniques such as genetic algorithms, evolutionary computations, and neural networks have been explored to improve the entire system's control mechanism and efficiency. This paper addresses PSO as a high-performance method for enabling collaborative robotic search operations. It has many critical parameters influencing its performance, which are finely optimized using another PSO process to make the best use of this optimization technique. Experimentations set forth show that this indeed does much improve the efficiency and accuracy of searches carried out by robots. These validation experiments work on single-target and multi-target cases to present vivid effectiveness. The results emphasize the ability of PSO to contribute toward autonomous search and rescue operations and all other applications. © 2025 IEEE.

关键词： Genetic algorithms

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Precision Placement of DLOs with Flexibility via Shape Manipulation

Precision Placement of DLOs with Flexibility via Shape Manip...

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Dai, Zhenjiu Yu, Hongyu Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong

ISBN: (纸本)9781665481090

In this paper, we present an open-loop method designed to enhance the precision of placing flexible linear objects on both flat and curved surfaces. Unlike rigid objects, where the motion can be accurately estimated from the configuration of a single point, the motion of soft objects is influenced by non-negligible internal forces, resulting in inconsistent and unpredictable behavior across different segments. We leverage the relationship between object shape and reaction forces, as explored through Lagrangian mechanics, to determine the desired shape that the object should be manipulated during placement, thereby mitigating unexpected displacements. A sequence of gripper motions is generated based on practical applications. We conducted a series of experiments utilizing a robotic arm to align and place deformable linear objects (DLOs) with different flexibility onto surfaces with diverse curvatures, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Robotic arms

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Dynamic Load Adaptive Control for Legged Robot with Manipulator Based on Whole-Body MPC

Dynamic Load Adaptive Control for Legged Robot with Manipula...

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Du, Ziqian Huang, Tianlun Wang, Shijie Wang, Weijun Liu, Xiao Li, Pengge Feng, Wei Southern University of Science and Technology Shenzhen518055 China School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen University of Advanced Technology Shenzhen518107 China

ISBN: (纸本)9781665481090

Legged robots equipped with manipulators exhibit significant benefits in unstructured manipulation tasks. How-ever, ensuring that these robots successfully perform locomotion and manipulation tasks remains challenging, especially when both the center-of-mass (CoM) state and object parameters are unknown and time-varying. Based on the whole-body model predictive control (MPC), a novel online payload identification method is proposed to compensate for modeling errors and tackle loco-manipulation tasks. The whole-body MPC integrates the robot state and user inputs in the robot centroidal dynamics. Moreover, the online payload identification method facilitates evaluating the time-varying CoM state and the parameters of unknown objects. Additionally, the MPC efficacy of the centroidal dynamics model is evaluated by a single rigid body dynamics model, considering both scenarios: with and without the object dynamics. The robot can manipulate an unknown and time-varying load of up to 20kg, equivalent to 50% of the manipulator's mass. After online identification, the linear and rotational root mean square errors of the manipulator are reduced by approximately 70%. The results indicate that the proposed strategy can effectively coordinate the interaction force/moment between the robot and the manipulated object while maintaining stability. © 2024 IEEE.

关键词： Manipulators

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Hotel Service Robot Control System 4

Hotel Service Robot Control System

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Yu, Hao Gong, Fan Zheng, Qi Guangzhou Institute of Science and Technology Faculty of Intelligent Manufacturing and Electrical Engineering Guangzhou China

ISBN: (纸本)9798331531225

With the advancement of technology and the growing demand for intelligent and efficient services, the traditional hotel service model is increasingly unable to meet people's expectations for personalized and non-contact experiences. In this context, this study proposes an STM32-based control system for hotel service robots. The system integrates motor control, voice recognition, touch screen interaction and an innovative doorbell module to improve service quality. It also runs the bidirectional A∗ path planning algorithm with the help of Raspberry Pi, which enables the robot to navigate autonomously in complex hotel environments and perform precise food delivery and guest guidance tasks. Experimental results show that this system can improve the service efficiency of hotels and reduce the waste of laboratory resources in the traditional hotel industry, thus promoting the transformation of traditional hotels into smart hotels, while improving customer experience, attracting more guests and enhancing the competitiveness of hotels. © 2024 IEEE.

关键词： Robot programming

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A New Practical Robust Control Design for Model-based Uncertain Collaborative Robot 8

A New Practical Robust Control Design for Model-based Uncert...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Zhen, Shengchao Li, Runtong Liu, Xiaoli Chen, Ye-Hwa School of Mechanical Engineering Hefei University of Technology Anhui Hefei230009 China School of Artificial Intelligence Anhui University Anhui Hefei230601 China The Geroge W.Woodrufi School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Anhui Hefei230601 China Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Anhui Hefei230601 China

ISBN: (纸本)9798350300178

A new practical robust control method is proposed for uncertain collaborative robots with six degrees of freedom, improving their trajectory tracking performance. The MPDP controller combines a model-based PD control and a robust control algorithm. Lyapunov theory analysis proves that this method ensures uniform boundedness and final boundedness of the system. Simulation results show that the MPDP controller outperforms the traditional PID controller, providing better robustness and smaller steady-state tracking error. © 2023 IEEE.

关键词： Controllers

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An Efficient Attention-Based Deep Reinforcement Learning Model for Traffic Signal Control 7

An Efficient Attention-Based Deep Reinforcement Learning Mod...

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7th international conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Lin, Aodi Chen, Feng University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9798331529314

Traffic Signal Control (TSC) is a significant challenge within intelligent transportation systems. As Vehicle-to-Everything (V2X) technology advances, TSC systems are increasingly able to utilize extensive vehicle driving data to enhance decision-making. This paper proposes a reinforcement learning-based model for TSC at urban intersections. We design a vehicle driving information matrix that integrates vehicle position and speed information. The state space includes vehicle driving information and the current traffic signal phase. The action space includes various traffic signal phases. To efficiently extract traffic features from the large state space, the model incorporates an attention mechanism within the neural network. The simulation results on the Simulation of Urban Mobility (SUMO) demonstrate the convergence and generalization capabilities of our model, showing significant advantages in trip duration compared to several benchmark methods. Ablation studies further validate the effectiveness of our proposed position-speed fusion matrix and attention mechanism in extracting traffic feature representations. © 2024 IEEE.

关键词： Deep reinforcement learning

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Approaches for Teaching robotics 20th

Approaches for Teaching Robotics

引用

20th international conference on Remote engineering and Virtual Instrumentation: Open science in engineering, REV 2023 co-organized with the international Edunet World conference, IEWC 2023

作者： Abreu, Paulo Restivo, Maria Teresa Urbano, Diana Chousal, Fátima Arachchige, Dilki LAETA-INEGI Faculty of Engineering University of Porto Porto Portugal LAETA-INEGI University of Porto PortoA3ES Portugal Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9783031424663

This paper presents two different approaches to teaching robotics within the Automation, Instrumentation, and Control Group of the Department of Mechanical engineering at the Faculty of engineering of the University of Porto (FEUP). One is a traditional curricular course and the other is based on tutoring and internship approaches carried out at robotic labs. Regarding the robotics course, a formative assessment process based on the use of pre- and post-evaluation quizzes and its results is presented. In the case of the internship, where the main goal is focused on preparing students of different engineering backgrounds in understanding how to deal with robotic systems in real-world tasks and the relevance this technology can play, it is presented one example of the work carried out that involved the design of rehabilitation exercises for the upper limb of a post-stroke patient, implemented through the patient-robot interaction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative robot Formative evaluation robotics Tutoring

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Classification of COVID-19 Utilizing CT Scan Images Employing the EfficientNet Model

Classification of COVID-19 Utilizing CT Scan Images Employin...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Sharma, Priyanka Sharma, Varsha Computer Science &engineering Rajiv Gandhi Proudyogiki Vishwavidhyalaya Bhopal India

ISBN: (纸本)9798350388800

In the realm of medical imaging, particularly for the critical task of distinguishing between COVID-19 and non-COVID cases, the analysis of CT scans has emerged as a formidable challenge. The primary concern is achieving high accuracy and low loss in predictive models to support diagnostic decisions. To tackle this, various deep learning architectures, including ResNet50, Inception, and VGG19, were employed, yet none matched the proficiency of the Proposed EfficientNet model. Utilizing a dataset enriched through augmentation techniques to yield 17,099 images, the models were trained and tested on a uniform framework of 15 epochs, a consistent learning rate of 0.00001, and an image resolution of 224 × 224. In this standardized environment, the proposed EfficientNet model, with a strategic batch size of 64 and 100 steps per epoch, showcased a substantial performance improvement, attaining a near-perfect training accuracy of 99.54% and a test accuracy of 98.24%, with impressively low training and test losses of 0.015 and 0.145, respectively. Compared to other models, this translates to an accuracy improvement of over 4% on training and approximately 14% on testing. Conclusively, the Proposed EfficientNet model marked an improvement of roughly 5% over the closest competitor, presenting a significant advancement in the field of medical image classification for the detection of COVID-19. © 2024 IEEE.

关键词： COVID-19

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