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检索条件"机构=Robotics and Electrical and Computer Engineering"

共 4373 条记录，以下是61-70 订阅

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Applying Radio Frequency Attenuators to Reduce the Impact of U-NII-4 Interference on DSRC Channel 180

Applying Radio Frequency Attenuators to Reduce the Impact of...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hatcher, Grayson Tello, Marco Kirkwood, Nathan Kihei, Billy Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States Kennesaw State University Department of Robotics and Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia Department of Transportation. Past research confirmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On-Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs currently in use that could increase PRR, by developing and testing RF technologies, primarily attenuators available on the market currently. These solutions could be easily adapted to infrastructure systems, and only require they run in-line with the stock antennas already in use. We were able to accomplish this goal, as well as made findings that may be beneficial within the world of RF communications for RSU devices. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis with Stereo Cameras in Unpredictable Environments

Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Patel, Shrey Kovarovics, Aiden More, Aaditya Chun, Carrington Fletcher, Montreal Tanveer, M. Hasan Chakravarty, Sumit Voicu, Razvan Kennesaw State University Dept. of Robotics & Mechatronics Engineering Marietta United States Kennesaw State University Dept. of Electrical and Computer Engineering Marietta United States

ISBN: (纸本)9798331504847

Current robotic arm systems typically depend on pretrained models or function within controlled environments using known positions or fiducial markers for object manipulation. Although these methods achieve high accuracy, they face limitations due to lengthy training processes and the need for carefully controlled setups. This research introduces a methodology that leverages stereo cameras to develop a generalized algorithm aimed at overcoming these constraints and enhancing robotic manipulation in uncontrolled environments. The proposed solution utilizes an Intel RealSense stereo camera in conjunction with YOLO segmentation models for initial object identification. It combines segmentation meshes with the camera's depth values to create a detailed three-dimensional point cloud of the target object's surface. This point cloud is analyzed through Random Sample Consensus (RANSAC) and geometry-based feature extraction techniques, which allow the algorithm to approximate the object's shape as a combination of fundamental 3D forms, such as cuboids, cylinders, and spheres. A grasp point is then estimated by identifying suitable surfaces for grasping with a two-pronged gripper, followed by calculating an optimal approach angle and position kinematics for the robotic arm. This innovative approach enables robotic arm systems to autonomously adapt to various objects in uncontrolled environments, significantly expanding their practical applications. For example, the methodology could support modular robotic systems designed for sample collection in extraterrestrial missions, where adaptability and autonomous operations are essential. Ultimately, this research aims to enhance the efficiency and capabilities of robotic systems in dynamic and unpredictable settings. © 2025 IEEE.

关键词： Robotic arms

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Mechatronic Design of an Omnidirectional Octorotor UAV 10

Mechatronic Design of an Omnidirectional Octorotor UAV

引用

10th International Conference on Automation, robotics, and Applications, ICARA 2024

作者： Hamandi, Mahmoud Ali, Abdullah Mohamed Evangeliou, Nikolaos Chaikalis, Dimitris Tzes, Anthony Kyriakopoulos, Konstantinos Khorrami, Farshad Center for Artificial Intelligence and Robotics Abu Dhabi129188 United Arab Emirates Electrical Engineering NYUAD Abu Dhabi129188 United Arab Emirates New York University Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350394245

The design of an omnidirectional octorotor (OmniOcta) is the scope of this article. The pose of the UAV's thrusters is computed via an optimization algorithm that guarantees the omnidirectionality of the platform. The thrusters' positions are restricted in a semispherical volume with different polar coordinates while their pose is determined in order to avoid the interaction between their propellers' air-funnels. The octorotor is manufactured using 3D printed parts and its employed electronics were selected so as to have the smallest weight. The number of thrusters was then selected as the minimal number that could lift the platform's weight in all rotation directions with the chosen electronic and aerodynamic components. Simulations were conducted to show the capacity of the UAV to lift its weight, while experiments were conducted to identify the allocation matrix of the constructed UAV. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection 21

Visual Tracking Nonlinear Model Predictive Control Method fo...

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21st International Conference on Advanced robotics, ICAR 2023

作者： Amer, Abdelhakim Mehndiratta, Mohit Le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus C8000 Denmark Gim Robotics Espoo Finland Automatic Control Group Paderborn University Department of Electrical Engineering and Information Technology Paderborn Germany

ISBN: (纸本)9798350342291

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes11https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced. © 2023 IEEE.

关键词： Inspection

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Modeling and Parameter Identification for Human-robot Coupled Systems in Powered Lower Limb Prostheses. 5

Modeling and Parameter Identification for Human-robot Couple...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Huang, Yongshan Wang, Xin Liang, Dingkun Xiong, Jiaming Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada

ISBN: (纸本)9798350307320

Good trajectory tracking results are the basis for the implementation of other upper-level algorithms in powered lower limb prostheses, which rely on dynamics modeling and good parameter identification. However, powered lower limb prosthesis are human-robot coupled systems with human-inthe-loop, and it has been a challenge to model them as well as to perform parameter identification. This paper proposes a modeling method that can realize the decoupling of human-machine, and by constructing the linearized models of human-machine coupled system and prosthetic subsystem about the physical parameter sets respectively, and finally obtaining the relationship of their corresponding physical parameter sets, thus the method of indirectly identifying the parameters of human-machine coupled system through the prosthetic subsystem can be realized. The experimental results verify the feasibility and effectiveness of the method proposed in this paper. And the experimental results also shows that prosthetic controller should take advantage of dynamic of hip motion in order to make the wearer more comfortable and fluid when swinging the leg, and will also make the prosthesis more energy efficient. © 2023 IEEE.

关键词： Energy efficiency

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A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control 32

A Resilient Framework for 5G-Edge-Connected UAVs based on Sw...

引用

32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Damigos, Gerasimos Seisa, Achilleas Santi Satpute, Sumeet Gajanan Lindgren, Tore Nikolakopoulos, George Ericsson Research Luleå Sweden Luleå University of Technology Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350399714

In recent years, the need for resources for handling processes with high computational complexity for mobile robots is becoming increasingly urgent. More specifically, robots need to autonomously operate in a robust and continuous manner, while keeping high performance, a need that led to the utilization of edge computing to offload many computationally demanding and time-critical robotic procedures. However, safe mechanisms should be implemented to handle situations when it is not possible to use the offloaded procedures, such as if the communication is challenged or the edge cluster is not available. To this end, this article presents a switching strategy for safety, redundancy, and optimized behavior through an edge computing-based Model Predictive Controller (MPC) and a low level onboard-PID controller for edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based on communication Key Performance Indicators (KPIs) over 5G to decide whether the UAV should be controlled by the edge-based or have a safe fallback based on the onboard controller. © 2023 IEEE.

关键词： Three term control systems

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Integrated Direct/Indirect Adaptive Robust Control for electrical Driven Injection Machine Mold Closing with Accurate Parameter Estimations 16th

Integrated Direct/Indirect Adaptive Robust Control for Elec...

引用

16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhu, Shiqiang Meng, Qiwei Song, Wei Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Zhejiang Lab Hangzhou Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

In this paper, the control issue of the mold closing system of Injection machine is studied. In actual application, there are two major control problems needed to be addressed in controller design: to ensure the quality of products, the precision of mold needs to be guaranteed;the operation parameters of the machines are willing to be recorded for analysis, such as health monitoring and prognosis. However, the existing controllers usually rely on the servo motor, where the dynamics of the mold closing system is not taken into consideration, that may lead to degraded tracking performances. Moreover, the uncertain model parameters are usually ignored or simply treated as disturbances. Instead in this paper, the nonlinear dynamic model of mold closing system is first proposed including parameter uncertainties and disturbances for controller design. Subsequently, an integrated direct/indirect adaptive robust control law(DIARC) is proposed that not only achieves excellent tracking performance but also attenuates the parameter uncertainties with accurate parameter learning. Comparative experiments are carried out to verify our proposed approach and the results demonstrate the effectiveness of the integrated direct/indirect adaptive robust control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robust control

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices 48

Benchmarking Convolutional Neural Network Inference on Low-P...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ferraz, Oscar Araujo, Helder Silva, Vitor Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Institute of Systems and Robotics Portugal

ISBN: (纸本)9781728163277

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s. © 2023 IEEE.

关键词： Artificial Intelligence Benchmarking Edge devices GPUs Inference Engine IoT Low-power

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An 8-bit Quantized Globalization Model for Cat Skin Disease Detection Using Convolutional Neural Networks and TensorFlow Lite

An 8-bit Quantized Globalization Model for Cat Skin Disease ...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Trinanda Putra, Karisma Zidni, Hasan Turrizka, Rifdha Chu, Hsueh-Ting Vu, Dinh-Trung Prayitno Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Bantul Indonesia Institut Teknologi Bandung Department of Electrical Engineering Bandung Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia Asia University Department of Computer Science and Information Engineering Taichung Taiwan Semarang State Polytechnic Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350379839

Skin diseases are health issues that can be experienced by pets and potentially be transmitted from wild animals. However, current prediction systems predominantly rely on complex calculations that necessitate high computational power, making them challenging to deploy on mobile applications. Therefore, this study aims to optimize a skin disease detection system based on convolutional neural networks (CNN) using datasets from both wild and domestic cats. The quantized globalization model is a modified ResNet50 converted with TensorFlow Lite. This 8-bit quantization is necessary to develop a lightweight deep learning model that could operate on low-computational power units, such as smartphone or 8-bit edge computing devices. This study utilized a dataset comprising 343 images, categorized into six classes: five classes of diseases (abscesses, mange, ringworm, skin cancer, dermatitis) and one class for healthy skin. Next, the dataset was augmented to increase data variation and quantity. Subsequently, the dataset was trained on the ResNet-50 model and then converted to TensorFlow Lite with 8-bit integer data type. Finally, the quantized globalization model was validated using a separate 20% of the dataset. The validation results yielded an accuracy score of 0.930, a precision of 0.908, a recall of 0.902, and an F1-score of 0.903. Additionally, there was a significant 74.7% reduction in model size compared to the non-quantized version, demonstrating the efficiency and effectiveness of the quantization process in maintaining performance while substantially decreasing the computational resource requirements. © 2024 IEEE.

关键词： Convolutional neural networks

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UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects

UniSaT: Unified-Objective Belief Model and Planner to Search...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Nguyen, Hoa Van School of Engineering Federal University of Minas Gerais MG Belo Horizonte Brazil Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Curtin University BentleyWA6102 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unifiedobjective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Object tracking

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