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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

Assisted Physical Interaction: Autonomous Aerial Robots with...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andrea Berra Viswa Narayanan Sankaranarayanan Achilleas Santi Seisa Julien Mellet Udayanga G.W.K.N. Gamage Sumeet Gajanan Satpute Fabio Ruggiero Vincenzo Lippiello Silvia Tolu Matteo Fumagalli George Nikolakopoulos Miguel Ángel Trujillo Soto Guillermo Heredia CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Visualization Pose estimation Neural networks Lighting Object detection Autonomous aerial vehicles Aircraft navigation

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PROSPECT: Precision Robot Spectroscopy Exploration and Characterization Tool

PROSPECT: Precision Robot Spectroscopy Exploration and Chara...

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

作者： Nathaniel Hanson Gary Lvov Vedant Rautela Samuel Hibbard Ethan Holand Charles DiMarzio Taşkın Padır Institute for Experiential Robotics Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts USA Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Electrical and Computer Engineering Department Northeastern University Boston Massachusetts USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an R 3 coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naïve point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment.

关键词： Reflectivity Point cloud compression Geometry Spectroscopy Three-dimensional displays Robot kinematics Robot sensing systems End effectors Planning Surface treatment

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Analysis of Air-Gap Field Modulation in Asymmetric Stator Tooth Bilateral-Excitation Flux Modulation Machine

Analysis of Air-Gap Field Modulation in Asymmetric Stator To...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Fei Zhao Kuang Yang Ruixiang Liu Jincheng Yu S M Sajjad Hossain Rafin Qasim Ali School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University USA Department of Electrical Engineering Sukkur IBA University Pakistan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

In view of the complexity of the flux density harmonic components of bilateral-excitation flux modulation (BFM) machines, it is not easy to analyze the role of air-gap flux density harmonics on the electromagnetic torque of BFM machines. In this digest, Taking the novel asymmetric stator tooth (AST) BFM machine as the research object, the flux modulation phenomena of the PM magneto motive-force (MMF) and armature winding MMF are analyzed in detail. Meanwhile, based on the air-gap field modulation theory and Maxwell stress tensor equation, the contribution of air-gap flux density harmonics to the electromagnetic torque of the BFM machine is obtained.

关键词： Magnetic flux Torque Tensors Magnetomechanical effects Air gaps Windings Modulation

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An Improved YOLOv5 Detection Method for Strip Surface Defect 28

An Improved YOLOv5 Detection Method for Strip Surface Defect

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28th International Conference on automation and Computing, ICAC 2023

作者： Liu, Yefeng Yu, Longfei Zhang, Qichun School of Automation and Electrical Dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Shenyang Institute of Technology Dept. Fushun China Shenyang Ligong University Dept. School of Automation and Electrica Engineering China University of Bradford Bradford Dept. Department of Computer Science United Kingdom

ISBN: (纸本)9798350335859

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm. © 2023 IEEE.

关键词： Convolution

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Design of a Flexible Robot Arm for Safe Aerial Physical Interaction

Design of a Flexible Robot Arm for Safe Aerial Physical Inte...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Julien Mellet Andrea Berra Achilleas Santi Seisa Viswa Sankaranarayanan Udayanga G.W.K.N. Gamage Miguel Ángel Trujillo Soto Guillermo Heredia George Nikolakopoulos Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Robotics and AI Team Electrical and Space Engineering LuleåUIniversity of Technology Luleå Sweden Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction.

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Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM During COVID-19 Pandemic

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM...

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2023 International Conference on Power System Technology, PowerCon 2023

作者： Xu, Da Liu, Bowen Lam, Chi-Seng Huang, Zhangyou University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi Macao China Institute of Microelectronics University of Macau Macao China School of Automation China University of Geosciences Wuhan430074 China University of Macau Fst Department of Electrical and Computer Engineering Macao China

ISBN: (纸本)9798350300222

Due to the high restrictions of the global COVID-19 on society, economy and production, the electrical supply-demand balance is facing a great challenge. This paper proposes a short-term load forecasting (STLF) method based on multi-factor fusion during the COVID-19. After fully analyzing and screening the nonlinear coupling interactions among cross-domain weather and public health safety features, a convolutional bi-directional long short-term memory neural network (ConvBiLSTM) is developed based on the convolutional and bidirectional loop enhancements of long short-term memory (LSTM). To avoid early termination of training the global optimal solution, multi-objective grasshopper optimization algorithm (MOGOA) is adopted to select the inherent hyperparameters of the forecasting network. Attention mechanism is further introduced to enhance the learning ability of the key features in temporal data, improving the forecasting accuracy and robustness of ConvBiLSTM. Comparisons on cross-domain COVID-19 dataset over classical methods are performed to verify its effective and superior performances. © 2023 IEEE.

关键词： Coronavirus

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

arXiv

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arXiv 2024年

作者： Berra, Andrea Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Mellet, Julien Gamage, Udayanga G.W.K.N. Satpute, Sumeet Gajanan Ruggiero, Fabio Lippiello, Vincenzo Tolu, Silvia Fumagalli, Matteo Nikolakopoulos, George Soto, Miguel Ángel Trujillo Heredia, Guillermo CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

关键词： Unmanned aerial vehicles (UAV)

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Optimized Admittance Control for Manipulators Interacting with Unknown Environment

Optimized Admittance Control for Manipulators Interacting wi...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Haiyi Kong Guangzhu Peng Guang Li Chenguang Yang Department of Electrical and Electronic Engineering University of Manchester Oxford Rd Manchester UK Bristol Robotics Laboratory University of the West of England Bristol UK School of Automation Nanjing University of Information Science and Technology Nanjing China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.

关键词： Adaptation models Control design Heuristic algorithms End effectors Dynamic programming Trajectory Iterative methods

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Efficient Arm Motion Planning with Reduced Viapoints for Mars Rover Operations

Efficient Arm Motion Planning with Reduced Viapoints for Mar...

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Space robotics (iSpaRo), International Conference on

作者： Justin Huang Valen Yamamoto Ana Warner Jet Propulsion Laboratory California Institute of Technology Pasadena CA Department of Electrical Engineering and Computer Science UC Irvine Irvine CA Department of Robotics University of Michigan Ann Arbor MI

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

This paper describes additions and enhancements we made to the standard RRT-Connect motion planning algorithm to suit the unique needs of operating the robotic arm on the Perseverance Mars Rover. One constraint we have is to maintain a minimum distance between the terrain and any part of the arm hardware. Another goal specific to rover planning is to minimize the number of viapoints in our motion plans. These algorithms should also run efficiently so that rover planners can generate arm motion sequences in time for uplink windows. We applied the RRT-Connect algorithm to the rover’s robotic arm and tested its performance, and we found that it could be used successfully for rover planning with a few changes. First, we implemented the terrain clearance requirement using a software tool called Proximeter. Second, we simplify the paths computed by RRT-Connect to minimize the number of viapoints in a motion plan. Third, we leverage Proximeter to reduce the number of expensive terrain collision checks done by the algorithm, speeding up motion planning.

关键词： Space vehicles Mars Costs Software algorithms Manipulators Hardware Planning

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