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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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BiRP: Learning Robot Generalized Bimanual Coordination Using Relative Parameterization Method on Human Demonstration

BiRP: Learning Robot Generalized Bimanual Coordination Using...

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IEEE Conference on Decision and Control

作者： Junjia Liu Hengyi Sim Chenzui Li Kay Chen Tan Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

Human bimanual manipulation can perform more complex tasks than a simple combination of two single arms, which is credited to the spatio-temporal coordination between the arms. However, the description of bimanual coordination is still an open topic in robotics. This makes it difficult to give an explainable coordination paradigm, let alone applied to robotics. In this work, we divide the main bimanual tasks in human daily activities into two types: leader-follower and synergistic coordination. Then we propose a relative parameterization method to learn these types of coordination from human demonstration. It represents coordination as Gaussian mixture models from bimanual demonstration to describe the change in the importance of coordination throughout the motions by probability. The learned coordinated representation can be generalized to new task parameters while ensuring spatio-temporal coordination. We demonstrate the method using synthetic motions and human demonstration data and deploy it to a humanoid robot to perform a generalized bimanual coordination motion. We believe that this easy-to-use bimanual learning from demonstration (LfD) method has the potential to be used as a data augmentation plugin for robot large manipulation model training. The corresponding codes are open-sourced in https://***/Skylark0924/Rofunc.

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An Innovative Approach for Natural Gas Liquefaction Using a Mixed-Multi-Component-Refrigerant 8

An Innovative Approach for Natural Gas Liquefaction Using a ...

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8th International Conference on Applications and Design in Mechanical engineering, ICADME 2023

作者： Ismail, N.H. Omar, M.N. Ishak, M.S.A. Yahaya, N.Z. Khalid, K.A.A. Ab Halim, M.F.M. Department of Automotive Institut Kemahiran MARA Beseri Kaki Bukit Perlis02400 Malaysia Perlis Arau02600 Malaysia Research Center for Applied Electromagnetics Institute of Integrated Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Centre for Robotics and Industrial Automation Fakulti Kejuruteraan Elektrik Universiti Teknikal Malaysia Melaka Melaka Malaysia

The natural gas liquefaction is the most expensive and energy-intensive phase in the natural gas-to-liquefied natural gas-to-natural gas chain. In addition, this region has the biggest development potential. As a result, numerous LNG production methods have been developed and are deployed at export facilities around the world. The goal of this study is to describe and assess an innovative approach for mixed-refrigerant (MR) LNG method. The authors have dubbed this technique the MR-X approach. The MR-X process was developed based on the globally proven liquefaction technology C3MR and its large-scale successor AP-XTM (which offers many benefits and flexibility), but with a novel precooling phase construct. In pre-cooling and liquefaction phases, the refrigerant is a combination of methane, nitrogen, propane, ethane, butane, and isobutane. The paper investigates the creation of the MR-X technology, as well as its modelling, energy, and exergy investigations. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Liquefied natural gas

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Solar-Powered Smart Wheelchair with Voice Controller for Physically Challenged People

Solar-Powered Smart Wheelchair with Voice Controller for Phy...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha L. Sreevidya R. Deepalakshmi K Premalatha Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

It is challenging for those with strong mental faculties but physical disabilities to get around in a conventional hand-powered wheelchair. This project will enable any existing wheelchair to have an inexpensive assembly that enables a smart motion automation system that can be controlled by speech recognition on a smartphone. Continuous operation is guaranteed by the dual-charging system, which also offers longer mobility without the need for frequent recharging and independence from external power outlets. This enables users to adjust to varying conditions by depending on solar power during the day and switching to battery charging when solar energy is scarce. The main concept is a smartphone with built-in Bluetooth connectivity and the Android operating *** the distribution of the application we have developed, the goal of our project may be extended to other mobile devices that run Android software. The second main component of our system design is the microprocessor, which drives the wheelchair's dc motor in different directions. The wheelchair may now travel in a variety of directions thanks to this. The suggested model operates autonomously and on its own.

关键词： automation Gears Navigation Wheelchairs Operating systems Wheels Solar energy DC motors Mobile handsets Telecommunication computing

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Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements

Aggregating Single-Wheeled Mobile Robots for Omnidirectional...

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

作者： Meng Wang Yao Su Hang Li Jiarui Li Jixiang Liang Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BI-GAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Robot Engineering School of Automation Science and Electrical Engineering Beihang University

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

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Nano porous Structure formation on Multicrystalline Silicon Surface by using Chemical Etching method.

Nano porous Structure formation on Multicrystalline Silicon ...

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Devices for Integrated Circuit (DevIC)

作者： Sangeeta Barua Sikha Bandyopadhyay Sayan Chatterjee Robotics and Automation Engineering Department The Neotia University Kolkata India Department of Physics Durgapur Women’s College Durgapur India Department of E.T.C.E Jadavpur University Kolkata India

Present article exhibits a method, formation of Nano porous silicon by metal-assisted chemical etching (MAE). Nano porous structure is formed on the top of the multicrystalline Silicon by using HF and H 2 O 2 etching solution with Nickel salt at 60 0 C temperature for the reaction time of 60 minutes. Metal (Ni) nanoparticles deposited on the silicon from the solution. Metal salt act as a catalyst here for silicon etching whereas HF and H 2 O 2 contribute as oxidizing species. Reflectance measurement obtained from nano porous silicon provides degree of reflection of$\sim$5% to 8% over a spectra range of (220 to 1050 nm). Metals used in MAE process generally have positive standard electrode potentials (EO) (e.g. for Ag, EO =+0.8V) but Ni has negative standard electrode potential (EO = -0.25V).

关键词： Reflectivity Temperature measurement Electrodes Photovoltaic cells Hafnium Nickel Etching

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Simulation Study of Control Techniques Used for 9- Phase Voltage Source Inverter

Simulation Study of Control Techniques Used for 9- Phase Vol...

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2021 International Conference on System, Computation, automation and Networking, ICSCAN 2021

作者： Renuka Devi, G. Karthik, C. Malaiyappan, Prakash Avanthikha, A. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Puducherry India Jyothi Engineering College Department of Mechatronics Kerala India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India SSN College of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665439862

This paper presents the control techniques used for 9-Phase Voltage Source Inverter. For high power application the 9-phase inverter fed 9-phase drives dominantly used. In the proposed work a comprehensive relationship between the 9-phase SPWM, 9th harmonic injection technique and 9-phase modified SVPWM technique are presented. The simulation study of the 9-phase voltage source inverter is investigated with the above said switching methods with varying modulation indices. The MATLAB /Simulink results are addressed for varying modulation indices and analysis the results in terms of output phase voltage and total harmonic distortion. From the results find the optimum method is appropriate for 9-phase VSI. © 2021 IEEE.

关键词： Electric inverters

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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Effects of Knee Joint Misalignments on Human-Exoskeleton Interaction Dynamics

Effects of Knee Joint Misalignments on Human-Exoskeleton Int...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Stefano Massardi David Rodriguez-Cianca Juan C. Moreno Matteo Lancini Diego Torricelli BioRobotics group Centre for Automation and Robotics (CAR) Madrid Spain Department of Industrial and Mechanical Engineering (DIMI) University of Brescia Brescia Italy Department of Medical and Surgical Specialties Radiological Sciences and Public Health (DSMC) University of Brescia Brescia Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

One aspect that characterizes exoskeletons is their close physical contact with the user. Physical human-exoskeleton interaction (pHEI) is strongly affected by kinematic incompatibilities generated in the human-exoskeleton kinematic chain, often resulting in undesired joint misalignments and interaction forces. However, a systematic analysis to highlight this relation is difficult to execute. In this work we use a methodology for the study of pHEI based on an active dummy leg named Leg Replica. Thanks to this system, our aim is to highlight the relations between key pHEI metrics such as joint misalignment, interaction forces and relative displacements, underlining the role and effects of joint misalignment in controlled conditions. We first validated a kinematic model available in the literature relating human-exoskeleton knee misalignment with the resulting device's motion relative to the limb. The model was further used to investigate which is the relation between the kinematic of the leg-device system and the forces produced between the two bodies. We discovered that the model predictions were consistent with the experimental kinematic observations and strongly related with the peaks reached by shear forces during the motion. Shear forces were found to play a key role in the overall interaction and related with the lost of energy during the motion. These results underscore the strong influence that misalignments can exert on the generation of force and motion, even under simplified and controlled conditions.

关键词： Legged locomotion Knee Systematics Force Exoskeletons Dynamics Kinematics Predictive models Motion measurement Robots

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SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autono...

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International Conference on Computer Vision (ICCV)

作者： Yi Wei Linqing Zhao Wenzhao Zheng Zheng Zhu Jie Zhou Jiwen Lu Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

3D scene understanding plays a vital role in vision-based autonomous driving. While most existing methods focus on 3D object detection, they have difficulty describing real-world objects of arbitrary shapes and infinite classes. Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images. We first extract multi-scale features for each image and adopt spatial 2D-3D attention to lift them to the 3D volume space. Then we apply 3D convolutions to progressively upsample the volume features and impose supervision on multiple levels. To obtain dense occupancy prediction, we design a pipeline to generate dense occupancy ground truth without expansive occupancy annotations. Specifically, we fuse multi-frame LiDAR scans of dynamic objects and static scenes separately. Then we adopt Poisson Reconstruction to fill the holes and voxelize the mesh to get dense occupancy labels. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our method. Code and dataset are available at https://***/weiyithu/SurroundOcc.

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