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arXiv 2024年

作者： Zhang, Zhen Chu, Xiangyu Tang, Yunxi Au, K.W. Samuel Department of Mechanical and Automation Engineering The Chinese University of Hong Kong HKSAR Multi-scale Medical Robotics Center Hong Kong

This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics, using a pinching strategy with a two-parallel-finger gripper. In addition, we trained a neural network with the down-sampled 3D occupancy and action as input to model the dynamics of an elasto-plastic object. Our dataset and all CADs of the data collection system will be released soon on our website. Copyright © 2024, The Authors. All rights reserved.

关键词： Grippers

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Odometry and Inertial Sensor-based Localization of a Snake Robot

Odometry and Inertial Sensor-based Localization of a Snake R...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Ramsha Shahid Anam Baloch Humayun Tahir Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan Department of Electronic Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Terrain adaptability gives snake robots an edge over wheeled mobile robots. Snake robot's applications expansion to disaster management is due to its limbless and modular design. Replicating the navigation and locomotion of a biological snake have been in the spotlight for many researchers. In this study, a robust localization algorithm of a snake robot is developed. A sensor fusion-based algorithm using probabilistic approaches: UKF (Unscented Kalman Filter) and EKF (Extended Kalman Filter) has been proposed. Odometry is fused with one and two IMUs (Inertial Measurement Units) with both probabilistic approaches. Evaluation of results in a simulation environment showed that the fusion of two IMUs and odometry using EKF outer performs in terms of accuracy when odometry is fused with one IMU using EKF. Furthermore, the fusion of two IMUs and odometry with UKF is computationally expensive resulting in a large convergence time which is not the best suitable approach that can be utilized for the snake robot.

关键词： Location awareness Industries Measurement units Snake robots Inertial navigation Disaster management Robot sensing systems

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Design and Analysis of a Robot for Automated Intravitreal Injection

Design and Analysis of a Robot for Automated Intravitreal In...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Meng, Yuming Wu, Jiahao Liu, Lin Brelen, Marten Erik Liu, Yunhui Harbin Institute of Technology Department of Engineering and Automation Shenzhen China The Chinese University of Hong Kong T Stone Robotics Institute the Department of Mechanical and Automation Engineering Hong Kong Hong Kong The Chinese University of Hong Kong Department of Ophthalmology and Visual Sciences Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Intravitreal injection is an essential operation of treating ophthalmic diseases, in which an injection needle pierces the sclera and then injects therapeutic agent into the vitreous cavity. The traditional injection method is strict in the surgeon's skills and limited efficiency. This paper presents a four-degree-of-freedom (DOF) automated intravitreal injection robot to replace the manual operation. The robot enables active needle manipulation, press and drug injection. For safer operation, a double-linearly-actuated mechanism is used to implement a remote center of motion (RCM) tilt joint that allows the injection needle to rotate around the injection port. And also an easily replaceable fixture is introduced to restrain the unconscious movement of the eyeball. All drives of the injection robot are placed at the rear end, enabling the robot to perform automatic injection actions flexibly. A prototype is built to validate the feasibility of the mechanism and the preliminary tests are conducted. © 2022 IEEE.

关键词： Needles

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Numerical Simulations of Coronary Artery Diseases Using a Differential Hybrid Petri Net Model of the Cardiovascular System 4

Numerical Simulations of Coronary Artery Diseases Using a Di...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Jorge, Andre A. Junqueira, Fabricio Riascos, Luis A. M. Filho, Diolino J. Santos Miyagi, Paulo E. Escola Politecnica University of Sao Paulo SP Sao Paulo Brazil Federal University of Abc Department of Automation and Robotics Engineering SP Sao Paulo Brazil

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

World statistics data confirm that cardiovascular diseases (CVDs) are the leading cause of mortality globally. CVDs include several cardiovascular system dysfunctions that affect heart functionality, such as heart failure and cardiac arrhythmias caused by coronary artery disease (CAD). The early diagnosis and prognosis of CVDs could decrease its high mortality rate. Therefore, several cardiovascular clinical assessment tools have been proposed to understand cardiovascular hemodynamics in normal and abnormal conditions. In this work, from the cardiovascular system model, which is based on the Differential Hybrid Petri Net formalism, numerical simulations for cases of healthy behavior and with CADs are performed. The Differential Hybrid Petri Net is introduced due its intrinsic feature to describe and manipulate continuous variables and discrete events that occur in the cardiovascular system. Then, the results are analyzed based on hemodynamic parameters and on the electrocardiogram for the diagnosis of CADs, in addition to comparing them with related works and clinical data, which validate the results of this study. © 2022 IEEE.

关键词： Electrocardiograms

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Crop Detection and Weed Removal Agriculture Robot

Crop Detection and Weed Removal Agriculture Robot

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Patil, Digambar P. Kanase, Digvijay B. Patil, Seema S. Patil, Deepak B. Purohit, Himmat Singh Mhatre, Nupoor Dr. D. Y. Patil Institute of Technology Department of Automation and Robotics Maharashtra Pune411018 India Dr. D. Y. Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune411018 India Ashokrao Mane Group of Institutions Department of EandTc Engineering Maharashtra Kolhapur416112 India NMIET Talegaon Dabhade Electronics and Telecommunication Engineering Department Maharashtra Pune410507 India

ISBN: (纸本)9798331539948

Weed management is important thing of modern agriculture, as weeds can significantly impact crop yields, increase production costs, and pose environmental challenges. Weeds are unwanted plants that make race with desirable crops for resources such as nutrients, sunlight and water. It can cause significant damage to crops and other vegetation if left uncontrolled. The use of herbicides to control weeds can result in environmental pollution if not applied properly or if excessive amounts are used. Continuous use of herbicides without proper rotation or alternative weed management strategies can lead to the development of herbicide-resistant weed populations. Weed presence in harvested crops can reduce their quality, making them less marketable. The weed removal robot will be equipped with various sensors to scan the field and detect the presence of weeds. Advanced computer vision algorithms and machine learning techniques are often employed to differentiate between crops and weeds based on color, shape, or other characteristics. Sensors scan the field and capture images or data that are then processed by the processor. The single board computer can be programmed to recognize the visual differences between crops and weeds based on color, shape, or other characteristics. This robot will be equipped with precise actuators, which can include mechanical arms or electric tools. These actuators are used to perform the actual weed removal tasks. The processor controls the weed removal mechanism, which could be a mechanical arm with tools like rotating brushes or blades for physical weed removal. © 2024 IEEE.

关键词： Digital storage

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Optimizing Mobile Charger Scheduling in Wireless Rechargeable Sensor Networks Using Min-Heap Algorithm

Optimizing Mobile Charger Scheduling in Wireless Rechargeabl...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Md. Kamaruzzaman Abhijit Chandra Department of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

In Wireless Rechargeable Sensor Networks (WRSNs), efficient energy management is crucial for prolonging network lifetime. Mobile Charging Vehicles (MCVs) play a vital role in replenishing the energy of sensor nodes (SNs); but optimizing their scheduling to minimize energy depletion and maximize network coverage remains a significant challenge till date. This paper proposes a novel scheduling approach based on the Min Heap algorithm to optimize the deployment of MCVs in WRSNs. By prioritizing SNs with the lowest remaining energy, the Min-heap algorithm dynamically adjusts the charging sequence, reducing the risk of node failures and improving overall network performance. To show the effectiveness of the proposed scheme, extensive simulations have been done by using MATLAB. Simulation results demonstrate that the proposed approach significantly improves the performance in terms of working time, total energy received, ratio of successful energy replacement and moving distance as compared to traditional scheduling approaches such as EDF and ETLBO.

关键词： Wireless communication Wireless sensor networks Processor scheduling Heuristic algorithms Simulation Scheduling Communication networks MATLAB Energy management Computational intelligence

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Hand Gesture Classification using sEMG Signals and Ensemble Learning

Hand Gesture Classification using sEMG Signals and Ensemble ...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Ranjeesh R Chandran D. Devaraj Saurav Ashok Donfred Shaji Sam K Saju Department of Robotics and Automation Adishankara Institute of Engineering and Technology Kalady Kerala India Department of Electronics and Electrical Technology Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Department of Robotics and Automation Adishankara Inst. of Eng and Tech. Kalady Kerala India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Electromyographic signals offer details on a muscle activity of a person. In the case of hand movements various combinations of forearm muscles must be activated in order to carry out each gesture, which results in unique electrical patterns. On the other hand, identifying the motion being executed is made possible by the examination of these patterns of muscle activation, which are captured by EMG signals. This article presents a novel implementation on hand gesture classification utilizing Electromyography (EMG) signals recorded through a MYO Thalmic bracelet. The dataset consists of raw data collected from 36 subjects, each executing a series of static hand gestures. The classification model employed for this study is an ensemble of decision trees, achieving an impressive validation accuracy of 98.0% and a test accuracy of 98.1 %. The article outlines the dataset details, recording methodology, and provides an in-depth analysis of the model architecture, hyperparameters, and training results.

关键词： Training Wrist Muscles Sensor phenomena and characterization Predictive models Electromyography Robustness

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Formation Control and Sub-Swarm Generation of Multirotor UAVs

Formation Control and Sub-Swarm Generation of Multirotor UAV...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muhammad Muzamal Shahzad Muhammad Haroon Asad Muhammad Haris Hammad Munawar Muhammad Haroon Yousaf Swarm Robotics Lab National Center of Robotics and Automation University of Engineering and Technology Taxila Taxila Pakistan Computer Engineering Department University of Engineering and Technology Taxila Taxila Pakistan

The research field of collective behaviors takes inspiration from natural self-organizing systems like honeybees, fish schools, social insects, bird flocks, and other social animals. These behaviors can be transformed into robots by replicating the same set of rules as found in the natural swarms. The deployment of aerial swarm robotics has become significant due to their multiple applications in surveillance, agriculture, and military. These applications lead to the development of aerial swarm robotic platforms to solve real-world problems. For example, a swarm of low-tech, cost-effective small UAVs can engage a high-tech target effectively. Similarly, a swarm of UAVs can perform surveillance and rescue missions in disastrous areas and build an emergency communication network through multiple UAV swarms. The critical part of swarm technology is multiple swarm intelligence algorithms that have been proposed and tested by various simulation platforms such as V-rep, Gazebo and MATLAB. Very few of them have been deployed on hardware to solve real scenarios that include formation control, navigation, pattern formation, and a few sub-swarm generation algorithms. Team connectivity is one of the essential aspects of a swarm network that emerges the collective intelligence of the group and ensures the readiness of the algorithm to deploy and solve real-world problems. Many researchers proposed the team connectivity algorithms in a single swarm network but still left the connectivity gap between the sub-swarms during a mission. This research work contributes toward designing a swarm intelligence algorithm to improve the connectivity techniques and team connectivity between the swarm agents and the sub-swarm groups.

关键词： Pattern formation Service robots Navigation Surveillance Insects Swarm robotics Formation control

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Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 375-386页

作者： Nimma, Divya Dhanke, Jyoti A. Murthy, G.S.N. Khandekar, Sachin Dadu Hymavathi, J. Jangir, Pradeep Singh, Mahesh Department of Computational Science University of Southern Mississippi Hattiesburg 39406 MS United States Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Maharashtra Pune India Department of CSE Aditya University Surampalem India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Robotics and Automation Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

Making informed economic decisions based on agricultural data is challenging without proper crop management. Data has become the single most important part of modern farming, and its rapid evolution is a major contributor to the rise of “smart farming.” Using sensors to gather objective data with the end aim of increasing productivity and sustainability has several advantages. Information that might boost productivity while decreasing resource waste and pollution levels is essential for managed farms. The cornerstone of future sustainable farming will be data-driven agriculture using robotic solutions based on AI-based techniques. This paper examines the current state of advanced farm management systems, discussing topics like data acquisition in crop fields and variable rate applications, to assist farmers in saving money, protecting the environment, and transforming food production so that it can sustainably keep up with the expected increase in population. The advent of machine learning, big data technologies, and high-performance computers has opened up new avenues for data-intensive research in the interdisciplinary subject of agro technology. These avenues of exploration have been aided by the development of more capable computers. A comprehensive literature review on the issue of the applications of machine learning in agricultural manufacturing is provided in this research. Machine learning has shown its utility in agriculture via two applications: filtering and categorizing products. The use of machine learning on sensor data is transforming conventional farm management systems into AI-enabled programs. These services provide farmers instantaneous, complete access to ideas and insights that may guide their decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Big data analytics Optimize crop yields Sustainable agriculture

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An Advanced System for Automatically Segregating Bolts using Thread Detection and Dot Matrix Marking.

An Advanced System for Automatically Segregating Bolts using...

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Global Conference for Advancement in Technology (GCAT)

作者： S Ramkumar M Sridharan G Nareshbabu R Giri Kanagaraj Venusamy R Priyadharshini Department of Robotics and Automation Rajalakshmi Engineering College Thandalam Chennai India Department of Mechatronics Engineering Rajalakshmi Engineering College Thandalam Chennai India

Packaging is a crucial stage in the mass production of components, especially when it comes to threaded components fabrication. Currently, the process of segregating bolts based on their length and thread presence is done manually, leading to numerous errors during packaging. To address this issue and achieve greater accuracy and perfection, we propose a digital segregation method using an electronic control module. By placing this module along the assembly line, it can automatically divide bolts according to their lengths and the presence of threads, eliminating the need for manual intervention and reducing human errors. Implementing this Mechatronics system allows for more precise and accurate punching of bolts based on their size, resulting in high-quality packaging that takes into account various segregation factors.

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