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Kinematics of Doosan Cobot-A0509 3

Kinematics of Doosan Cobot-A0509

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Dash, Anjan Kumar Arunkumar, V. Sastra Deemed University School of Mechanical Engineering Tamil Nadu Thanjavur India SASTRA-MHI SASTRA-MHI Training Center Thanjavur India

ISBN: (纸本)9798331515911

Cobots are becoming more popular in the industries, especially for machine tending, assembly, inspection and other operations alongside a human operator. The Cobot in discussion here is Doosan A0509. When one purchases such Cobots, although he gets all the necessary tools for its operation, still one has to know how the information on the teach pendant is according to the kinematics that we read from text books. The Doosan family of Cobots use ZYZ Euler angle notation for representing the orientation of the end-effecter. How to interpret it from the information given in the teach pendant? More than that, during task space operations, the desired rotations of the end-effecter and the orientation of the end-effecter are both shown by Rx, Ry and Rz on the Cobot teach pendant. In that case does it represent the amount of rotation the user wants or the orientation of the end-effecter? Similarly, for the A0509 Cobot, one may fix frames and determine DH parameters and the forward kinematics model. But the forward kinematics results that he gets by coding may not be the same as shown in the teach pendant. Now how to get the same forward kinematics model as implemented in the Cobot software given to the user? In this article, all the above-mentioned issues are addressed with elaborate testing on the Cobot. This bridges the gap between what we learn about robot kinematics and the actual Cobot. © 2025 IEEE.

关键词： End effectors

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Plundervolt Attack: Simulation-Based Voltage Glitching Attack Against the robotics Industry 3rd

Plundervolt Attack: Simulation-Based Voltage Glitching Attac...

引用

3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Kaur, Shaminder Birla, Shilpi Singh, Neha Sandhya Institute of Engineering and Technology Chitkara University Chitkara University Punjab India Manipal University Jaipur Jaipur India School of Engineering and Technology Chitkara University Chitkara University Himachal Pradesh India

ISBN: (纸本)9789819784288

The robotics industry is growing rapidly along with an increase in pace of fault injection attacks against them. Fault injection attacks on robotics systems aim in altering the behavior of the system to produce erroneous output thereby compromising its security and trust. It becomes a precondition of the industry to critically analyze robotic devices against fault injection attacks. Plundervolt is a new type of fault injection attack which breaks the integrity and security of embedded devices. We proposed the simulation-based method for generating plundervolt attack so that industry experts can perform dependability analysis of fault injection attacks at the hardware level. Plundervolt attack on a 8051 microcontroller successfully skips important instructions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robots

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A Deep Learning Approach to Stock Trend Forecasting using CNNs with Technical Indicators 3

A Deep Learning Approach to Stock Trend Forecasting using CN...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Bharathiraja, M. Krishna Kumar, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University TamilNadu Chennai India Department of Mechatronics Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

The approach encompassed under this model introduces stock trend forecasting using a new approach that involves Convolutional Neural Networks (CNNs) supplemented with technical indicators like the Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI). This study reacts to the limitations of traditional methods like ARIMA and GARCH due to their shortcomings with non-linear dependence on stock data. The robust solution for trend prediction closely follows the hierarchical learning of CNN. This model attains a remarkable accuracy of 99% in stock trend prediction using Convolutional Neural Networks, fine-tuning crucial but elaborate patterns for stock price movements. The forecast results categorize stock trends as Neutral, Bullish, and Bearish, with validation shown from high precision, recall, and F1-scores computed from complete classification reports and confusion matrices. The capability of forecasting provides tremendous assistance in making informed investment decisions. These results prove that CNNs more efficiently predict stocks if combined with technical analysis and give portions of insight to traders. This study shows the capacity to apply various deep learning techniques in finance to enhance stock trend predictions and shape on-the-ground investment strategies. The study fills an existing gap in research with the introduction of combining CNN with technical indicators in the prediction models for improvement with predictive power and accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Advanced Multimodal Emotion Recognition using Integrative Analysis of Multilingual Text, EEG, Voice Signals, and Facial Analysis 3

Advanced Multimodal Emotion Recognition using Integrative An...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Reddy, Jambula Snehith Surat, Dev Aakarsh Shyamala, Pachila Syama, S. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Emotion recognition with the help of AI is critically important for improving the processes of human computer interaction, and can be effectively implemented in healthcare, robotics, and mental health analysis. Systems that adopt inputs in a single mode only, be it text, voice, or facial expressions all have their issues such as low accuracy, easiness to fake and the inability to comprehend the context. These are overcome by a multimodal method that uses textual, voice, facial and EEG data to be more reliable or accurate Multilingual text preprocessing hence translates the inputs from other regions to English for the ML/DL algorithms to classify it as either having positive, neutral or negative feelings. Voice converter translates into feelings with pitch and rhythm with the help of DL models and face using DeepFace even in brightness variations. The Electroencephalogram or EEG signals are obtained at different times and those with higher layers of neural works are analyzed in real time. Fusion strategies also help in resolving compatibility issues and conflict between the modalities and increases the security against any attempt of adulteration. Through this approach, it is possible to detect accurate real-time emotion with aims of screening of mental health, enhance HCI and designs of empathetic robots. Future work therefore seeks to enhance integration methods, extend its use and investigate other approaches for a higher level of enhancement. © 2025 IEEE.

关键词： Emotion Recognition

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Cloud-Based AI robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Agricultural Automation Fruit Trees Real-Time Data Processing Sensor Integration Sustainable Farming

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Stabilization and Performance Analysis of Double Link-Rotary Inverted Pendulum Using LQR-I Controller 3

Stabilization and Performance Analysis of Double Link-Rotary...

引用

3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Rai, Anjali Bhushan, Bharat Jaint, Bhavnesh Delhi Technological University Electrical Engineering Department New Delhi India

ISBN: (纸本)9798331533205

This paper presents an optimal LQR-Integral based controller (LQR-I) for balancing control of the Double Link Rotary Inverted Pendulum (DL-RIP) system. It is one of the challenging topics in control and robotics due to its nonlinearity and instability, also complex system due to 3 degree of freedom rotary system. This system uses a motor to control and balance two rigid links serially connected pendulums in a vertically upward direction. Pole-zero mapping is used to determine the stability of the system. In this paper, a performance analysis of the system is done with an LQR-I controller using three reference paths (Square, Sawtooth, and Sinusoidal wave), two of which are discontinuous and one continuous path. The system shows less overshoot and deflection to stabilize the pendulum vertically upwardly with a continuous sinusoidal reference path compared to a square and sawtooth wave. © 2025 IEEE.

关键词： Proportional control systems

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Development and Implementation of a Wisdom Course Utilizing the "teaching-Innovation-Competition" Model within the Virtual Teaching and Research Section of Mechatronic engineering Major in China 3

Development and Implementation of a Wisdom Course Utilizing ...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Wei Ma, Wenpeng Hu, Wenhua Liu, Fulong Zhang, Xiaotao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400712692

A new teaching model of teaching-innovation-competition is presented and a wisdom course based on this model is developed for mechatronics engineering majors, mainly to deal with several existing problems such as limited experimentation and lack of innovative inspiration in Chinese universities. This study elaborates the course objectives and structure of curriculum system, including digitalized teaching content, project-driven teaching means, innovation platform, web-based resource support, team competition and diversified evaluation method. Through a teaching case study within mechatronic engineering major, the teaching mechanism and achievements are demonstrated. The study results indicate that the proposed teaching mode based on the virtual teaching and research section, significantly improves students' innovation ability and the teaching effects, compared to traditional teaching methods. This teaching mode can also be extended to other majors for reference. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Construction and Practice of Wisdom Course Mechatronic engineering Major Teaching-Innovation-Competition Mode Virtual Teaching and Research Section

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Generalized Wavefront Path Planning

Generalized Wavefront Path Planning

引用

mechatronics, Control and robotics (ICMCR), international conference on

作者： Wesley Chorney Computational Engineering Mississippi State University Mississippi United States

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

A generalization of the wavefront path planning algorithm with respect to the choice of grid and the cost function is presented. Furthermore, it is shown that this generalization limits to the typical wavefront path planning algorithm in the simplest case. This algorithm achieves a local optimum, and its advantages are demonstrated through simulation. In particular, the adaptive grid size allows for better navigation around corners, while avoiding excessive computations related to fine grid resolution in areas without obstacles. The expanded nature of the cost function allows for better specification of navigational constraints. In essence, it is shown that the proposed method can effectively handle scenarios that would be difficult for typical wavefront path planning.

关键词： mechatronics Navigation Computational modeling Cost function Path planning Computational efficiency Robots

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TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device 3

TinyML Speech Classification Embedded Ai Module for Hand Reh...

引用

3rd international conference on mechatronics, Control and robotics, ICMCR 2025

作者： Numsomran, Arkorn Numsomran, Arjin Chaoraingern, Jutarut Triam Udom Suksa Pattanakarn Suvarnabhumi School Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Robotics and AI Engineering Bangkok Thailand

ISBN: (纸本)9798331544546

Recent advancements in artificial intelligence and machine learning have significantly improved healthcare, especially in the development of assistive technologies for rehabilitation. This paper introduces a method for hand rehabilitation that utilizes the capabilities of tiny machine learning to enhance speech classification in a rehabilitation device. The proposed method employs a CNN model capable of classifying speech signals that are indicative of different hand movement patterns. Patients emit these speech signals during prescribed hand exercises, which are crucial for their rehabilitation process. The main focus of this study is on training and deploying a speech classification system that can work in the resource-limited environment of TinyML platforms. We detail the process of capturing speech data, preprocessing it, and extracting the most features relevant to different hand movements. Our results show that using TinyML to help with hand rehabilitation works. The method we came up with shows how TinyML could change the way rehabilitative devices are controlled, and it also shows us what personalized and easy-to-use rehabilitation tools might look like in the future. © 2025 IEEE.

关键词： CNN Embedded AI Hand Rehabilitation Device Speech Classification TinyML

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Power System Equipment Detection in Thermal Images by Deep Learning Approach 3

Power System Equipment Detection in Thermal Images by Deep L...

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3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Khandual, Biswojyoti Balabantaraya, Rupali Sahoo, Ashwin Kumar Sahoo, Prabodh Kumar Panda, Sibashis Parashar, Anushka C.V.Raman Global University Department of Electrical Engineering Bhubaneswar India Parul Institute of Technology Parul University Department of Mechatronics Gujarat India

ISBN: (纸本)9798331533205

The non-contact accuracy of infrared thermography has made it indispensable for electrical equipment inspection. In order to recognize electrical equipment in thermal pictures, this study presents a revolutionary neural network-based learning model. Based on the neural network algorithm, the proposed work suggests a real-time object detection model YOLO-V8, that can predict the coordinate points, orientation angle, and type of class of certain equipment. To improve accuracy, particularly for small items, the model incorporates an orientation consistency prior. In the proposed work, an extensive thermal imaging dataset of a distribution transformer;covered a range of settings and equipment kinds. According to experimental results, the suggested model achieves a mean average precision of 93.7%, demonstrating its robustness to noise and ability to function at 20 frames per second resulting in accurate detection of different segments of a distribution transformer in a thermal image. © 2025 IEEE.

关键词： Thermography (imaging)

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