IAES International Journal of Robotics and Automation (IJRA)
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IntelliDrive autonomous robot powered by large language model
Get PDFThe rapid advancements in artificial intelligence (AI) and robotics have paved the way for innovative autonomous systems capable of performing complex tasks. This project integrates robotics with Large Language Models (LLMs) to develop an intelligent, versatile and user-friendly robotic system. The robot is designed to interpret structured commands, make real-time decisions, and navigate autonomously in dynamic environments, addressing key challenges faced by traditional autonomous systems. Central to the system is a Raspberry Pi 4, which serves as the main processing unit, integrating components such as a webcam for visual data capture, an L298N motor driver for motor control, and a Bluetooth speaker for real-time feedback. The LLM API enables the robot to process natural language commands, providing context-aware task execution and adaptability to changing scenarios. Testing has demonstrated the system’s ability to perform autonomous navigation, detect obstacles, and execute tasks effectively. This research offers a foundation for various industries, including logistics, healthcare, education, and hazardous environment operations. By incorporating LLMs the robot overcomes limitations of traditional rule-based systems, enhancing dynamic decision-making and user interaction. With its modular design and scalability, it bridges the gap between human-like intelligence and mechanical precision, setting the stage for future advancements in AI-driven robotics
An Internet of Things based mobile-controlled robot with emergency parking system
Get PDFThis paper presents an Internet of Things (IoT) based mobile-controlled car with an emergency parking system that integrates advanced functionalities to enhance safety and user convenience, utilizing the ESP32 microcontroller as its core. The system allows users to control the car remotely via a mobile application, leveraging Wi-Fi connectivity for seamless communication. Key features include LED indicators for various operations such as reversing, left and right turns, and brake activation, ensuring clear signaling in real-time. The innovative emergency parking system detects obstacles or emergencies using sensors and halts the vehicle automatically, reducing the risk of accidents. The car's lightweight, energy-efficient design, combined with the versatility of the ESP32, ensures a responsive and reliable operation. Additionally, the system provides an intuitive user interface through the mobile app, enabling precise control and real-time feedback. The proposed system is faster in response compared to the existing systems. Moreover, the proposed system consumes less energy, and hence, it uses the battery more efficiently, extending the time of operation. Lower power consumption ensures longer operation time, reducing the need for frequent charging and making the system more practical. This paper demonstrates the integration of IoT and embedded systems to create a smart vehicle solution suitable for various applications, including robotics, automation, and personal transport. Its cost-effectiveness and scalability make it a viable choice for both hobbyists and developers
A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit
Get PDFConvolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made
Design and development of an Arduino-based oxygen saturation, heart rate variability, and blood glucose measurement device
Get PDFThis study presents the design and development of an Arduino-based device for measuring oxygen saturation, heart rate variability (HRV), and blood glucose levels. The primary goal is to create an affordable, portable, and accurate health monitoring solution suitable for home health care and remote medical services. The device integrates a pulse oximeter sensor to measure oxygen saturation and heart rate, an electrocardiogram (ECG) sensor for capturing HRV data, and a non-invasive sensor for regular blood glucose monitoring, all managed by an Arduino microcontroller. Data collected by the sensors is processed and displayed on a user-friendly interface, enabling real-time tracking of health metrics. The device's performance was rigorously tested and validated against standard medical equipment, demonstrating comparable accuracy in measuring the targeted health parameters. This innovative solution aims to enhance personal health monitoring, reduce the burden on healthcare systems, and promote early detection and better management of health conditions. The affordability and ease of use make this device accessible to a wider population, potentially improving health outcomes. Future developments will focus on refining sensor accuracy and expanding the device's capabilities to monitor additional health metrics
A fuzzy inference system for hand injury level classification using surface electromyography signals
Get PDFThe surface electromyography (SEMG) is extensively used in assessing injuries in the musculoskeletal parts of the body. Integrating intelligence in such applications impacted the development of intelligent medical devices. The conventional way of assessing hand injury level is manually and subjectively done by experts to identify the type of rehabilitation program recommended to the patient. This work uses SEMG data to classify hand injury levels through a fuzzy inference system (FIS). Three of the many features of the SEMG signal were selected based on its high distinction levels, namely, the root-mean-square, enhanced mean-absolute value, and the waveform length. Segmentation through a sliding window method is used for feature extraction. The FIS rules were designed based on the assessment guide of the experts. A Mamdani-type FIS classifier was used with membership functions which are a combination of trapezoidal and triangular types. A MATLAB Simulink model was also designed to test the FIS system. The setup effectively identified injury levels through tests with a healthy subject, wherein no muscle activation means an injury, while the full fist, as a full muscle activation or healthy. In between signal values vary with different injury levels. In the future, this setup will be tested on patients in a rehabilitation clinic for validation
Design and development of humanoid robotic arm
Get PDFThis paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm featuring a sophisticated 5-finger gripper. The five degrees of freedom include the base, shoulder, elbow, wrist, and gripper, all controlled by MG996R servo motors to enhance grasping, positioning, flexibility, and mobility. The arm is constructed from laser-cut aluminum sheets. It effectively picks and places objects such as bottles and bags. A high-speed portable computing system is used to control robotics hand operations. A webcam is used for object detection and to acquire information about the surroundings. The system uses a convolutional neural network-based MobileNet architecture for object detection. The robotic hand is used as an assistive aid for amputees. It mimics finger movements based on detected objects. The system achieved a precision of 0.97 for bags and 0.93 for bottles, with accuracies of 96.83% and 92.42%, respectively. The system employs advanced computer vision algorithms and real-time strategies, ensuring adaptability across various tasks. It integrates advanced visual systems and improved feedback to enhance user interaction and overall usability. It addresses trade-offs between detection precision and processing time
VotTomNet: Voting-based tomato disease diagnosis with transfer learning
Get PDFThe research presents an advanced automation system, termed VotTomNet, designed for diagnosing tomato leaf diseases using transfer learning, and soft and hard voting ensemble techniques. By leveraging six pre-trained deep learning convolutional neural networks—VGG16, InceptionNet, ResNet, MobileNet, EfficientNet, and DenseNet—the system achieved an impressive accuracy of 99.2%. These models were meticulously fine-tuned to diagnose multiple types of tomato diseases with heightened precision. The integration of a soft and hard voting mechanism further enhanced the overall diagnostic accuracy by combining the strengths of these diverse models into a powerful ensemble. The findings underscore the robustness, reliability, and effectiveness of this ensemble technique, marking a significant advancement in precision agriculture and crop health assessment. By outperforming traditional methods, this approach offers a more practical and efficient solution for large-scale agricultural applications, enabling comprehensive crop management and improved yield. In conclusion, this research lays a strong foundation for future innovations in automated plant disease diagnosis and agricultural technology. Its contributions have the potential to revolutionize disease management, reduce crop losses, and ultimately enhance food security on a global scale
Internet of Things-enabled smart robotic baggage monitoring and tracking system for enhanced traveler convenience and security
Get PDFBaggage travel is a significant issue, causing inconveniences and financial losses for travellers. The rise in efficient international and domestic travel has led to the need for live baggage tracking systems. Traditional methods, such as manual tracking and locks, are inefficient and counterproductive due to power limitations. IoT has revolutionized baggage management by providing real-time tracking feedback and enhancing security. IoT-enhanced smart luggage systems use biometric locks, GPS tracking, and smart locking mechanisms to prevent theft and unauthorized usage. Geofencing allows users to draw boundaries for luggage, and smart luggage systems can adapt to airport security requirements. Some smart suitcases also have self-following features, allowing travellers to have better control over their bags. IoT-enabled baggage solutions also improve airport and travel centre efficiency. RFID and barcode identification devices enable airline employees to quickly recognize, monitor, and manage luggage, reducing waiting times and loss risks. Cloud-based systems allow users to remember their luggage and receive travel suggestions based on predicted frequency of use. IoT-enabled baggage management systems have the potential to transform airport ecosystems into smarter ones through automated tracking with minimal human involvement and errors. AI and machine learning can also proactively address concerns and improve the overall customer journey
Multi-microcontroller system for Mecanum robots with gripper-shooter mechanisms
Get PDFThis study presents the design and implementation of a multi-microcontroller digital control system for a Mecanum-wheeled robot with gripper and shooter mechanisms, tailored for agricultural applications. The proposed system integrates an Espressif32 master controller with Arduino Nano slave microcontrollers, enabling precise control of robot movement and functional components. Wireless control is facilitated by a PlayStation 3 controller, while Mecanum wheels ensure omnidirectional mobility in dynamic environments. Experimental results indicate a 66.67% success rate in seedling planting and an 83.33% success rate in ball collection tasks. Despite its notable performance, enhancements in sensor feedback and automation are recommended to improve efficiency. This research underscores the potential of cost-effective, multi-microcontroller systems for advancing real-time control and task execution in agricultural robotics
Robotic mist bath wheelchair: innovations in automated body drying and sanitization for improved patient hygiene
Get PDFThis paper presents the development and evaluation of the robotic mist bath wheelchair (MBWC), a multifunctional assistive device designed to enhance hygiene and comfort for individuals with limited mobility. The MBWC integrates mist-based bathing, automated sanitization, and warm air-drying into a compact, wheelchair-mounted system suitable for home and clinical settings. Experimental evaluations demonstrated effective temperature maintenance and a 30% reduction in bathing time compared to conventional methods. User trials with 20 participants indicated a 92% satisfaction rate, reflecting improvements in hygiene, comfort, and operational ease. MBWC provides a cost-effective, hygienic alternative to traditional bathing methods, addressing critical challenges in eldercare and rehabilitation environments