Volume 12 | Issue 5 |

 Abstract

The goal of the project "Self-Navigation Robotics: Mastering Autonomous Path" is to create an intelligent robot car that can drive itself from a starting point to a user-specified destination while dynamically changing its course to avoid obstacles in real time. In order to accomplish flawless autonomous navigation in challenging surroundings, the project blends a powerful combination of sensor technologies, innovative algorithms, and hardware components. This study presents a unique camera module, LiDAR, ROS2, Gazebo, and Raspberry Pi self-navigating vehicle system. LiDAR enables accurate 3D mapping and obstacle recognition, while ROS2 provides seamless system integration and communication. Gazebo provides a reliable simulation environment for verifying system functionality prior to a system being physically deployed. The Raspberry Pi functions as the main processing unit, managing decision-making algorithms and combining sensor data. The camera module improves versatility in a range of circumstances by enhancing perception with visual signals in addition to LiDAR. More advancements are possible when the system architecture prioritizes scalability and real-time responsiveness. Experimental validation the system's autonomous navigation competency highlights its potential for robotics research and industrial automation applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Self-navigating automotive system, Camera module, LiDAR, ROS2, Gazebo, Raspberry Pi, 3D mapping, Obstacle identification, Simulation, Central computing unit, Sensor data fusion, Real-time responsiveness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this paper, Sericulture involves raising silkworms to produce silk and is crucial for India's development. Temperature and humidity are vital for healthy silkworm growth. We used a MEGA controller to monitor silkworms in real-time. Image processing helps identify infections of silkworms. The MEGA controller automates tasks like temperature control, reducing manual work for farmers. It also monitors the environment in the silkworm rearing room. Image processing detects color changes in silkworms, indicating the infections.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

SMART SERICULTURE USING IMAGE PROCESSING

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In contemporary times, significant resources are allocated towards implementing antiquated security measures to deter border trespassers. In high-risk areas where conventional military tactics may falter, various military entities employ robotic assistance. These military-grade robots are clandestinely deployed and outfitted with a suite of technologies including cameras, sensors, metal detectors, and camouflage. The primary objective of our system is to seamlessly blend into the surrounding environment, while also boasting additional functionalities such as an infrared sensor for intruder tracking and Wi-Fi connectivity for real-time data processing. Consequently, the proposed Wi-Fi system minimizes errors in defense operations, thereby bolstering national security against potential intruders.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blending into environment, Military-grade Robots, IR (Infrared), WI-FI (wireless fidelity)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project outlines a cost-effective 3D scanning solution utilizing Arduino Nano technology. It utilizes structured light to capture the geometry of objects. The hardware setup comprises an Arduino Nano microcontroller, a camera module, and a laser module for structured light projection. Software-wise, it employs an open-source computer vision library for image processing and depth map generation. The workflow involves system calibration to ensure optimal performance and accuracy. Once calibrated, the structured light pattern is projected onto the object, and images are captured by the camera. These images are then processed to extract depth information, enabling the reconstruction of the object's 3D model. The resulting point cloud data can be refined further for improved accuracy. To evaluate the scanner's performance, experiments are conducted with objects of various shapes and sizes. Metrics such as accuracy, resolution, and scanning speed are analysed to gauge its practical usability. Overall, the developed 3D scanner demonstrates its ability to generate detailed and accurate 3D models, making it suitable for applications like rapid prototyping, reverse engineering, and educational purposes.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Low-cost 3D scanner, Arduino Nano microcontroller, Rapid prototyping, Calibration for optimal performance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper shows a comparison of power requirement and delay consumed between the 4 - bit ALU that is designed using a fin-shaped field-effect transistor [FinFET] and the traditional CMOS to demonstrate a better and efficient 4 - bit ALU in terms of delay and power consumption. The aim of this work is provide a Comparative Analysis of Arithmetic Logic Unit (ALU) Performance using CMOS and FinFET Technologies. ALU is a fundamental component in digital circuits, performing arithmetic and logic operations. The transition from CMOS to FinFET technology has brought significant advancements in terms of power efficiency, speed, and scalability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ALU (Arithmetic Logic Unit), CMOS (Complementary Metal Oxide Semiconductor), FinFET (Fin Field Effect Transistor), technology, comparison, performance evaluation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

By combining cutting-edge sensors and machine learning on Raspberry Pi, the "Weather Wizards using Machine Learning and Raspberry Pi" initiative transforms weather forecasting. By implementing Random Forest algorithm we record temperature, humidity, precipitation, wind speed using four sensors namely (wind, temperature, humidity, rain) . We deliver real-time forecasts for sunny, cloudy, windy, and rainy days .This ground-breaking system represents a huge advancement in weather prediction, serving applications and businesses that depend on accurate and fast weather data

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Weather Forecasting, Machine Learning, Raspberry Pi, Real-Time data, Sensors.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the world of wireless communication, there is an increasing demand for high-performance MIMO (Multiple Input Multiple Output) antenna systems tailored for smartphone applications. MIMO technology increases data rates, coverage, and reliability by using multiple antennas for transmission and reception. However, integrating MIMO antennas within the constraints of compact smartphone designs is fraught with difficulties, particularly in terms of guaranteeing adequate isolation between antenna parts to minimize interference. This work investigates the design and implementation of a MIMO antenna system specifically designed for smartphone applications in order to establish and maintain excellent isolation between antenna sections. Many design strategies are investigated, including better materials, decoupling techniques, and antenna element placement. In addition, recent advances in the fields of metamaterials, smartphone form factor integration, and micro antenna designs are discussed. Furthermore, the study addresses performance metrics including data throughput, power economy, and dependability, providing an understanding of the practical uses of these advancements. This paper's main objective is to promote MIMO antenna systems for smartphones by offering a comprehensive grasp of the challenges, solutions, and most recent developments in achieving high isolation within small form factors.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smartphone applications, High isolation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents and focuses on the design and development of a A Human Following Robo (HFR) is a technology that can follow a human operator in an autonomous manner. Maintaining a constant distance and orientation in relation to the human operator is the major goal of the HFR. while navigating through indoor and outdoor environments. Key components of the system include computer vision for people recognition and tracking, obstacle avoidance mechanisms, and motion control strategies. The effectiveness of the proposed HFR system is evaluated through extensive simulations and real world experiments, demonstrating Its capacity to accurately track and follow a human operator in various scenarios. The results highlight the potential of HFRs as versatile and intelligent robotic assistants capable of enhancing human-machine interaction and productivity in diverse applications. The development of Human Following Robots (HFRs) represents a significant advancement in robotics technology, offering the potential to revolutionize various industries such as surveillance, healthcare, and entertainment. These robots are designed to autonomously track and follow human operators, facilitating seamless human-robot interaction and enhancing productivity in dynamic environments. This paper aims to explore the design, development, and evaluation of an HFR system capable of intelligently tracking and following a human operator. By leveraging a combination of sensors, actuators, and control algorithms, the HFR system enables intuitive and efficient navigation while maintaining a safe and consistent distance from the human operator.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Human Following Robo (HFR), Obstacle Avoidance, Object detection, Burning source code to processor

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper focuses on the development of a versatile robotic arm with advanced functionalities, aiming to enhance automation in diverse industries. Integrating precision engineering and smart control systems, the robotic arm demonstrates agility and adaptability for various tasks, such as assembly, pick-and-place operations, and intricate manipulations. Employing state-of-the-art sensors and machine learning algorithms, the system ensures optimal performance and safety. This project contributes to the evolution of robotic technology, addressing real-world challenges and fostering efficiency in industrial processes. Developing a cutting-edge robotic arm is the primary objective of this project, aimed at revolutionizing automation across industries. Leveraging advanced materials and sophisticated control algorithms, the robotic arm exhibits unparalleled precision and adaptability. Integrating innovative sensing technologies and machine learning, it excels in complex tasks, offering a transformative solution for assembly lines, manufacturing, and research applications. This project underscores the pivotal role of robotics in shaping the future of automation, showcasing a versatile and intelligent robotic arm that pushes the boundaries of technological innovation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Robotic arm, Automation, Precision engineering, Machine learning algorithms, Industrial processes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Wild animals become more aggressive when wounded because they're scared and confused and may think you're trying to capitalize on their vulnerable state. They're more likely to lash out and bite or scratch you than to accept your help. To address this, our project proposes an alerting system using YOLOv3, a real- time object detection algorithm based on deep convolutional neural networks, to classify and monitor animals that are hurt in Zoo or forests. This algorithm enables efficient identification and tracking of animals, aiding safeguarding wildlife observers and ensuring the preservation of wildlife in their natural habitats.By this wildlife monitoring robotic car using IOT, wildlife observers may get up and personal with wild creatures. An ESP32 is used in this system. The system receives these orders using a Wi-Fi module. We can capture the images of the wildlife continuously. And able to detect the wildlife.We are using the MLX9061an infrared thermometer designed for non-contact temperature sensing to measure the temperature of the animals and an ultrasonic non-contact type of sensor used to measure a wildlife's distance.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image Processing, YOLO Algorithm, Convolutional Neural Network, COCO, ESP,UNO,IDE.

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Creative Commons Attribution 4.0 and The Open Definition