Mobility impairment poses one of the most significant challenges for differently abled individuals, directly impacting their independence, confidence, and quality of life. Traditional wheelchairs—whether manual or joystick-operated—often fail to meet the diverse needs of users with severe physical limitations such as paralysis, neuromuscular disorders, or limb amputations. This paper presents a Smart Voice and Gesture Controlled ML-Driven Wheelchair Assistance system that empowers users to navigate hands-free through voice commands and hand gestures, leveraging Machine Learning (ML) for accurate, real-time motion responses. The hardware framework integrates a Raspberry Pi 4 (main controller), ESP32 microcontroller, MPU6050 accelerometer/gyroscope for gesture detection, HC-SR04 ultrasonic sensor for obstacle detection, and L293D motor driver. On the software side, RNN/LSTM-based models process speech features (MFCCs) while CNN/MediaPipe models recognize hand gestures. A Command Fusion Engine arbitrates between modalities, enforces a safety layer, and executes motor commands. Experimental evaluations project ≥85% command recognition accuracy with sub-200ms response latency, offering a costeffective, offline-capable, and inclusive assistive mobility solution.

Agricultural intelligence is an important trend in the development of modern agriculture. However, agricultural UGVs that rely on manual intervention or fixed path planning suffer from low operational efficiency and poor adaptability. To address these issues, an obstacle-avoidance UGV based on an STM32 microcontroller is designed in this study. The UGV uses a motor driver chip for motion control, Bluetooth for signal transmission, and an integrated image transmission module, which effectively improves obstacle recognition and rapid obstacle avoidance compared with conventional agricultural UGVs. Experimental results show that the steering angle control accuracy of the vehicle ranges from 90.0% to 98.9%, while the obstacle detection accuracy ranges from 92.5% to 98.3%. The UGV is able to stably complete obstacle-avoidance tasks under field conditions

This project presents the design and development of a smart line follower robot using IoT system. The robot is capable of autonomously following a predefined path using infrared (IR) sensors, ensuring accurate navigation without human intervention. In addition to line-following functionality, the system enhances security by incorporating a theft detection mechanism. The theft detection module uses sensors such as motion sensors or IR interruption sensors to detect unauthorized access or disturbances. When a theft attempt is detected, a buzzer is activated to provide an immediate local alert. Simultaneously, the system sends realtime alert messages to the user via Telegram, ensuring instant notification regardless of location. To further enhance monitoring, the project includes a live video streaming feature, allowing users to visually monitor the environment in real-time through an IoT platform. This enables better surveillance and quick decision-making during security breaches. The system operates without traditional remote control, relying entirely on autonomous behaviors and IoTbased communication for monitoring and alerts. The main components used in this project include a microcontroller (such as Arduino or Node MCU/ESP8266), IR sensors for line detection, motion or proximity sensors for theft detection, a buzzer for alerts, a camera module for live streaming, motor drivers, and DC motors for robot movement. The integration of these components creates a cost-effective and efficient smart robotic system suitable for industrial automation and security applications. Overall, this project demonstrates the effective combination of robotics and IoT technologies to achieve autonomous navigation, realtime monitoring, and enhanced security without the need for manual remote operation.

The Next-Generation Smart Classroom is an innovative educational environment that seamlessly integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies to automate and optimize the traditional learning space. This system leverages a Raspberry Pi microcomputer running Python-based software to coordinate multiple hardware components including IR sensors, a camera module, motor drivers, and LED indicators, enabling intelligent control of classroom resources such as lighting, fans, and entry systems. The proposed framework employs facial recognition algorithms for automated student attendance, real-time intrusion detection via IR sensors, and remote monitoring through IoT-enabled dashboards, thereby reducing manual administrative overhead and improving energy efficiency. The system transmits live data to cloud platforms allowing educators and administrators to remotely monitor classroom status, attendance logs, and environmental parameters from any internet-connected device. Experimental results demonstrate that the implemented solution achieves over 95% facial recognition accuracy, reduces energy consumption by approximately 30%, and significantly enhances classroom security and operational efficiency in educational institutions.

Abstract—Agriculture remains a cornerstone of developing economies, yet traditional farming practices continue to rely heavily on manual labor, incurring high operational costs and productivity losses. This paper presents the design and implementation of an IoT and Sensor-Based Smartphone-Operated Multipurpose Agricultural Robotic Vehicle (SARWR) capable of performing multiple farming operations—including grass cutting, seed sowing, soil moisture monitoring, and irrigation—within a single automated platform. The system utilizes an ESP32 microcontroller as the central processing unit, interfaced with soil moisture, temperature, humidity, and Light Dependent Resistor (LDR) sensors for real-time field monitoring. An L298N dual H-bridge motor driver controls the locomotion and task-specific DC motors, while a relay module manages the water pump. The robot is wirelessly controlled through a smartphone application via Bluetooth and Wi-Fi communication protocols. Solar panels provide a renewable energy source, making the system eco-friendly and energy-independent. The proposed system was tested under various field conditions, and results demonstrate significant reductions in manual labor, improved seed placement accuracy, and effective weed removal. The integration of IoT connectivity enables remote monitoring of field parameters through a cloud dashboard, supporting precision agriculture and sustainable farming practices. Keywords—IoT, ESP32, agricultural robot, soil moisture sensor, smartphone control, L298N motor driver, precision farming, solar energy, automation, seed sowing. Previous SOLAR BASED MULTIFUNCTIONAL AGRICULTURAL MACHINE USING ZIGBEE

Abstract- The Research Paper “Design and implementation of smart web-QR controlled vending system” focuses on developing an intelligent and contactless vending system that integrates Internet of Things (IoT) technology with web-based QR functionality. Traditional vending machines rely on manual operation and cash-based payments, making them less efficient, unhygienic, and difficult to monitor remotely. The proposed system overcomes these limitations by using a Raspberry Pi 3B+ microprocessor as the central controller, connected to a Firebase real-time cloud database for monitoring inventory and transactions. Users can scan a QR code displayed on the vending machine to access a web interface built using React JS, which enables them to select products, view prices, and confirm purchases without physical contact. The Raspberry Pi processes the request and activates the corresponding motor driver to dispense the selected item, while sensors verify successful delivery and update the database automatically. This system provides touchless operation, real-time control, and efficient management of inventory. It also demonstrates scalability for integration with digital payment gateways and AI- based stock prediction. By combining IoT, cloud computing, and web technologies, the Research Paper aims to enhance user convenience, hygiene, and automation in next-generation vending solutions. Keywords— Internet of Things (IoT), Smart Vending System, QR Code Technology, Raspberry Pi, Contactless Operation, Real-time cloud database, Firebase, React JS, automated inventory management.

Power quality is a critical factor in maintaining the reliability and efficiency of modern power systems, particularly in distribution networks that extensively utilize power electronic equipment. One of the primary sources of harmonic distortion is the use of Variable Speed Drives (VSDs), which operate as non-linear loads and cause current and voltage waveforms to deviate from their ideal sinusoidal shape. This study aims to analyze the harmonic characteristics generated by VSDs in the distribution system of PT. PLN ULP Tomohon, determine the levels of Total Harmonic Distortion of current (THD-I) and voltage (THD-V), and evaluate the effectiveness of harmonic mitigation using a passive single-tuned harmonic filter. The research employs a descriptive quantitative approach through modeling and simulation of the power system using the Electrical Transient Analyzer Program (ETAP) software. Technical system data, including transformer capacity, distribution network configuration, and load parameters, are modeled in the form of a Single Line Diagram (SLD) and analyzed using the Harmonic Load Flow module. The simulation results indicate that VSD operation produces dominant harmonics at the 5th and 7th orders, which significantly increase the THD levels within the system. The implementation of a passive single-tuned harmonic filter is shown to effectively reduce harmonic distortion, allowing the THD-I and THD-V values to approach or comply with the limits specified in the IEEE 519-2014 standard. Therefore, the application of appropriate harmonic mitigation methods can improve power quality, enhance energy efficiency, and increase the reliability of electrical power systems in distribution networks.

Industrial machinery is highly susceptible to faults such as oil leakage, overheating, excessive vibration, and abnormal current consumption, which may lead to equipment damage, production loss, or safety hazards. This paper presents an IoT-based industrial monitoring and protection system that continuously observes machine health parameters including vibration direction, temperature, oil leakage, and motor current consumption. The system uses a MEMS accelerometer to detect vibration intensity and direction, along with temperature sensors, oil leakage sensors, and current sensors to monitor critical operational conditions. When abnormal conditions are detected, the system automatically stops the motor using a motor driver and activates a buzzer for immediate alert. Additionally, all sensor data and total current usage are transmitted to an IoT platform for real-time monitoring and notifications. This system enhances equipment safety, reduces downtime, improves operational efficiency, and supports predictive maintenance in modern industrial environments

Abstract -Railway transportation is a backbone of modern infrastructure, but safety remains a major challenge due to undetected track faults such as cracks, misalignment, and obstacles. Traditional manual inspection methods are inefficient, time-consuming, and prone to human error. This paper presents the design and implementation of a Smart Railway Track Monitoring and Fault Detection System that enables real-time fault detection and alerting. The system uses ultrasonic sensors to continuously monitor track conditions by measuring distance variations. An ATmega8 microcontroller processes the sensor data and detects faults based on predefined threshold values. When a fault is identified, a GSM SIM900A module sends an instant SMS alert to maintenance personnel. A 16×2 LCD display shows system status, while an L293D motor driver with a BO motor simulates train movement and demonstrates real-time response. The system achieves approximately 95% accuracy with a 2–5 second alert delay, ensuring fast and reliable fault detection. This solution is cost-effective, energy- efficient, and suitable for improving railway safety by replacing manual inspection with automated monitoring.

Precision in pesticide application plays a vital role in sustainable agricultural practices. Conventional manual spraying methods frequently result in excessive chemical usage, uneven distribution, and health hazards to farmers. This paper presents the design and implementation of a solar-assisted autonomous robotic sprayer intended to deliver pesticides in a controlled and energy-efficient manner.The system is built around an ESP32 microcontroller responsible for locomotion control and regulated chemical discharge. A four-wheel drive mechanism powered through an L293D motor driver enables stable field navigation. The spraying unit employs a diaphragm pump integrated with fine atomizing nozzles to achieve uniform droplet dispersion. Energy requirements are met through a 12V solar module supported by a rechargeable battery system, ensuring uninterrupted off grid operation. Field evaluation demonstrates improved spray consistency, reduced chemical consumption, and minimized human exposure. The developed model offers a practical step toward affordable precision agriculture for small and medium-scale farms

ABSTRACT The rapid growth of automation and service robotics has opened new opportunities for improving operational efficiency in public and institutional environments educational campuses, in particular, require frequent transportation of materials, visitor guidance, and inter-departmental coordination, which are often performed manually and are time-consuming. This paper presents the design and development of "EduPorter - Autonomous Campus Assistant Robo." A service-based autonomous robot intended to assist educational institutions in routine logistical tasks. The proposed system integrates line-following navigation, obstacle detection, wireless communication, and real-time monitoring to perform material delivery and guidance functions with minimal human intervention. An ESP32-based control system combined with IR sensors, Proximity sensors. ESP32 camera module, and motor driver circuitry, enables reliable navigation and safe operation in dynamic campus environments. The system demonstrates improved efficiency reduced manpower dependency, and enhanced user experience. The proposed robot provides a scalable and cost-effective solution for smart campus automation and lays the groundwork for future advancements in service robotics Keywords: Autonomous Robot, Service Robot, Campus Automation, Line Follower Robot, ESP32, Obstacle Detection, Smart Campus.

ABSRACT: This paper presents the design and implementation of a smart vehicle safety system using an ARM Cortex-M3 based microcontroller (STM32). The system aims to enhance vehicle safety by detecting obstacles using ultrasonic sensors and providing real-time feedback to the driver. The system integrates two ultrasonic sensors for front and rear obstacle detection, a GPS module for location tracking, DC motors for vehicle movement simulation, and an LCD for displaying status information. The motor driver controls the motors based on sensor inputs. When an obstacle is detected within a predefined distance, the system alerts the user and can stop the vehicle automatically. The proposed system is cost-effective, reliable, and suitable for modern intelligent transportation systems. Keywords: Smart Vehicle Safety, ARM Cortex-M3, STM32, Ultrasonic Sensor, GPS, Embedded System

Abstract— Road damage and potholes are one of the major causes of vehicle accidents and traffic disruptions. Traditional pothole detection methods rely on manual inspection, which is inefficient and time-consuming. This project proposes a Smart Pothole Detection and Alert System using ESP32 that utilizes computer vision and sensor-based detection to identify potholes and automatically control vehicle speed. In the proposed system, a web camera connected to a laptop captures road images periodically. The captured images are compared with a predefined reference image using Python-based image processing techniques. If the similarity between images falls below a threshold, the system identifies the presence of a pothole or road anomaly. The detection result is then transmitted to an ESP32 microcontroller through wireless communication. Once the ESP32 receives the signal, it activates the ultrasonic sensor (HC-SR04) to measure the depth of the pothole. Based on the measured depth, the motor speed is automatically reduced using the L293D motor driver controlling BO motors. This helps simulate a smart vehicle system that slows down when a pothole is detected. The proposed system demonstrates how computer vision, IoT, and embedded systems can be integrated to create an intelligent road safety solution.

Abstract— Precision agriculture demands real-time monitoring and intelligent automation to improve crop yield, conserve resources, and protect crops from environmental and external threats. This paper proposes an IoT-driven precision agriculture system with live monitoring and automated crop protection using an ESP32 microcontroller with inbuilt Wi-Fi connectivity as the central control unit. The system integrates soil moisture, rain, LDR, and PIR sensors to continuously monitor soil conditions, weather changes, light intensity, and unwanted intrusions in the field. A solar-powered battery supply ensures sustainable and uninterrupted operation. Based on real-time sensor data, the ESP32 automatically controls irrigation through a motor driver–based water pump system to optimize water usage. A buzzer alert is activated during intruder detection to prevent crop damage. For enhanced field surveillance, an ESP32-CAM module captures live images and sends instant alerts to farmers via Live monitoring. Real-time sensor data visualization, remote monitoring, and control are achieved through the Blynk IoT platform using the ESP32’s built-in Wi-Fi module, eliminating the need for external communication hardware. An LCD provides local system status display. The proposed system reduces manual intervention, improves water efficiency, and enhances crop security, making it a reliable and cost-effective solution for smart agriculture applications. Keywords — Precision agriculture, Internet of Things (IoT), ESP32 microcontroller, Blynk IoT platform, Smart irrigation system, Automated crop protection, Solar-powered agriculture, Real-time monitoring etc.,

This article presents a high-efficiency, dual-output isolated DC-DC converter for tethered unmanned aerial vehicle (TUAV) illumination. The converter features an integrated architecture combining a multi-level phase-shifted bridge (PSB) and four-phase LLC resonant channels. A multi-level structure on the 750 V input side effectively reduces switching voltage stress and facilitates soft switching. The PSB channel provides wide-range regulated voltage for LED loads, while the LLC channels deliver high current to the DC motor and assist in realizing zero-voltage switching (ZVS) across the system. Both precise and simplified models of the PSB channel are developed to support gain analysis and control design. Optimized modulation modes further enhance efficiency and reduce peak current. A 3 kW experimental prototype validates the design, achieving 98.5% peak efficiency, with an output voltage range of 40-140 V and up to 40 A per port, supplying 1 kW to LEDs and 2 kW to DC motor drivers.

A spy robot is a modern surveillance system used to monitor areas remotely without human presence, improving safety and security. This project develops a spy robot using ESP32 for robot movement control and ESP32-CAM for real-time video streaming. The ESP32 microcontroller controls the DC motors through a motor driver, allowing the robot to move forward, backward, left, and right. The robot is controlled wirelessly using Bluetooth communication from a mobile device, making it easy for the user to operate. The ESP32-CAM module captures live video and transmits it to a web page using Wi-Fi, enabling realtime monitoring of the surrounding environment. This allows the user to observe remote or dangerous areas safely and efficiently. The system is designed to provide continuous surveillance without the need for physical presence. The integration of wireless control and live video streaming improves the functionality and effectiveness of the robot. The project is low cost, portable, and easy to implement using IoT technology. This system can be used in security surveillance, military applications, industrial monitoring, and rescue operations. Overall, the project demonstrates an efficient and reliable solution for remote monitoring and surveillance using ESP32 and ESP32-CAM.

Paralysis caused by spinal cord injuries, neurological disorders, or muscle degeneration significantly affects the mobility and independence of individuals. Assistive robotic technologies such as wearable exoskeletons provide a promising solution to restore movement and improve quality of life. This project presents the design and development of a robotic suit intended to assist paralyzed individuals in performing lower limb movements and basic walking activities. The system integrates sensors, actuators, and an embedded control system to replicate natural human leg motion. Components such as an ESP32 controller, MPU6050 IMU sensor, flex sensors, motor drivers, and DC motors work together to detect user intention and generate assisted movement. The suit is designed using lightweight structural materials such as aluminium or steel to ensure durability and comfort. The robotic suit aims to enhance mobility, support rehabilitation processes, and increase independence for individuals with lower limb paralysis. This work demonstrates the potential of wearable robotics in improving human mobility and supporting modern rehabilitation technologies. Index Terms - Robotic Suit, Exoskeleton, Paralysis Rehabilitation, Assistive Robotics, Mechatronics, Wearable Robotics

This paper presents the design and development of a safety-based hand gesture-controlled wheelchair prototype using an ATmega8 microcontroller. The system uses an MPU6050 sensor to detect hand tilt gestures for movement control. A touch sensor is used for safe activation, while two ultrasonic sensors are used for front obstacle detection and staircase edged etection. The ATmega8 processes all sensor inputs and controls the motor driver circuit for safe prototype emovement. The system is powered by a 12V supply, with a voltage regulator providing 5V to the controller and sensors. The proposed system is low-cost, simple, and suitable as an assistive mobility prototype with improved operational safety

This project aims to build a Bluetooth-controlled car using Arduino technology. A smartphone controls the vehicle wirelessly. The main part of the system is the Arduino Uno, which processes signals from the HC-05 Bluetooth module and sends them to the L298 motor driver to control the DC motors. Users can easily move the car forward, backward, left, or right by sending commands through a mobile application. Features: • Wireless Operation – Controlled via smartphone using Bluetooth connectivity. • Cost-Effective – Uses affordable and easily available components. • Multi-Directional Movement – Moves forward, backward, left, and right. • Easy to Use – Simple mobile app interface for real-time control. • Portable Design – Compact and battery-powered for mobility. Requirements: • Hardware:- Arduino UNO, TT Gear Motor, L298 motor driver, Wheels, HC – 05 Bluetooth module, Jumper Wires, Lithium Battery. • Software:- Arduino IDE C/C++, Arduino Bluetooth Controller. Advantages and Disadvantages: • Advantages: - Real-Time Response – Provides smooth and quick movement based on user commands. - Low Cost – Built using inexpensive and easily available components. - Customizable – Can be enhanced with sensors for obstacle avoidance, line following, or automation. • Disadvantages: - Limited Range – Bluetooth control works only within a short distance (about 10 meters). - Low Speed & Power – DC motors and battery power restrict heavy load or highspeed operation. Existing system (feedback): The Arduino Bluetooth-controlled car is a simple, low-cost project that demonstrates wireless control using a smartphone. It provides hands-on learning in Arduino programming, motor control, and Bluetooth communication, while also offering scope for future upgrades like sensors and automation.

This paper presents the development of GESTOBOT, a smartphone and gesture-controlled robotic system designed to provide an intuitive and wireless human robot interaction platform. The system integrates a robotic arm with a mobile base, enabling both object manipulation and directional movement within a single framework. Hand gestures are captured using flex sensors and inertial sensors such as accelerometers and gyroscopes embedded in a wearable glove or smartphone interface. The acquired signals are processed and transmitted wirelessly via Bluetooth or RF communication to a microcontroller-based receiver mounted on the robot. Upon reception, the control unit interprets the signals and drives DC motors and servo motors through an H-bridge motor driver to execute the desired actions. The mobile platform performs movements such as forward, backward, left, right, and stop, while the robotic arm replicates finger motions for grasping and handling objects. The proposed system eliminates conventional joystick-based control, enhancing portability, flexibility, and user convenience. This approach demonstrates significant potential for applications in surveillance, hazardous environment operations, assistive systems, and educational robotics..