The robot is wirelessly controlled through a web browser interface, enabling live video streaming and remote navigation from any connected device. The night vision camera allows continuous monitoring in low-light or dark environments, making the system suitable for military, border security, and high-risk surveillance applications.

Image processing techniques are implemented on the Raspberry Pi to enhance autonomous intelligence. When the system detects metal objects using sensor, it automatically captures an image and sends an alert email along with the GPS-based location coordinates and also activate the buzzer for alerts. Similarly, face recognition algorithms are employed to identify unknown individuals. Upon detecting an unrecognized face, the robot captures the image and transmits an alert email containing both the image and real-time location data.

The integration of GPS ensures accurate location tracking, while web-based live streaming provides real-time situational awareness. This intelligent robotic surveillance system enhances security operations by combining remote control, night vision monitoring, object detection, face recognition, and automated alert mechanisms into a compact and cost-effective platform.

The main objectives of the project:

  To design and build a spy robot using the Raspberry Pi 3 Model B+.

  To provide live video streaming using a night vision camera for monitoring in dark areas.

  To control the robot wirelessly through a web browser.

  To track the robot’s real-time location using a GPS module.

  To detect metal objects using sensor techniques.

  To recognize unknown faces using face recognition technology.

  To automatically capture images and send alert emails with location details when a threat or metal is detected.

  To store captured data on an SD card.

  To ensure proper power supply using a rechargeable battery and voltage regulator.

  To develop a smart and cost-effective robotic system for security and surveillance applications.

The major building blocks of this project are:

1. Battery Power Supply.
2. Raspberry pi3 B+ processor.
3. SD card.
4. Night Vision Pi camera.
5. GPS.
6. LCD display.
7. L298 motor driver with DC motors.
8. LM2596 Buck converter.
9. Metal detector sensor.
10. Buzzer.

Software’s used in the project:

1. Raspbian OS.
2. WEB technology.
3. Express SCH for Circuit design.
4. Face recognition.

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The main building blocks of the project are:

• ARDUINO NANO Microcontroller.
• Voltage sensor.
• Current sensor.
• Raspberry pi zero.
• Li-ion Battery.
• LM2596.
• DC Motor.
• LCD display.

Software’s used:

• Arduino IDE for Embedded C programming.
• Express SCH for Circuit design.

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A battery management system (BMS) is proposed which is used for electronic vehicle that manages a rechargeable battery (cell or battery pack), such as by protecting the battery from operating outside its safe operating area, monitoring its state of charging using Machine Learning algorithm to improve the performance of the designed project. Machine learning and IOT; real-time data from multiple sensors can be analyzed simultaneously, providing a comprehensive view of vehicle health and performance. Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.

The Raspberry Pi  measure the SOC (State-of-Charge) from voltage and current sensors and based on that it will switch ON/OFF the relays for Battery charging. Here relay works as a switch to ON/OFF the charging connection. And also, it will display the voltage, current, temperature, SOC values on LCD module. It will activate the Buzzer if the sensor data exceed threshold value. Raspberry pi has an inbuilt WI-FI, which is used to upload the Battery parameters such as voltage, current, temperature and SOC values into the Thingspeak cloud along with date and time.

The major building blocks of this project are:

• Adapter power supply.
• Raspberry pi.
• Temperature sensor.
• Voltage sensor.
• Current sensor.
• Buzzer.
• Li-Ion Battery pack.
• Cooling fan.
• Relay.
• Charging Circuit.
• LCD display.
• LED Indicators

Software’s used in the project:

1. Embedded Linux OS.
2. Python language.
3. Express SCH for Circuit design.
4. Machine learning (ML).
5. Thingspeak cloud technology.

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The energy meters are connected to the micro controller through an optocoupler. The optocoupler sensor gives an interrupt each time the meter LED ashes to the programmed PIC controller and PIC microcontroller process this data to the raspberry pi  module then raspberry pi module send this data to the thingspeak cloud.

ThingSpeak is an open-source Internet of Things (IoT) application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network. This IoT device could measure the SOLAR parameters. It continuously monitors the data and updates them to an IoT platform. We can increase the monitoring techniques by making use of advanced technology. In this project we are making use of technology to sense solar energy efficiency so that efficient services can be provided to the solar systems.

. The status of the project is display on LCD. This design eliminates the human involvement in electricity maintenance. The project makes a use of PIC Microcontroller, raspberry pi zero w processor and it has inbuilt Wi-Fi which is monitor the energy consumption in remote location. The SD card is a key part of the Raspberry Pi; it provides the initial storage for the Operating System and files.

Features:

• Consumer can monitor their energy consumption like current, wattage, KWH in thing speak cloud along with date and time.
• Monitoring solar reading likes voltage, wattage in thing speak cloud so user can check from anywhere on the globe.
• Visible alerts using LCD display.
• Using PIC Microcontroller for analog to serial converter.
• Using RASPBERRY pi zero W to upload the data into the thing speak cloud.
• To achieve this task using PIC microcontroller and raspberry pi zero.

The project provides the following learning’s:

1. KWH calculations.
2. Energy meter interfacing techniques to microcontroller.
3. Thingspeak technology.
4. Raspberry pi zero processor.

The major building blocks of this project are:

• Power supply
• Energy Meter
• Optocouplers
• Solar panel.
• LCD display.
• Raspberry pi.
• PIC Microcontroller.
• Crystal oscillator.
• Reset button.
• LED indicator.

Software’s used:

• PIC C compiler to write the program.
• Pickit2 programmer to dump the code into the microcontroller.
• Linux OS for Raspberry pi zero.
• Express SCH for circuit diagram.

Regulated Power Supply:

Block Diagram:

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Smart Receptionist with smart lock system is mainly designed and developed for security system. This smart security system is used to see a visitor when the main door of the office or Home is closed. Wi-Fi (Short for Wireless Fidelity) is a wireless technology that uses radio frequency to transmit data through the air. Wi-Fi has initial speeds of 1mbps to 2mbps. Wi-Fi transmits data in the frequency band of 2.4 GHz. It implements the concept of frequency division multiplexing technology. Range of Wi-Fi technology is 40-300 feet.

The controlling device of the whole system is a raspberry pi processor; it has an inbuilt Ethernet. LCD display, Pi-camera, servo Motor is interfaced to the raspberry pi processor. In this system whenever a person presses the calling bell, image of the person is captured by the Pi-camera and it will send the raspberry pi then the processor mails the person image to owner. Once the image is uploaded user need to check the image and take decision accordingly like access/denies through mail. If the person denies the permission to open the door, door will remain locked and the system will give buzzer alert. The status of the project is displayed on LCD display. Raspberry Pi is the controlling section in our project. We program it using Python Language and the OS used here is Linux.

Objectives of the project:

• Buzzer alert for unknown persons.
• Door access/denies from MAIL.
• Using Pi camera to capture the image.
• Servo motor-based door lock/unlock.
• Using raspberry pi ZERO W to achieve this task.

The major building blocks of the project are:

• Power Supply. u Raspberry pi ZERO W.
• SD card. u Pi camera. u Buzzer.
• LCD display.
• Servo motor.

Software’s used:

• Raspbian OS.
• Python programming language.
• Express SCH for Circuit design.
• IOT technology.

Block diagram:

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This system to detect the object using machine learning algorithm which is written in embedded Linux platform. All images like table, chair, bottle…..we will prepared dataset and stored into the raspberry pi and trained for preprocessing. This system uses Ultrasonic sensor to detect the nearest obstacle and announce the voice through speaker.

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It mainly focuses on image processing; video capture and analysis including features like face detection and object detection.

In this project we are using python language to write the program. Python is an interpreter, object-oriented, high-level programming language with dynamic semantics. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.

The controlling device of the whole unit is a Raspberrypi3 processor to which input and output modules are interfaced. Raspberrypi3 has Broadcom BCM2837B0, Cortex-A53 (ARMv8) 1GB LPDDR2 SDRAM 1.4GHz 64-bit quad-core processor, dual-band wireless LAN, Bluetooth 4.2/BLE, faster Ethernet, and Power-over-Ethernet support (with separate PoE HAT).

The processor is programmed in PYTHON language which intelligently performs the specific task. Here, the processor gets input from the sensor and pi camera attached to the raspberry pi. This input is processed by processor and announce the voice appropriately.

The major features of this project are:

• 1. Automatic obstacle sensing.
  2. Automatic object detection and classification.
  3. Using Raspberrypi3 processor to achieve this task.
  4. Using pi camera and openCV machine learning to detect the objects
  5. Using Ultrasonic sensor to detect the obstacle.
  6. Voice alerts for obstacle detection.
  7. Voice announce for object detection.

The major building blocks of this project:

• Battery power supply.
• Raspberry Pi3 Processor.
• SD Card.
• Ultrasonic sensor.
• Pi camera.
• LED indicators.

Software’s used in the project:

1. Python Programming.
2. OpenCV MACHINE LEARNING Image processing.
3. Embedded Linux operating system.
4. Express SCH for Circuit design.

Block Diagram:

video:

The objectives of the project are:

• To design and implement of fingerprint and face authenticated based real time security system.
• To design and implement face authentication of captured image using pi camera by Python platform on Raspberry Pi.
• The captured image is compared and verified with the database, if found matching then system gives the Green LED indications.
• In case failure of face authentication then system gives the RED led indication.
• To design and implement fingerprint authentication using finger print module by python platform and raspberry.
• By using two buttons we can enroll/delete the finger prints.
• This system also give the red, green led indications for fingerprint access and denied.
• Visible alerts using LCD display.

The major building blocks of the project are:

• Power supply.
• Raspberry Pi
• Pi Camera
• Finger print module
• LCD display.
• Two push buttons.
• Red, Green LEDs.

Software’s used:

1. Embedded Linux programming.
2. Express SCH for Circuit design.

Block diagram:

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