Multi mode remote control car

Multi mode remote control car Multi mode remote control car Multi mode remote control car Multi mode remote control car

Research Rationale

Throughout history, humanity has consistently sought to master and influence its environment, from the invention of the wheel to the Industrial Revolution and the latest advancements in AI The Multi-mode project represents a significant milestone in this ongoing quest, aiming to develop machines that can not only listen but also learn and adapt to their surroundings.

Choosing a car is a straightforward decision, as cars embody progress, movement, and innovation From Karl Benz's pioneering automobile to Elon Musk's autonomous Tesla, each development signifies not only technological advancement but also our changing connection with machines Cars are tangible representations of our aspiration to travel farther, quicker, and more efficiently Creating vehicles that function across various control modes transcends mere technical challenges; it delves into the future of automation and human creativity working in harmony.

Transportation has consistently reflected advancements in technology, with innovations like the SpaceX Starship reshaping our understanding of space travel Similarly, autonomous vehicles are revolutionizing mobility on Earth The Multi-mode, despite its modest scale, captures the essence of this transformative movement, symbolizing the drive for continuous improvement and daring exploration.

Reasons for selection the topic

The topic of "Multi-mode Remote Control Car" arises from the growing need for innovative remote control systems in the era of IoT and robotics, as traditional single-mode systems like RF or Bluetooth lack the necessary flexibility and multifunctionality Inspired by Alan Kay's assertion that "the best way to predict the future is to invent it," this project seeks to design a system that bridges conventional and smart automated technologies, paving the way for future advancements By integrating web-based control, gesture-based navigation, and autonomous obstacle avoidance into a unified platform, the project addresses the limitations of traditional systems while aligning with global technological trends This initiative leverages IoT technologies and advanced sensors to create scalable, efficient, and cost-effective solutions adaptable to various real-world applications, ultimately transforming industries such as transportation and environmental monitoring.

The article emphasizes a dedication to enhancing practical knowledge in embedded systems and robotics, creating a foundation for education and research By integrating cutting-edge technologies like ESP32-CAM, MPU6050, and HC-SR04 into a unified system, it showcases the significance of merging hardware and software innovations Reflecting Steve Jobs' belief that “innovation distinguishes between a leader and a follower,” this project aims to pioneer the development of smart, flexible systems for the future.

This project addresses the limitations of traditional remote control systems by harnessing the rapid advancements in IoT and robotics It aims to contribute to real-world applications and educational initiatives, ensuring alignment with practical needs and academic objectives Ultimately, this effort provides a significant and impactful addition to the field.

Research Motivation

This research aims to bridge critical gaps and explore opportunities in remote control systems and their applications, particularly focusing on traditional systems that rely on established technologies.

Traditional RF and Bluetooth controls are limited to a single mode, restricting their versatility This research aims to address these constraints by developing a multi-mode remote control system that combines web-based, gesture-based, and autonomous control for improved adaptability and user engagement Additionally, the rapid evolution of IoT and robotics highlights the necessity for flexible, remote-controlled, and automated systems applicable in various sectors, including monitoring, transportation, and driver training By utilizing IoT and advanced sensors, this project showcases the integration of modern technologies to create intelligent and efficient systems.

The system provides significant real-world benefits, such as driving simulations for learner assistance, operations in challenging environments, and a platform for testing control and automation algorithms This research emphasizes the integration of IoT and sensors into a flexible, scalable system, effectively connecting traditional remote control systems with the advanced smart automated systems needed for contemporary applications.

Research Aims and Objectives

This research focuses on designing and evaluating a multi-mode remote control system that combines web-based control, gesture-based navigation, and autonomous obstacle avoidance By utilizing advancements in IoT and robotics, the project aims to showcase the practicality of modern technologies in developing scalable and efficient systems These innovative systems have potential applications in education, transportation, and environmental monitoring, providing significant academic contributions and real-world benefits.

The research focuses on creating a modular hardware and software architecture that integrates advanced technologies, utilizing the ESP32-CAM for real-time video streaming and web-based control The MPU6050 sensor enhances gesture recognition accuracy, while the HC-SR04 ultrasonic sensor enables autonomous obstacle avoidance by detecting barriers Effective communication and integration among these components are essential for optimal performance across all control modes Additionally, the project aims to optimize performance by reducing latency in web control, enhancing gesture recognition, and improving obstacle detection efficiency through thorough calibration, tuning, and testing in various scenarios.

This research emphasizes the importance of evaluation and validation through extensive experimental tests that assess system performance in latency, accuracy, and reliability Conducted under diverse environmental and operational conditions, these tests evaluate the system's robustness The results are compared with theoretical predictions and existing systems to confirm the proposed approach's effectiveness By presenting detailed performance metrics, this study aims to establish a benchmark for multi-mode control systems and identify opportunities for enhancement.

The project encounters several limitations despite its successes The HC-SR04 ultrasonic sensor has difficulty detecting small objects or surfaces that poorly reflect sound waves, such as foam or fabric Additionally, the cost-effective ESP32-CAM suffers from limited resolution and processing power, affecting real-time image processing quality The system's dependence on a stable Wi-Fi connection makes it vulnerable to network instability, causing latency and reduced responsiveness in web-based control Environmental factors like lighting variations, surface irregularities, and weather conditions also create challenges, as the system is mainly designed for indoor use Furthermore, the simplistic algorithms for gesture control and obstacle avoidance may lead to suboptimal performance in complex or dynamic environments Lastly, interference from 2.4GHz Wi-Fi signals can disrupt the NRF24L01, complicating the reception of vehicle control commands.

The research establishes a solid groundwork for future enhancements in IoT-enabled robotic systems, highlighting the potential for integrating advanced sensors like Lidar for improved obstacle detection, utilizing artificial intelligence for enhanced decision-making, and broadening usability to outdoor and industrial settings By tackling these challenges in subsequent developments, the project aims to significantly advance the creation of adaptable and intelligent robotic systems designed for contemporary applications.

Research Scopes

This research focuses on developing and evaluating a multi-mode remote control car system that features web-based control, gesture-based navigation, and autonomous obstacle avoidance The study prioritizes practical hardware and software design and testing, employing a modular approach for flexibility and scalability Key hardware components include the cost-effective ESP32-CAM for real-time video streaming, the MPU6050 for gesture recognition, and the HC-SR04 ultrasonic sensor for obstacle detection The small-scale prototype is optimized for indoor use, serving as a proof-of-concept for the system while acknowledging limitations such as the ESP32-CAM's resolution and the HC-SR04's detection range.

The system functions in three distinct modes: web-based control via a web interface, gesture-based navigation using hand gestures with the MPU6050, and autonomous obstacle avoidance through the HC-SR04 sensor This research intentionally excludes long-range outdoor navigation, voice control, and multi-vehicle interaction, concentrating on core objectives within a defined timeframe The study is limited to indoor environments with controlled conditions, as outdoor testing under varying lighting, weather, and terrain is not included Although the timeline restricts iterative improvements, the project establishes a strong foundation for future research aimed at expanding functionality, optimizing performance, and investigating advanced applications, ensuring a focused approach that meets objectives while guiding further development.

Research Contents

Inspired by Alan Turing's vision of integrating logic with machine capabilities, this research focuses on the design, development, and evaluation of a multi-mode remote control system that merges modern robotics with IoT technologies It starts with an extensive review of existing literature to ground the study in established theoretical frameworks and practical insights that have influenced control system evolution By analyzing traditional single-mode designs and the latest advancements in multi-modal integration, the research lays a strong foundation for its technical innovations.

This research focuses on developing a modular and adaptive system that integrates web-based control, gesture-based navigation, and autonomous obstacle avoidance The ESP32-CAM is a cost-effective solution for real-time video streaming, embodying the project's sophisticated approach The MPU6050 sensor enhances the system's ability to interpret human gestures, influencing its directional logic Additionally, the HC-SR04 ultrasonic sensor serves as the car's perceptive eyes, accurately detecting obstacles and mimicking human spatial awareness.

The study emphasizes empirical rigor through iterative experimentation, testing and refining each subsystem It carefully analyzes the interaction between hardware and software, ensuring a balance between computational constraints and the need for efficiency and reliability Performance metrics, including latency, accuracy, and obstacle detection rates, are evaluated under various operating conditions, showcasing the system's adaptability and identifying opportunities for future improvements.

This research builds on Turing's vision of a symbiotic relationship between humans and machines by developing a system that not only responds to commands but also anticipates the needs of modern applications With a modular design and a focus on purposeful innovation, it aims to foster a dialogue between technology and society while providing a scalable model for future intelligent systems Through clear objectives, a rigorous experimental framework, and a commitment to advancing technology, this project highlights the importance of thoughtful design in a rapidly changing technological landscape.

Research Method

Research Philosophy and Approach

This research adopts a pragmatic approach, emphasizing practical design, implementation, and testing of a multi-mode remote control car The methodology combines theoretical principles of embedded systems and

IoT with hands-on experimentation to ensure the proposed solutions are functional and applicable in real-world scenarios.

The system design process involved selecting and integrating key hardware components like Arduino Uno and Nano microcontrollers, ESP32-CAM, and sensors such as HC-SR04 and MPU6050, chosen for their compatibility, cost-effectiveness, and ability to support multi-mode operation The software development included writing firmware for sensor data processing and motor control, creating a web-based interface for remote operation, implementing gesture recognition algorithms for hand movement interpretation, and developing obstacle detection and avoidance algorithms utilizing ultrasonic sensors.

Experimental testing was carried out using a real-world prototype of the system, focusing on the strategic placement of components to enhance sensor performance, motor stability, and communication reliability Initial evaluations in controlled environments assessed the performance of each control mode, measuring the responsiveness of the web-based control mode under different network conditions, the accuracy of gesture-based commands, and the efficiency of the autonomous navigation system in obstacle detection and avoidance Feedback from these tests played a crucial role in refining both hardware and software, leading to significant improvements in overall system performance.

The system's performance was thoroughly evaluated through key metrics like latency, accuracy, and operational stability Benchmarking against existing technologies revealed valuable insights into the proposed system's strengths and limitations This iterative analysis guaranteed that the research outcomes were robust and reliable, effectively addressing the challenges associated with multi-mode remote control systems.

Data Collection Methods

The research employed a systematic data collection process to thoroughly evaluate the system's performance across various control modes, including web-based, gesture-based, and autonomous navigation Data was collected from sensors during real-world testing and analyzed to confirm the effectiveness and reliability of the proposed system.

Data was collected via Arduino microcontrollers interfacing with components like the MPU6050 accelerometer and gyroscope, HC-SR04 ultrasonic sensors, and ESP32-CAM Real-time processing of movement data from the MPU6050 enabled gesture-based control, allowing user commands to be logged and compared to expected outcomes for system accuracy assessment In autonomous navigation, the HC-SR04 continuously measured distances to obstacles, which were crucial for evaluating the precision of the obstacle detection algorithm Performance metrics such as latency, response time, and accuracy were recorded during testing using various tools, including a high-resolution camera that visually confirmed the vehicle's responses to commands and environmental changes Additionally, software-based logging tools tracked sensor readings and system commands for post-test analysis, while performance monitoring tools assessed network conditions to analyze latency in web-based control.

User interaction feedback, alongside sensor data, was gathered to evaluate the system's usability and intuitiveness This information was instrumental in enhancing the control interfaces and improving the gesture recognition algorithm.

By combining quantitative sensor data with qualitative user feedback, the research ensured a holistic evaluation of the system’s capabilities and limitations.

This systematic method of data collection facilitated a comprehensive understanding of the system's behavior, which in turn informed iterative enhancements and confirmed the performance of the multi-mode remote control car across diverse scenarios.

Issues of Validity and Reliability

Ensuring the validity and reliability of the data collected in this research was crucial A systematic methodology was employed, emphasizing consistent testing, controlled environments, and minimizing external interference to achieve accurate results.

To ensure accurate and reproducible data, multiple trials were conducted for each control mode—web-based, gesture-based, and autonomous navigation—under identical conditions Latency measurements in web-based control were tested across various network conditions to identify patterns and eliminate anomalies Gesture recognition accuracy was validated by executing the same gestures multiple times, confirming consistent command interpretation Additionally, the obstacle avoidance system was tested with different layouts to ensure reliability across diverse scenarios.

To ensure data accuracy, environmental factors like lighting, surface textures, and network interference were meticulously controlled during testing The system's adaptability was evaluated in both controlled indoor and semi-controlled outdoor environments, with the autonomous navigation mode tested in low-noise areas to guarantee precise ultrasonic sensor readings Meanwhile, the gesture control mode was validated under stable lighting conditions to avoid sensor miscalibration Any environmental deviations were documented for analysis Additionally, sensors such as the MPU6050 and HC-SR04 underwent calibration before each testing session to uphold data integrity, while software logging mechanisms were validated to confirm accurate data recording without loss or corruption.

The research implemented rigorous measures to guarantee the validity and reliability of its findings Through comprehensive testing and a controlled methodology, the study demonstrated confidence in the system's performance and its relevance to real-world applications, showcasing its robustness and precision.

Thesis Layout

This report mirrors that structure:

• Introduction– Outlines the project’s motivation, objectives, and scope.

• Literature Review– Examines existing technologies and development methodologies.

• Theoretical Modeling and Designing– Focuses on technical aspects and theoretical foundations related to system design and modeling.

• Experimental Result and Discussion- Present the experimental results and discuss in detail the main findings of the study.

• Conclusion and Recommendations– Reflects on the project’s success and offers pathways for future development.

The Multi-mode Car project is driven by a compelling question: "What if machines could navigate through gestures instead of buttons while independently avoiding obstacles?" This initiative goes beyond mere innovation; it embodies a deep-rooted curiosity that, as Einstein famously stated, is essential for uncovering the universe's mysteries.

Theoretical Background

Embedded Systems Principles

Embedded Systems – Overview and Definition

Embedded systems are specialized computing systems designed for specific tasks, distinguishing them from general-purpose computers They play a crucial role in remote control applications, enabling the management of devices like robotics, drones, smart home technology, and industrial machinery from a distance This integration allows for real-time monitoring and control, overcoming spatial limitations and fostering an interconnected environment As noted by Lee and Seshia (2016), these systems combine hardware and software to ensure precise control over physical processes, making them essential in the fields of IoT and robotics Ultimately, embedded systems form the backbone of modern automation, where convenience and precision merge through intelligent technologies.

Microcontrollers (MCUs) like Arduino Uno and Nano are essential components in IoT systems, acting as the brain of embedded devices They serve as gateways that connect the physical world to the digital ecosystem by integrating sensors, actuators, and communication modules Arduino's user-friendly design and scalability enable seamless interfacing with various devices, while the compact Arduino Nano is perfect for space-constrained applications These microcontrollers process sensor data, execute algorithms, and facilitate real-time communication, functioning as the command center for IoT applications With features like GPIO pins for data exchange and PWM for efficient motor control, microcontrollers are ideal for creating modular and scalable designs, such as the Multi-mode RC Car.

Structure and Operating Principles of Microcontrollers (GPIO, PWM, ADC):

GPIO, or General Purpose Input/Output, serves as essential communication channels in embedded systems, enabling microcontrollers to engage with the external environment These versatile pins facilitate interaction by reading signals from sensors (input) and controlling devices (output), making them crucial for effective system operation.

PWM, or Pulse Width Modulation, is an effective technique used to simulate analog output by adjusting the width of digital pulses This method is commonly employed to control motor speeds, LED brightness, and heating elements By utilizing PWM, users can achieve efficient power consumption while ensuring precise control over various applications.

Analog-to-Digital Converters (ADCs) play a crucial role in transforming real-world analog signals—like temperature, pressure, and light—into digital data for microcontrollers This conversion process is vital for IoT systems, allowing them to effectively interact with and comprehend the physical environment.

The Internet of Things (IoT) refers to a vast network of interconnected devices capable of autonomously exchanging data and collaborating This technological advancement aims to improve efficiency, automation, and intelligence in various aspects of life, including smart homes and industrial automation As the trend continues, it is expected that nearly all devices will be interconnected, forming a complex network that enhances human experiences The rise of affordable sensors, strong connectivity, and cloud computing is propelling IoT's growth, establishing it as a fundamental component of the Fourth Industrial Revolution.

IoT technology enables real-time monitoring and remote control of various systems, allowing users to manage home appliances, vehicles, and industrial machines from virtually anywhere By integrating sensor networks with cloud computing, IoT facilitates immediate data collection and analysis, enabling systems to adapt to environmental changes in real-time This shift from manual control to automated decision-making significantly enhances efficiency across both consumer and industrial sectors.

The ESP32-CAM module exemplifies an IoT device that integrates Wi-Fi and Bluetooth for seamless data and video transmission, making it ideal for real-time surveillance and remote monitoring Complementing this, wireless modules like NRF24L01 enable long-range communication between microcontrollers, fostering a decentralized IoT network where multiple devices interact independently Together, these technologies capture the core principles of IoT: decentralization, autonomy, and interconnectivity.

• Communication Protocols: Communication protocols form the backbone of any IoT system, enabling data to flow between microcontrollers, sensors, and other peripherals.

UART, or Universal Asynchronous Receiver/Transmitter, is a robust protocol designed for asynchronous data transfer, primarily facilitating communication between microcontrollers and external devices like Bluetooth modules and wireless transceivers This protocol allows devices to exchange data efficiently over a serial connection.

SPI (Serial Peripheral Interface) is a high-speed, synchronous communication protocol designed for fast data transfer between microcontrollers and various peripherals, including sensors, displays, and memory devices Its rapid speed makes it particularly suitable for real-time applications that require low-latency communication.

I2C, or Inter-Integrated Circuit, is a communication protocol that enables interaction with multiple devices through a two-wire interface This protocol is widely utilized for connecting various sensors or actuators to a single microcontroller, effectively simplifying wiring and minimizing system complexity.

The NRF24L01 is an advanced wireless communication module that excels in long-range data transmission, operating on the 2.4 GHz frequency band and achieving distances of up to 100 meters in open environments Its low power consumption and capability to establish mesh networks make it particularly suitable for IoT applications requiring extensive wireless communication By facilitating decentralized, peer-to-peer communication, the NRF24L01 allows devices to share information without dependence on a centralized Wi-Fi network.

The ESP32-CAM Wi-Fi module is a compact and versatile platform that combines Wi-Fi, Bluetooth, and a camera interface, making it ideal for IoT applications Its seamless connectivity enables devices to easily upload images and video streams to the cloud Equipped with an onboard camera and the OV2640 image sensor, the ESP32-CAM supports video surveillance, real-time video feeds, and remote image capture Its flexibility in data communication and image processing positions it as an excellent choice for security systems, autonomous vehicles, and industrial monitoring applications.

Computer Vision and FPV (First-Person View):

The ESP32-CAM module enables real-time video transmission, revolutionizing IoT applications that depend on visual data This feature is vital for drones and surveillance cameras, enhancing situational awareness and informed decision-making With Wi-Fi integration, it facilitates remote monitoring, live streaming, and video analytics, offering unprecedented real-time insights.

FPV (First-Person View) technology enhances control experiences in drones and robotics by streaming real-time video from the device's camera to the operator, creating an immersive perspective This capability allows for precise navigation and control, making it feel as if the operator is physically present with the device The ESP32-CAM plays a crucial role in these applications by providing live video feeds over Wi-Fi, enabling effective interaction from a distance.

IoT and Wireless Communication

IoT Concept and Importance in FPV Car

The Internet of Things (IoT) is a network of interconnected physical devices equipped with sensors and software that enable data exchange over the Internet or intranet According to Atzori et al (2010), IoT systems utilize reliable communication protocols like UART, SPI, and I2C for efficient data transmission This technology facilitates remote monitoring and control of devices, fostering the development of smart environments where machines can function autonomously or respond to human commands.

In the FPV Car project, IoT plays a key role in:

• Control the vehicle remotely via a web interface using a WiFi network.

• Transmit live video (FPV) from the ESP32-CAM to the control unit.

• Send and receive control commands over a wireless network, eliminating the dependence on physical cables or traditional infrared controllers.

• Connect multiple control devices in parallel, allowing vehicle control from both web and hand gesture devices.

FPV Car is a testament to the applicability of IoT in robotic systems and autonomous vehicles, where embedded modules can communicate continuously to complete complex tasks.

IoT System Structure in FPV Car

FPV Car’s IoT system can be divided into three main layers, similar to standard IoT architectures:

This layer is responsible for collecting data from the environment and executing commands:

– Acts as the eye of the system, transmitting FPV (First Person View) images from the OV2640 camera.

– Built-in WiFi to transmit images directly to the web interface.

– Receives control signals from the browser and sends them to Arduino Uno via UART.

– Arduino Uno as the main microcontroller, processing signals from the ESP32-CAM and HC-SR04 sensors.

– Arduino Nano collects hand gesture data from the MPU6050 and sends control signals through NRF24L01.

• HC-SR04 (Ultrasonic sensor):Measures distances and avoids obstacles in autonomous mode.

• MPU6050 (Accelerometer and gyroscope):Collects hand gesture data, measuring tilt and angular speed to control vehicle direction.

This layer ensures data transfer between devices, creating an internal IoT network:

– Creates an internal WiFi network (Access Point – AP) or connects to an existing network to transmit FPV video and receive commands from the web interface.

– Communicates via HTTP protocol to control the vehicle.

The NRF24L01 is a 2.4GHz wireless module that enables seamless wireless signal transmission between Arduino Nano and Arduino Uno, making it ideal for applications like hand gesture control with ultra-low latency of less than 1ms This technology significantly enhances the capabilities of the Internet of Things (IoT), as highlighted by Nordic Semiconductor.

The NRF24L01, introduced in 2019, functions on the 2.4 GHz frequency band and provides a range of up to 100 meters in open areas Its low power consumption and excellent noise resistance make it an ideal choice for gesture-based control systems.

The Serial Peripheral Interface (SPI) is utilized for communication between the NRF24L01 and Arduino, operating on a Master-Slave principle where the Arduino serves as the master device controlling the NRF24L01, which acts as the slave This communication protocol relies on four primary wires.

* MOSI (Master Out Slave In) is used to transmit data from Master to Slave.

* MISO (Master In Slave Out) is used to transmit data from Slave to Master.

* SCK (Serial Clock) is the transmission speed control clock.

* CSN (Chip Select Not) is an active-LOW pin and is normally kept HIGH.

* Maximum up to 8 Mbps - suitalbe for applications that require fast signal transmission and low latency.

* Benefits of SPI and Multi-mode Control Car:

∙ High speed: Meets the requirements of gesture signal transmission with extremely low latency.

∙ Multi-device commnunication: Allows communication with multiple modules on the same SPI bus.

∙ Simplicity:Easy to program and deploy.

– UART (Universal Asynchronous Receiver/ Transmitter:is an asynchronous serial communica- tion protocol, used to communicate between ESP32-CAM and Arduino Uno.

∙ TX (Transmit):Data transmission pin.

∙ RX (Receive): Data reception pin.

∙ Bidirectional communication:Data can be transmitted in parallel in both directions (from ESP32-CAM to Arduino Uno and vice versa).

∙ ESP32-CAM sends HTTP commands to Arduino Uno via UART.

∙ Arduino Uno sends control feedback on motor or sensor status.

∙ Simple and easy to deploy.

∙ Stable in transmitting control commands.

– I2C (Inter-Integrated Circuit): I2C is a multi-device serial communication between MPU6050 and Arduino Nano to transmit sensor data.

∙ SDA (Serial Data): Data transmission line.

∙ SCL (Serial Clock): Clock signal transmission line.

* Operating mode: I2C allows multiple devices (Master-Slave) to connect on the same bus, reducing the number of wires required.

* Application in the system: Arduino Nano reads data from MPU-6050 via I2C The data is then transmitted to Arduino Uno via NRF24L01.

∙ Good scalability: Connect multiple sensors on the same bus.

∙ High transmission speed:Meets the need to read sensor data in real time.

∙ Save connection wires:Only 2 wires are needed to connect to multiple devices.

• Conclusion: The Network Layer plays an essential role in the Multi-mode Control Car system, helping hardware components communicate and operate synchronously.

– WiFi (ESP32-CAM):Transmit FPV video and receive control commands via the web.

– NRF24L01: Transmit remote hand gesture control signals.

– SPI, UART, I2C:Ensure fast, accurate and efficient data transmission between microcontrollers, sensors and communication modules.

The wireless network layer helps the system operate smoothly, enhancing the ability to monitor and control remotely in many different environments.

Application Layer – Control and Monitoring

This layer includes the following vehicle control software and interfaces:

– Users control the vehicle via a browser (Chrome, Firefox) by connecting to the ESP32-CAM’s WiFi network.

– The interface includes controls (forward, backward, turn left, turn right) and a live video stream from the camera.

– The user wears gloves with MPU6050, performs gestures (tilting the hand, rotating the wrist), and the data is transmitted wirelessly through NRF24L01 to Arduino Uno.

The ESP32-CAM plays a central role in establishing WiFi connections, transmitting video, and receiving com- mands from the web interface.

• ESP32-CAM Network AP or Station:

– In Access Point (AP) mode: ESP32-CAM creates its own WiFi network, and the user connects directly to it.

– In Station mode (STA): ESP32-CAM connects to an existing WiFi network, allowing remote control via LAN.

– Real-time image transmission up to 1600x1200 pixels.

– Easy integration with built-in libraries.

– Video delay can reach up to 200ms over long distances.

– Dependence on WiFi – loss of connection stops vehicle control.

NRF24L01 – Hand Gesture Signal Transmission

NRF24L01 is a 2.4GHz wireless signal transmission module, ideal for real-time gesture control.

– Arduino Nano sends MPU6050 control signals to Arduino Uno via NRF24L01.

– Transmission distance up to 100m in unobstructed conditions.

– Cannot transmit complex data (e.g., video).

Data Flow and Signals in Multi-mode Car System

• FPV Video Stream:ESP32-CAM→WiFi→Computer/phone (web browser).

• Web Control Command Flow:Browser→WiFi→ESP32-CAM→UART→Arduino Uno→L298N

• Gesture Control Flow: MPU6050 (Arduino Nano)→SPI (NRF24L01)→NRF24L01 (Arduino Uno)

• Autonomous Flow:HC-SR04 (scan by Servo SG90)→Arduino Uno→L298N→4 DC Motors.

Figure 2.1: Detailed flowchart of Multi-mode Remote Control Car system.

Figure 2.1 is a visual diagram showing the relationship between hardware, software, and data flow in the system.

The design structure of the multi-mode vehicle control system is crucial for enhancing performance and optimizing operations As shown in Figure 2.1, this comprehensive system structure facilitates improved data transmission and signal processing from sensors Utilizing a modular model allows for the seamless integration of additional Lidar sensors and advanced image processing algorithms, further boosting the vehicle's capabilities.

Related Works Analysis

The evolution of multi-mode remote control systems is heavily influenced by advancements in robotics and autonomous technologies, with Tesla Autopilot and DJI Robomaster serving as key examples This analysis compares these established systems with the proposed project, emphasizing their similarities, differences, and the unique contributions of this research.

Tesla Autopilot: Autonomous Driving System

Tesla Autopilot showcases advanced vehicular automation through its integration of cameras, radar, ultrasonic sensors, and sophisticated machine learning algorithms This technology is specifically engineered for efficient highway navigation, effective lane-keeping, adaptive cruise control, and reliable obstacle avoidance.

The Tesla Autopilot system exhibits significant similarities to multi-mode vehicle control research, as illustrated in Figure 2.2 Both systems utilize various sensors, such as ESP32-CAM, MPU6050, and HC-SR04, to perform diverse tasks and incorporate both autonomous and user-controlled modes Tesla Autopilot employs deep learning algorithms that enhance its functionality over time, paralleling the future integration of AI in this project However, its high cost and reliance on environmental conditions restrict its accessibility for educational use.

– Advanced Sensor Suite: Tesla’s use of multiple cameras, ultrasonic sensors, and radar enables a 360-degree awareness of the surroundings.

– Machine Learning Algorithms: Neural networks process massive amounts of real-world data, allowing Tesla vehicles to recognize complex scenarios like traffic patterns and pedestrians.

– Real-time Decision Making: The system can dynamically adjust speed and steering based on its environment, achieving a high degree of autonomy.

– Complexity and Cost: Tesla’s system requires expensive hardware and extensive computational power, making it inaccessible for educational or experimental purposes.

– Environment Dependency: Performance may degrade in adverse weather conditions (e.g., fog, rain) or poorly marked roads.

• Comparison to the Proposed System

The proposed multi-mode remote control car emphasizes affordability and modularity for academic and experimental applications, contrasting with the advanced capabilities of Tesla Autopilot This project utilizes HC-SR04 ultrasonic sensors for obstacle avoidance in its autonomous mode, offering a simpler yet effective solution tailored to its specific purpose.

DJI Robomaster: Educational Robot with FPV

The DJI Robomaster is a cutting-edge educational robot that combines FPV (First-Person View) technology with programmable features and AI modules It aims to facilitate hands-on learning in robotics and coding, making it an ideal tool for education.

The DJI Robomaster S1, depicted in Figure 2.3, showcases essential features like its FPV camera system, sensors, and AI capabilities, highlighting its potential in educational and research settings While it excels in first-person view (FPV) functionality and programmability, it primarily emphasizes manual control rather than full autonomy In comparison, the Multi-mode RC Car provides a more versatile solution by incorporating autonomous obstacle avoidance.

– FPV Integration: Equipped with a high-quality camera, it offers real-time video streaming for immersive control.

– Programmability: Users can program the robot using platforms like Scratch or Python, enabling a variety of applications.

– Modular Design:The system is easily expandable, allowing users to integrate additional compo- nents for advanced functionality.

– Cost Barrier:The Robomaster’s high cost limits accessibility for many users, especially in educa- tional contexts with budget constrains.

– Limited Autonomy: While it supports FPV and some autonomous functions, its focus remains on manual control and user programming rather than full-scale autonomy.

• Comparison to the Proposed System

The multi-mode remote control car features FPV streaming capabilities similar to the DJI Robomaster, utilizing ESP32-CAM technology It is designed for educational purposes and stands out with its emphasis on gesture control and autonomous navigation, providing enhanced control modes that surpass the primary functionalities of the Robomaster.

Position of the Proposed Research in the Context of Related Works

The proposed Multi-mode Remote Control Car occupies a unique position in the spectrum of related works:

This project offers an affordable and accessible solution by incorporating budget-friendly components like Arduino, ESP32-CAM, and ultrasonic sensors, making it a viable alternative to expensive systems such as Tesla Autopilot and DJI Robomaster.

• Multi-mode Operation: Unlike Tesla or DJI, this project combines web-based control, gesture-based nav- igation, and autonomous obstacle avoidance into a single system, demonstrating versatility and adaptabil- ity.

• Educational and Experimental Applications: The proposed system is tailored for academic and experi- mental purposes, enabling students and researchers to explore IoT, robotics, and control systems without prohibitive costs.

• Scalability and Modularity: The modular design allows future enhancements, such as integrating machine learning for improved autonomy or expanding sensory capabilities with advanced sensors like Lidar or

• The system is not designed for large-scale or real-world deployment, unlike Tesla Autopilot.

• The reliance on basic sensors (e.g., HC-SR04) restricts precision and environmental awareness compared to commercial systems.

The proposed research aims to connect high-end autonomous systems with cost-effective educational tools, offering a practical and scalable platform for experimenting with IoT and multi-mode control technologies Although it may not rival the complexity of Tesla Autopilot or the advanced features of DJI Robomaster, it plays a crucial role in making autonomous systems more accessible for educational purposes.

The Multi-mode RC Car, despite its smaller scale compared to larger commercial autonomous systems, presents significant potential for enhancing automation and remote control technologies By leveraging advanced technologies like Wi-Fi 6, LoRa, and optimized image processing algorithms, challenges such as image latency and connectivity limitations can be effectively resolved These improvements will not only enhance real-time control and monitoring capabilities but also pave the way for future applications in the field.

The Multi-mode RC Car stands out due to its integration of advanced technologies such as ultrasonic sensors, FPV cameras, and motion sensors, enabling a user-friendly autonomous system This project enhances learning from existing systems and encourages further research in autonomous vehicles and remote-controlled robotics With significant potential for growth and enhancement, the Multi-mode RC Car is poised to make meaningful contributions to practical applications while broadening technology access for a wider audience.

Control system design

Component Selection - Arduino Uno

The Arduino Uno is a widely used microcontroller board, renowned for its role in embedded and automation projects Its open-source ecosystem encourages innovation by allowing developers to create and share custom libraries With a user-friendly design, flexibility, and robust community support, the Arduino Uno is perfect for developing control systems that demand stability, scalability, and straightforward programming In the "Multi-mode RC Car" project, it serves as the central microcontroller, effectively coordinating and processing signals from various sensors, peripheral modules, and remote controls.

The Arduino Uno features the ATmega328P microcontroller, an 8-bit RISC chip running at 16 MHz, which allows for rapid and efficient task execution Its popularity in the Arduino community stems from its user-friendly design and compatibility with various hardware components With 32 KB of Flash memory, 2 KB of RAM, and 1 KB of EEPROM, the Arduino Uno is well-equipped to handle sensor signal processing, motor control, and communication with wireless modules.

Technical Specifications of Arduino Uno

The Arduino UNO is the ideal board for beginners in electronics and coding, offering a robust platform for first-time users As the most widely used and documented board in the Arduino family, it provides extensive resources for learning and experimentation.

The pinout diagram of the Arduino Uno, a widely used microcontroller in embedded systems and robotics, is depicted in Figure 2.4 This illustration details the I/O (Input/Output) ports and the specific functions of each pin, highlighting its versatility for various projects.

DC Current per I/O Pin 20 mA

DC Current for 3.3V Pin 50 mA

Flash Memory 32KB (ATmega328P), of which 0.5 KB used by bootloader

Table 2.1 outlines the technical specifications of the Arduino Uno, highlighting its importance in understanding the technology and related devices This table provides comparative insights that clarify the rationale behind selecting specific components The Arduino Uno is favored for its expandability, featuring both digital and analog I/O pins that enable connections to various peripheral devices, including sensors, motors, displays, and wireless communication modules This capability facilitates seamless integration into complex systems, exemplified by the autonomous vehicle control system used in the "Multi-mode RC Car" project Additionally, the Arduino Uno supports communication through SPI, I2C, and UART protocols, allowing connections to wireless modules like NRF24L01, cameras such as ESP32-CAM, ultrasonic sensors like HC-SR04, and motors.

PWM pins) These modules can communicate with Arduino Uno either directly or indirectly through these communication protocols, optimizing data transmission and precise control.

The Arduino Uno can be powered through its USB port or the Vin pin, with the USB providing a direct 5V supply from a computer, while the Vin pin accepts input power ranging from 7V to 12V for versatile power source options Furthermore, the onboard voltage regulator ensures a stable 5V output, effectively powering connected peripherals and modules.

The Arduino Uno features built-in fuse protection, which shields the board from overcurrent and overvoltage, making it essential for projects that demand high stability, like autonomous vehicle systems, where power-related challenges can arise.

Why Choose Arduino Uno for the ”Multi-mode RC Car” Project

The "Multi-mode RC Car" project utilizes an Arduino Uno as the main microcontroller, effectively managing signals from multiple sensors and modules such as the HC-SR04 ultrasonic sensor, NRF24L01 wireless module, and ESP32-CAM camera This setup enables the development of a robust, easily programmable, and integrable control system.

The Arduino Uno facilitates motor and servo control via PWM and digital pins, specifically using PWM pins D3, D5, D6, D9, D10, and D11 This setup allows for the adjustment of DC motor speeds and the angle of SG90 servos, ensuring precise control that significantly improves the mobility and maneuverability of the car.

• Communication with ESP32-CAMIn the ”Multi-mode RC Car” system, theESP32-CAMwill handle live video transmission over Wi-Fi Arduino Uno will use theUARTprotocol to communicate with the

ESP32-CAM, receiving and processing video signals for direct display on the control device The use of UART minimizes latency in signal transmission between devices.

The HC-SR04 ultrasonic sensor, when integrated with an Arduino Uno, effectively measures distance and detects obstacles By utilizing an obstacle avoidance algorithm, the system processes data from the sensor, allowing the vehicle to autonomously adjust its path This capability enables the car to accurately gauge distances to obstacles, ensuring efficient collision avoidance.

The NRF24L01 module enables wireless communication with Arduino Uno, allowing for remote control over distances of several hundred meters This versatile module supports various control features, including Web Control, Autonomous Mode, and Gesture Control, enhancing the overall functionality of your project.

Arduino Uno is the ideal choice for the ”Multi-mode RC Car” project due to its outstanding advantages:

• Affordable cost: Arduino Uno is reasonably priced, helping reduce overall project costs while ensuring effectiveness.

• Stability: The use of the ATmega328P microcontroller ensures stable operation in projects requiring high reliability.

The system offers flexible expandability through its I/O pins and a variety of communication protocols, including UART, SPI, and I2C, enabling seamless connection with multiple expansion modules for future development.

• Compatibility with Arduino IDE: The Arduino IDE programming environment makes it easy for de- velopers to write and upload code to the microcontroller while supporting many libraries and resources.

The Arduino Uno is an excellent choice for the control system in the "Multi-mode RC Car" project due to its user-friendly programming, stability, and strong expandability Its flexibility allows for seamless integration of various modules like sensors, motors, and cameras, while also optimizing both cost and system performance With these significant benefits, the Arduino Uno proves to be a dependable and efficient option for our project.

ESP32-CAM Module (OV2640 Camera)

The ESP32-CAM is a versatile development module featuring the ESP32 microcontroller and an OV2640 camera, designed for applications like FPV, remote monitoring, and IoT Its dual-core processor, along with integrated Wi-Fi and Bluetooth, allows for real-time data processing and transmission, as highlighted by Zhang et al (2020) With the capability to stream live video over Wi-Fi, the ESP32-CAM is ideal for projects that necessitate remote observation and control, exemplified by the "Multi-mode RC Car" project.

The ESP32-CAM module is a compact and powerful device featuring the ESP32 microcontroller, which is a dual-core processor with a Tensilica LX6 32-bit architecture, operating at speeds up to 240 MHz This robust processing capability makes it ideal for various IoT applications Additionally, the module supports both Wi-Fi and Bluetooth connectivity and is equipped with an OV2640 camera for transmitting video and images.

Technical Specifications of ESP32-CAM:

• Microcontroller:ESP32 (dual-core Tensilica LX6, 32-bit, 240 MHz clock speed)

– Flash Memory:4MB (or 8MB, depending on the module variant)

• Camera: OV2640 (2 MP, resolution up to 1600 x 1200 pixels)

• Wi-Fi:IEEE 802.11 b/g/n, with Wi-Fi Direct and Access Point Mode

• Bluetooth:Bluetooth v4.2 BR/EDR and BLE

– GPIOs: 9 configurable GPIO pins for various purposes

– UART: 2 UART ports for communication with other devices

– SPI: SPI communication for connecting peripheral modules

– I2C: I2C communication support for sensors and other devices

• Power Supply:5V (can be supplied via the 5V pin or micro-USB port)

– Analog Input: 1 ADC pin (ADC1)

• Video Resolution:640x480 (VGA) or 1600x1200 (UXGA), depending on the configuration

The ESP32-CAM has a simple pinout structure with pins that can be flexibly configured for various purposes. Below is a detailed analysis of the ESP32-CAM module pinout.

To utilize the ESP32-CAM module, ensure that the IO0 pin is connected to GND during the boot process to enter programming mode Once programming is complete, you can disconnect the IO0 pin to restore its normal GPIO functions.

5V Power supply for the module (5V DC)

GPIO0 GPIO pin (can be used for GPIO, UART, I2C functions)

GPIO1 GPIO pin (TXD for UART)

GPIO3 GPIO pin (RXD for UART)

GPIO12 GPIO pin (can be used for PWM or ADC)

GPIO13 GPIO pin (RXD or PWM)

GPIO14 GPIO pin (PWM or ADC)

GPIO15 GPIO pin (PWM or ADC)

GPIO16 UART U2RXD receive pin

GPIO4 GPIO pin (PWM or Flash)

3.3V Provides 3.3V power for other modules and sensors

Figure 2.5 presents the connection diagram for the multi-mode control system, showcasing the electrical and physical interfaces among various devices, including sensors, communication modules, and controllers Complementing this, Table 2.2 outlines the specifications and functions of these components, detailing the devices illustrated in Figure 2.5.

Application in ”Multi-mode RC Car” Project

The "Multi-mode RC Car" project utilizes the ESP32-CAM for live video streaming via Wi-Fi, enabling an immersive FPV (First Person View) experience The ESP32-CAM is the perfect choice for this application due to its compact design, built-in camera, and reliable connectivity, making it an ideal module for enhancing remote control car experiences.

The ESP32-CAM offers Wi-Fi video streaming capabilities with resolutions reaching up to VGA (640x480) and UXGA (1600x1200), allowing for live video transmission to the controller's screen This feature enables operators to effectively monitor the vehicle's surroundings while minimizing transmission latency for stable video signals during operation.

The ESP32-CAM enables remote connection and control through Wi-Fi Direct and Access Point Mode, facilitating direct video transmission to devices like smartphones and computers This capability is particularly beneficial for applications in FPV (First Person View) and remote surveillance.

The ESP32-CAM enables wireless communication with a vehicle's control systems through Wi-Fi, seamlessly integrating with modules like the Arduino Uno for processing and control via UART or SPI protocols This wireless approach simplifies the complexity of wired connections, resulting in a flexible and easily deployable control system.

The ESP32-CAM enables live video streaming from the car's perspective, supporting various control modes including Web Control, Autonomous Mode, and Gesture Control Users can remotely manage the vehicle through a web interface or mobile app while accessing real-time video feeds.

• Cost-effective:The ESP32-CAM is relatively affordable compared to other video streaming modules.

• Strong processing power:The dual-core ESP32 processor and large memory allow for quick processing of video signals and other tasks during operation.

• Built-in OV2640 camera:This feature makes it convenient to use and saves space in the design.

• Limited GPIO pins: The ESP32-CAM only provides 9 GPIO pins, which could be a limitation when more pins are needed for external devices.

• 5V power supply: Care should be taken when powering the module, as some peripheral modules may require different voltages (e.g., 3.3V).

The ESP32-CAM is perfect for Wi-Fi video streaming projects, especially in FPV and remote monitoring applications such as the "Multi-mode RC Car." Equipped with a built-in camera, Wi-Fi, Bluetooth connectivity, and a robust microcontroller, the ESP32-CAM significantly enhances the vehicle's control system by delivering video signals, thereby improving the overall remote control and monitoring experience.

Arduino Nano

The Arduino Nano is a compact and versatile development board, perfect for projects with limited space and the need for flexible expansion According to Margolis (2011), its small size makes it particularly suitable for applications such as gesture control Featuring the ATmega328P microcontroller, the Arduino Nano shares many similarities with the Arduino Uno, making it an excellent choice for robotics, remote-controlled vehicles, and IoT projects.

The Arduino Nano, powered by the ATmega328P microcontroller, operates on 5V systems (and 3.3V in certain variants) It offers easy programming through the Arduino IDE and supports various sensors and modules Its compact design makes the Arduino Nano ideal for applications needing control and monitoring in limited spaces.

• Microcontroller: ATmega328P (or ATmega328, depending on the variant)

• I/O Pins: 14 GPIO pins (6 PWM pins)

• Flash Memory: 32 KB (2 KB used for bootloader)

• Operating Voltage: 5V or 3.3V (depending on version)

• USB Connection: Mini-USB (or micro-USB, depending on version)

• Power Supply: 5V via USB or external power (7V to 12V)

• Operating Modes: Programming and execution mode

– SPI: Pin 11 (MOSI), 12 (MISO), 13 (SCK)

• Analog Input Pins (ADC): 8 ADC pins (0-5V, 10-bit resolution)

• PWM Pins: PWM control on 6 pins (D3, D5, D6, D9, D10, D11)

Arduino Nano provides numerous flexible connections for interfacing with modules and sensors The de- tailed pinout is as follows:

D0 (RX) Receive signal (UART) D1 (TX) Transmit signal (UART) D2-D13 GPIO pins (PWM on pins 3, 5, 6, 9, 10, 11) A0-A7 Analog pins (ADC, used for sensor input)

VCC Power supply pin (5V or 3.3V) RAW External power input (7V to 12V)

Table 2.3: Pinout of Arduino Nano

Figure 2.6 illustrates the pin locations on the Arduino Nano, while Table 2.3 details the functions and applications of each pin Together, these resources enhance understanding of the pins' usage, ensuring accurate connections and programming for effective multi-mode control systems.

The Arduino Nano offers exceptional flexibility for interfacing with various sensors and peripheral modules, thanks to its abundant GPIO pins and PWM control features This versatility makes it an excellent choice for managing devices such as servos and LEDs, as well as a wide range of other applications.

Application in ”Multi-mode RC Car” Project

In the”Multi-mode RC Car”project, theArduino Nanowill play a key role in gesture control using the

The MPU6050 sensor, which combines a 6-axis accelerometer and gyroscope, is effectively paired with the compact Arduino Nano This combination offers the necessary processing power to accurately interpret and transmit control signals based on user gestures, making it an excellent choice for gesture-based applications.

The Arduino Nano utilizes the I2C protocol to receive data from the MPU6050 sensor, which measures hand movements such as tilting and rotating This data is processed by the Arduino Nano to control a car's movements, including moving forward, backward, and turning left or right Its compact design allows for efficient space optimization and seamless integration into small control systems.

In gesture control mode, the Arduino Nano receives signals from the MPU6050 to identify hand gestures, such as tilting and rotating By processing data from the accelerometer and gyroscope, the Arduino Nano transmits these control signals to the Arduino Uno using the NRF24L01 module.

• Arduino Nano receives signals from the MPU6050 via the I2C protocol (pins A4 and A5).

• Hand movement data is processed on Arduino Nano, recognizing gestures such as tilting or rotating the hand.

• After processing, Arduino Nano uses the NRF24L01 module to send the control signals to the Arduino Uno wirelessly.

The Arduino Uno decodes signals received from the NRF24L01 and controls the car's motors and hardware components, such as servos and DC motors, according to the instructions sent by the Arduino Nano.

The NRF24L01 module enables wireless communication between Arduino Nano and Arduino Uno, allowing data transmission and reception over short to medium distances This capability is ideal for connecting devices within confined spaces, such as in a car control environment.

The NRF24L01 module communicates using the SPI (Serial Peripheral Interface) protocol, connecting to Arduino boards through the MISO, MOSI, SCK, CSN (D9), and CE (D10) pins The CE and CSN pins are crucial for managing the transmission and reception of signals between the Arduino and the NRF24L01 module.

The Arduino Uno processes signals from the Arduino Nano to control the car's motors via PWM pins (D2, D3, D4, D5, D6, D7), enabling the vehicle to move forward, backward, or turn in response to the controller's gestures.

• Compact size: With dimensions of just 45 x 18 mm, Arduino Nano is ideal for projects with space constraints.

• High compatibility: Arduino Nano can easily integrate with sensors, modules, and peripherals thanks to its GPIO, PWM pins, and I2C protocol.

• Affordable cost: Arduino Nano is low-cost, making it an economical choice for small to medium-sized projects.

• Easy programming: Arduino Nano uses the Arduino IDE platform, making software development and programming straightforward.

• Limited GPIO pins: Arduino Nano provides only 14 GPIO pins, which may be insufficient for more complex projects.

• No integrated Ethernet or Wi-Fi support: Arduino Nano does not support direct wireless connections, requiring external modules like ESP8266 or ESP32 for Wi-Fi connectivity.

The Arduino Nano is ideal for gesture control in the "Multi-mode RC Car" project due to its powerful processing capabilities, compact design, and efficient space utilization, which ensure stable performance Its I2C and UART communication allows for seamless integration with other control systems, such as the Arduino Uno, and sensors like the MPU6050, facilitating advanced functions like gesture recognition and motor control.

L298N: DC Motor

Reason for using L298N in the system

The L298N motor driver is selected for this vehicle control system due to its efficient control of both DC motors and stepper motors As an H-Bridge motor driver, the L298N offers precise control over motor rotation direction and can simultaneously manage two DC motors or one stepper motor, providing versatility for vehicle motion control.

The L298N is a dual-channel H-Bridge motor driver crucial for controlling DC and stepper motors, enabling bidirectional motor control in robotics (Rashid, 2017) With a capacity of up to 2A per channel, it is ideal for high-power applications Additionally, the L298N utilizes separate power sources for the motor and controller, which alleviates the microcontroller's load and enhances overall performance.

– DC Motor: 4.5V to 36V (depending on the motor type).

– Controller: 5V (can be sourced from Arduino).

• Logic voltage:5V (compatible with microcontrollers like Arduino).

• Power consumption:Varies depending on load, typically around 1.5W to 3W during operation.

• Durability:Operating temperature range from -25°C to +130°C, with a maximum temperature of 150°C when no load is applied.

The L298N has 8 output pins, each controlling one motor channel Below is the detailed information about the connection pins:

The L298N serves as the central motor control module in a multi-mode control system, playing a crucial role in enhancing the project's overall performance However, optimizing the design or upgrading components can address performance and size limitations.

• IN1: Input control for motor A (clockwise/counterclockwise).

• IN2: Input control for motor A (counterclockwise/clockwise).

• OUT1: Output for motor A (provides current to motor A).

• OUT2: Output for motor A (provides current to motor A).

• IN3: Input control for motor B (clockwise/counterclockwise).

• IN4: Input control for motor B (counterclockwise/clockwise).

• OUT3: Output for motor B (provides current to motor B).

• OUT4: Output for motor B (provides current to motor B).

• ENA: PWM output for motor A (motor A speed).

• ENB: PWM output for motor B (motor B speed).

• Vcc: Power supply for the controller (typically 5V from Arduino).

• Vin: Power supply for the motor (from 4.5V to 36V, depending on the motor).

The DC motor operates via Vsand and is controlled by the L298N module, allowing the Arduino to independently adjust the speed and rotation direction of each motor for flexible and precise movement The L298N modifies the motor's rotation direction by altering the logic levels (HIGH/LOW) on the IN1, IN2 (or IN3, IN4) pins.

• IN1 = HIGH, IN2 = LOW → The motor rotates clockwise

• IN1 = LOW, IN2 = HIGH → The motor rotates counterclockwise.

The L298N motor driver regulates motor speed by utilizing PWM signals on the ENA and ENB pins, allowing for precise control of rotation speed Additionally, it features a built-in diode that safeguards against overcurrent, ensuring the motor operates safely and efficiently.

The L298N motor driver effectively manages two DC motors in a vehicle system by utilizing PWM signals from an Arduino Uno or Nano through the IN1, IN2, IN3, and IN4 pins This setup allows for precise control over the motors' rotation direction and speed, enabling movement through various modes such as remote control via the web, gesture control, or autonomous operation Additionally, the L298N regulates the voltage supplied to the motors, ensuring stable speed and pulling force while preventing overload.

The L298N motor driver is perfect for robotic systems and autonomous vehicles, offering high current capabilities and precise control over DC and stepper motors By integrating the L298N driver, users can significantly enhance vehicle movement control, especially when managing PWM signals from an Arduino Uno to adjust motor speed and direction.

MPU-6050: Specifications and Applications

Reason for Using MPU-6050 in the System

The MPU-6050 is a crucial 6-axis sensor module that combines an accelerometer and a gyroscope, enabling gesture control in vehicles It measures both linear motion and rotational angles, allowing it to detect user gestures like hand tilting or rotation This data is then transmitted to the Arduino Nano, which controls the vehicle's actions based on these gestures.

The MPU-6050 is a widely utilized 3-axis accelerometer and 3-axis gyroscope sensor developed by InvenSense, now part of TDK Corporation With its 16-bit resolution and I2C communication protocol, it offers precise motion detection, making it a popular choice for IoT and robotics projects, particularly in gesture recognition applications.

The motion sensor not only gathers environmental data but also features specialized communication pins that enhance transmission speed As illustrated in Figure 2.8, the module's power supply and I2C communication pins are efficiently organized, facilitating seamless connections with microcontrollers and other components.

• VCC: Provides power to the sensor, from 3.3V to 5V.

• SCL: Serial Clock Line for I2C communication.

• SDA: Serial Data Line for I2C communication.

• AD0: I2C address pin Can be connected to GND for the default address 0x68 or VCC for address 0x69.

• INT: Interrupt pin used for signaling events such as sensor value changes.

– Measures acceleration from -2g to +2g, -4g to +4g, -8g to +8g, or -16g to +16g (adjustable depending on the application).

– Measures rotational speed from -250°/s to +250°/s, -500°/s to +500°/s, -1000°/s to +1000°/s, or -2000°/s to +2000°/s (adjustable depending on the application).

– I2C: Data transmission via the I2C protocol, easy integration and I/O pin-saving.

– Power consumption: Approximately 3.9mA during operation.

– Sleep mode for power saving when the sensor is not in use.

– Integrated signal conditioning (low-pass filter) to reduce noise.

The MPU-6050 sensor captures signals from the Arduino Nano to detect user movements and gestures, such as hand tilting or rotation This processed data is then wirelessly transmitted to the Arduino Uno using the NRF24L01 module, enabling precise control of the vehicle's motion By providing detailed movement data, the MPU-6050 aids the Arduino Nano in recognizing and analyzing the user's hand gestures effectively.

Gesture control utilizes signal processing algorithms to eliminate noise and precisely identify user gestures The MPU-6050 sensor transmits data through the NRF24L01 module to the Arduino Uno, which then manages the vehicle's motors via the L298N motor driver.

The MPU-6050 sensor plays a vital role in motion detection for gesture control, offering high precision and resolution Its compatibility with the I2C protocol allows for seamless integration with microcontrollers such as the Arduino Nano, making it an excellent option for gesture recognition and control in robotics and autonomous vehicle systems.

NRF24L01: Specifications and Applications

Reason for Using NRF24L01 in the System

The NRF24L01 is a versatile 2.4GHz wireless communication module commonly utilized in IoT applications and robotics for short-to-medium range data transmission In autonomous vehicle control systems, it facilitates wireless communication between the Arduino Nano, which handles gesture control, and the Arduino Uno responsible for motor control, enabling the effective transmission of sensor data from the MPU-6050 along with control commands.

The NRF24L01 is a 2.4GHz transceiver module by Nordic Semiconductor, optimized for data transmission and reception via the SPI (Serial Peripheral Interface) protocol, ensuring fast and reliable communication between microcontrollers This low-power wireless communication solution is easily integrable with popular microcontrollers such as Arduino and ESP32.

Effective wireless communication in complex embedded systems relies on the precise interaction of data and control pins The communication module, as illustrated in Figure 2.9, features well-designed pins like CE, CSN, and SPI, which facilitate rapid and stable signal transmission in real-world operating conditions.

• VCC: Power supply for the module Connect to a 3.3V source.

• CE: Chip Enable pin When the CE signal is high, the module will operate (if CE is low, the module will be in sleep mode).

• CSN: Chip Select Not This pin is used to select the device for SPI communication.

• SCK: Serial Clock Used to synchronize the clock signal between the module and the microcontroller.

• MOSI: Master Out Slave In Used to transmit data from the microcontroller to the module.

• MISO: Master In Slave Out Used to receive data from the module back to the microcontroller.

• IRQ: Interrupt Request Used to notify the microcontroller when an event occurs, such as receiving data.

• Operating Frequency: 2.4 GHz ISM band (Industrial, Scientific, and Medical band), widely used in wireless devices.

– The module can achieve a range of up to 100m in an unobstructed environment, depending on surrounding conditions and module configuration.

– The practical range typically varies from 20m to 100m.

– Data can be transmitted at speeds up to 2 Mbps.

– Supports automatic error checking (CRC) and automatic retransmission in case of errors.

– Designed for use with a 3.3V supply, so a stable 3.3V power source is required.

– Low power consumption, about 12mA during transmission and lower in standby or sleep modes.

– Communicates with microcontrollers via SPI (Serial Peripheral Interface).

– Main communication pins: MISO, MOSI, SCK, CSN.

– Auxiliary pins: CE (Chip Enable), IRQ (Interrupt Request).

• Transmission Channels: Supports 125 channels in the frequency range from 2.400GHz to 2.525GHz, helping reduce interference when many devices are present in the area.

– Transmission mode:1-to-1 (Point-to-Point) or 1-to-many (Point-to-MultiPoint).

– Automatic Retransmission: In case of transmission errors, the module automatically retransmits without user intervention.

– Transmit power can be adjusted with three levels: -18 dBm, -12 dBm, and 0 dBm, allowing for power savings or increased transmission range as needed.

• The NRF24L01 creates a wireless communication network between microcontrollers in the system.

• The Arduino Nano, connected to the MPU-6050(gesture sensor), will send data to the Arduino Uno via the NRF24L01.

• In this case, the Arduino Uno will control the vehicle’s motors based on the gesture data received from the Arduino Nano.

• The NRF24L01 enables data transmission in a wireless environment, reducing the complexity of the wiring system.

The NRF24L01 is a cost-effective wireless communication module known for its high data transmission speed and reliable performance, making it perfect for applications such as remote control and autonomous vehicle systems It effectively transmits data from gesture sensors to microcontrollers, and its compatibility with the SPI protocol allows for easy integration with platforms like Arduino, offering a robust wireless communication solution.

HC-SR04: Ultrasonic Sensor

Reason for Using HC-SR04 in the System

The HC-SR04 ultrasonic sensor is a popular choice for distance measurement, utilizing ultrasonic waves to detect objects and provide precise positioning Its time-of-flight principle enables accurate measurements ranging from 2cm to 4m, making it ideal for applications in obstacle detection, such as in robotics and autonomous vehicles.

RC Car” project, the HC-SR04 will help the car detect and avoid obstacles, supporting the Autonomous Mode and optimizing the car’s movement.

Detailed Analysis of HC-SR04

The HC-SR04 is a user-friendly ultrasonic sensor module that works seamlessly with Arduino and various microcontrollers It features a transmitter and a receiver that utilize ultrasonic waves to accurately measure distances to objects.

The accuracy of an ultrasonic sensor in measuring distance relies on the coordination between the Trig and Echo pins in transmitting and receiving sound wave signals As illustrated in Figure 2.10, the sensor's pinout diagram features a straightforward design that facilitates easy implementation and integration into control systems.

• VCC:Provides power to the sensor (5V).

• Trig: Trigger pin When it receives a high logic signal (5V), the sensor will start emitting ultrasonic waves.

• Echo: Echo pin The sensor sends a high logic signal when it receives the reflected wave, and this time is measured to calculate the distance.

– 5V (or 3.3V, but accuracy may decrease when using lower voltages).

– The sensor consumes about 15mA when operating.

– 40 kHz (the frequency of the emitted ultrasonic waves).

– The measuring range of the HC-SR04 is from 2cm to 4m (the maximum range may decrease in noisy environments or materials that absorb ultrasonic waves).

– The sensor’s accuracy is about 3mm.

– Fast response time with 1ms for each measurement.

* Trigger: This pin is used to activate the sensor.

* Echo: This pin receives the returned signal after the ultrasonic wave has traveled to and re- flected from an object.

– When a signal is sent from the Trigger pin (activation), the HC-SR04 sensor emits ultrasonic waves and then receives the returned signal at the Echo pin.

– Based on the time difference between when the wave is emitted and when it is received, the sensor calculates the distance to the object.

– Simple and easy to usewith microcontrollers like Arduino.

– Cost-effective:A low-cost ultrasonic sensor but effective in measuring distance.

– Detection Range: Can detect small or hard-to-detect objects in environments with strong light(unlike infrared sensors).

• The HC-SR04 sensor will be used to detect obstacles and measure the distance between the car and objects in the environment.

• When the distance to an object becomes smaller than a certain threshold, the car will change its direction or stop.

• The SG90 Servo will sweep 180 degrees to increase the ability to detect obstacles at different angles, helping to extend the sensor’s range of view.

The HC-SR04 sensor is an ideal solution for obstacle detection and distance measurement in autonomous vehicle projects due to its affordability, user-friendly design, and high effectiveness in short-range applications This sensor enhances the vehicle's ability to avoid obstacles, contributing to a more intelligent control environment When paired with modules such as the Servo SG90, the HC-SR04 enables the autonomous car to navigate obstacles with precision and efficiency in Autonomous Mode.

Servo SG90: Micro Servo Motor

Reason for Using Servo SG90 in the System

The SG90 servo motor is a compact and widely used component in robotics and remote control applications, known for its precise movement capabilities According to Futaba Corporation (2018), its PWM control enables accurate angle adjustments In the "Multi-mode RC Car" project, the SG90 facilitates a 180-degree scanning mechanism for the HC-SR04 ultrasonic sensor, significantly improving obstacle detection This precision in rotation allows the autonomous vehicle to "see" from various angles, enhancing its functionality.

Detailed Analysis of Servo SG90

The SG90 is a small DC servo motor suitable for applications requiring limited space and precise movement.

It can rotate up to 180 degrees, making it ideal for controlling the scanning of the HC-SR04 ultrasonic sensor to gather data about surrounding obstacles [26]

A compact yet highly efficient servo motor is demonstrated by its reasonable signal pin layout and power supply.

The motor, equipped with just three basic pins, demonstrates precise rotation in response to control signals, significantly enhancing the flexible mobility of the entire system.

– Receives PWM signals from the microcontroller.

– Typically connected to a PWM pin on the Arduino.

– Provides power to the servo (5V from Arduino or external power source).

– Ground pin connected to the system ground (Arduino or external source).

– Maximum current consumption is approximately 500mA under load.

– Rotation time from 0 to 180 degrees: 0.1s to 0.2s (depending on the load).

– Maximum torque:1.8 kg∙cm at 4.8V.

– Maximum torque:2.5 kg∙cm at 6V.

– Sufficient torque to control lightweight parts or sensors.

– Controlled via PWM (Pulse Width Modulation) with a pulse frequency of 50 Hz.

– Pulse duration ranges from 1ms to 2ms.

– Approximately 9g, making it lightweight and suitable for compact applications.

– 22.2 x 11.8 x 31 mm, compact and easy to integrate into small systems.

– Easy to Control:Simple PWM signals allow direct control from microcontrollers like Arduino.

– Compact Design:Small size saves space in robots or small models.

– Affordable: One of the most cost-effective servos, providing high performance for hobby and research applications.

• The SG90 servo motor will control the scanning mechanism of the HC-SR04 ultrasonic sensor.

• This scanning mechanism enhances obstacle detection at various angles around the car, especially during Autonomous Mode operation.

• The servo can also be used to control mechanical parts like steering wheels or other actuators in the system.

The SG90 servo motor is perfect for this project due to its precise 180-degree angle control, enabling the autonomous vehicle to effectively detect and avoid obstacles Its compact design, easy PWM control, and low power consumption contribute to space and energy efficiency while maintaining high operational performance.

DC Motor: Theory and Application in Multi-mode RC Car

Reason for Using DC Motors in the System

DC motors are favored in remote-controlled vehicle projects for their simplicity, efficiency, and control ease As noted by Hughes and Drury (2013), brushed DC motors are economical and ideal for small-scale applications In the "Multi-mode RC Car" project, the DC motor acts as the main driving force, allowing the car to move flexibly and respond swiftly to control signals.

DC motors are typically divided into two main types:

The brushed DC motor is the most prevalent type of DC motor, known for its straightforward design and affordability However, its brushes make direct contact with the rotating components, leading to wear over time and necessitating maintenance after extended use.

• Brushless DC Motor (BLDC):These motors offer higher efficiency and durability as they do not have brushes However, they are more expensive and require complex control circuitry.

In this project, the selected motor is abrushed DC motordue to its low cost, ease of control, and suitability for small applications like remote-controlled cars.

Specifications of 6V 200RPM DC Motor

A DC motor, as illustrated in Figure 2.12, features a straightforward design with two primary connections, allowing for versatile control over both speed and rotation direction This diagram highlights the direct conversion of the control signal into driving force through current, ensuring optimal performance in real-world applications.

• Motor Shaft Diameter:5mm (compatible with common wheels)

• Motor Weight:Approximately 50 - 80g (depending on type)

• Average Lifespan: 1000 - 2000 continuous operating hours

The DC motor has two primary connection terminals:

• Terminal 1 (Motor +): This is the positive terminal, receiving positive voltage from the controller or H-Bridge circuit.

• Terminal 2 (Motor -):This terminal is grounded or reverses the voltage to rotate the motor in the opposite direction.

To adjust motor speed, Pulse Width Modulation (PWM) is applied PWM allows modification of the average voltage supplied to the motor, thereby controlling the rotational speed.

H-Bridge Circuit - DC Motor Direction Control

The H-Bridge circuit is essential for controlling the direction of a DC motor by reversing its current flow Its name derives from its H-shaped structure, with the DC motor positioned at the center In this project, the L298N IC is utilized to independently control two motors.

The H-Bridge circuit is composed of four switches, usually MOSFETs or transistors, organized into two parallel branches By selectively opening and closing these switches, the circuit regulates the current flow through the motor, which in turn controls the motor's rotation direction The primary operating states of the H-Bridge circuit are essential for its functionality.

• Forward Rotation: Switches S1 and S4 are closed, while S2 and S3 remain open Current flows from the positive terminal, through S1, the motor, and S4, and then returns to the negative terminal.

• Reverse Rotation: Switches S2 and S3 are closed, while S1 and S4 remain open Current flows from the positive terminal, through S3, the motor, and S2, and then returns to the negative terminal.

• Motor Stop: All switches are open, preventing current from flowing through the motor.

Motor braking occurs when two switches on the same circuit, such as S1 and S2 or S3 and S4, are closed at the same time This action creates a short circuit across the motor, which generates resistance and facilitates a quicker stop of the motor.

• Number of Channels:2 (capable of controlling two motors)

• Control Mode:PWM control to adjust motor speed

Application of DC Motors in Multi-mode RC Car

• Wheel Drive System:Two DC motors are mounted on the rear wheels to provide the main driving force.

• Steering and Direction Mechanism: A small motor or servo is responsible for controlling the front wheels’ direction.

• Autonomous Mode:The DC motors work in combination with the HC-SR04 ultrasonic sensor to avoid obstacles in autonomous mode.

Comparison of DC Motors with Other Motor Types

Brushed DC Motor Easy to control, low cost Wears quickly, lower efficiency

Brushless DC Motor High efficiency, durable Expensive, complex control

Stepper Motor Precise positioning, small steps Low speed, complex design

Servo Motor Precise angle control Low torque, limited to 180°

Table 2.4: Comparison of DC Motors and Other Motor Types

Table 2.4 outlines various motor types, such as Brushed DC Motors, Brushless DC Motors, Stepper Motors, and Servo Motors Each type is assessed based on its advantages and disadvantages, offering readers insights into the unique features and limitations associated with each motor category.

Table 2.4 offers a comprehensive comparison of motor types, aiding users in selecting the most suitable option for their project needs For applications demanding precision at a reasonable cost, stepper or servo motors are recommended Conversely, brushed DC motors serve as a cost-effective solution for simpler requirements However, for projects prioritizing durability and performance, brushless DC motors emerge as the preferred choice, despite their higher price.

DC motors are an excellent option for the Multi-mode RC Car project because of their simplicity, affordability, and ease of integration These motors are versatile and can be used in various applications such as RC cars, drones, electric vehicles, and 3D printers When paired with the L298N H-Bridge circuit, they enable precise control over speed and direction, meeting the operational needs for both remote control and autonomous driving modes.

Project Implementation

Introduction to Theoretical Modeling

Kinematic and Dynamic Modeling of the Vehicle

H-Brigde Control for Motor Direction

Sensor Data Processing and Sensor Fusion

Wireless Communication and Control System

Conclusion of Theoretical Design

Conclusion

Limitations

Further Works

Conclusion

Recommendations