if you in college graduate or pursuing CSE or automobile or robotics etc....; definitely interested in doing some projects. but you dont know about tools and AI models ways to implement ways to deploy some IOT or ARDUINO & Nvidia Jetson Nano & Other Developer Kits. this blog especially that kind of persons.
I think your already see this autonomous car in Amazon or other online shopping websites....!
because companies built in inside the 2D Lidars and Stereo Cameras or etc..... these sensors are Huge price; especially Lidar👀
I will mention some companies to know the price of these CARS
NOTE: Clearly read documentation (Because So many live tutorials are given; you will learn more Real-world Knowledge)
IF YOU WANT TO LEARN ARDUINO & Nvidia Jetson Nano & Raspberry Pi
for FREE: Link (Complete Great stuff)
The Main parts of these cars are ARDUINO & Nvidia Jetson Nano & Raspberry Pi
so I'll explain deeply; also given great links to Learn more
ARDUINO
Arduino is a hardware and open-source software platform that is widely used for creating projects and prototypes in the field of electronics and embedded systems. It consists of a range of microcontroller boards with different capabilities and features. Arduino boards are beginner-friendly and are often used for DIY projects, very small robotics projects, home automation, and various sensor-based applications. They have a large community of users and plenty of libraries and resources available for easy development.
Nvidia Jetson Nano
Nvidia Jetson Nano is a single-board computer designed for AI and deep learning applications. It features a powerful Nvidia GPU, multiple CPU cores, and a variety of I/O interfaces. The Jetson Nano is specifically designed for running complex AI algorithms and computer vision tasks in edge computing scenarios. It is widely used in autonomous vehicles, robotics, surveillance systems, and smart cameras. The Jetson Nano offers significant computational capabilities, making it suitable for real-time AI inference tasks.
The Nvidia Jetson Nano is a popular single-board computer (SBC) developed by Nvidia, primarily designed for AI and robotics projects. Here's a brief history of the Nvidia Jetson Nano:
Announcement (March 2019): Nvidia announced the Jetson Nano at the 2019 Nvidia GPU Technology Conference. It was introduced as an affordable and power-efficient SBC for AI development, targeting hobbyists, students, and developers.
Release (March 2019): The Jetson Nano Developer Kit was made available to the public shortly after its announcement. It featured a quad-core ARM Cortex-A57 CPU and a Maxwell-based Nvidia GPU with 128 CUDA cores.
First Iteration: The initial version of the Jetson Nano had 4 GB of LPDDR4 RAM, a microSD card slot for storage, and GPIO pins for hardware interfacing. It ran Nvidia's JetPack SDK, which included support for popular deep learning frameworks like TensorFlow and PyTorch.
AI and Robotics Projects (2019-2020): The Jetson Nano gained popularity in the AI and robotics communities for its capabilities in running deep learning models at an affordable price point. It was used in various projects, including autonomous drones, home automation, and object recognition systems.
Jetson Nano 2GB (October 2020): In October 2020, Nvidia released a more cost-effective version of the Jetson Nano called the "Jetson Nano 2GB." This version had 2 GB of RAM, making it even more budget-friendly.
Jetson Nano 2GB (2021): The Jetson Nano 2GB continued to be popular among educators and students for teaching AI and robotics concepts. It offered a lower-cost entry point for learning and experimentation.
Community and Development (Ongoing): The Nvidia Jetson Nano community continued to grow, with developers creating and sharing a wide range of projects and tutorials. Nvidia provided regular updates and support, making it an accessible platform for AI development.
NOTE: ALMOST ALL PEOPLE in 5 YEARS SWITCH Rasberry PI TO Nvidia Jetson Nano
Raspberry Pi
it is a Linux machine
Raspberry Pi Documentation: Link
Raspberry Pi camera modules: Link The history of the Raspberry Pi begins with a group of engineers and computer scientists at the University of Cambridge's Computer Laboratory in the United Kingdom. The primary aim of the project was to promote computer science education and provide an affordable platform for students to learn programming and electronics. The development of the Raspberry Pi started in the mid-2000s and culminated in the release of the first Raspberry Pi model in 2012. Here's a brief overview of the key milestones in the history of Raspberry Pi:
Conception (2006-2008):
The idea of creating an affordable and accessible computer for educational purposes was conceived by Eben Upton, Rob Mullins, Jack Lang, and Alan Mycroft at the University of Cambridge. They were concerned about the decline in computer science skills among students and wanted to create a tool that would inspire young people to learn programming.
Prototype Development (2008-2011):
The team started developing prototypes of the Raspberry Pi, exploring different hardware configurations and form factors. They aimed to create a low-cost single-board computer that could be used for basic programming tasks and experimentation.
Foundation Formation (2009-2011):
To oversee the development and distribution of the Raspberry Pi, the Raspberry Pi Foundation was established as a UK-based charity in 2009. The Foundation's mission was to promote the study of computer science and related subjects, especially among young people, and to put the power of computing into the hands of people all over the world.
Launch of Raspberry Pi Model B (2012):
The first commercially available Raspberry Pi model, the Raspberry Pi Model B, was launched on February 29, 2012. The Model B featured a Broadcom BCM2835 system-on-chip (SoC) with an ARM11 CPU, 512MB of RAM, HDMI output, USB ports, an SD card slot, and GPIO (General Purpose Input/Output) pins for interfacing with external hardware.
Popularity and Expansions (2012-2017):
The Raspberry Pi Model B quickly gained popularity, with widespread adoption in educational institutions, hobbyist communities, and DIY enthusiasts. The success of the Model B led to the release of several more powerful and feature-rich models, including the Raspberry Pi Model A, Model A+, Model B+, and the Raspberry Pi Zero series. Each iteration brought improvements in performance, connectivity, and form factor while maintaining affordability.
Raspberry Pi in Education (2013-Present):
The Raspberry Pi Foundation actively promoted the use of Raspberry Pi in education, offering educational resources, software, and workshops for teachers and students worldwide. The low cost and versatility of Raspberry Pi made it an excellent tool for teaching programming, electronics, and computer science concepts in schools.
Raspberry Pi 4 (2019):
The Raspberry Pi 4 Model B, released in June 2019, was a significant milestone in the evolution of Raspberry Pi. It featured a more powerful Broadcom BCM2711 SoC with options for 1GB, 2GB, or 4GB of RAM, USB 3.0 ports, dual HDMI outputs, Gigabit Ethernet, and improved graphics capabilities. The Raspberry Pi 4 offered enhanced performance, making it more suitable for a wider range of applications, including multimedia and desktop computing.
Continued Innovation (2020-Present):
The Raspberry Pi Foundation continues to innovate and release new models, expanding the capabilities of Raspberry Pi for various applications. The Foundation also supports various educational initiatives and community projects, making Raspberry Pi a leading platform for computer science education and DIY electronics.
Over the years, Raspberry Pi has become a global phenomenon, with millions of units sold worldwide. It has inspired countless projects, from basic programming exercises to sophisticated robotics, home automation systems, media centers, and much more. The Raspberry Pi's impact on computer science education and maker culture has been profound, providing people of all ages and backgrounds with access to affordable and powerful computing resources.
Raspberry Pi can be utilized in autonomous car projects:
Sensor Interface:
Raspberry Pi can act as a central hub to interface with various sensors commonly used in autonomous vehicles, such as cameras, LiDAR (Light Detection and Ranging), ultrasonic sensors, GPS, and IMU (Inertial Measurement Unit). It can capture data from these sensors and process it to extract useful information about the vehicle's environment.
Data Preprocessing:
The collected sensor data can be preprocessed on the Raspberry Pi to reduce data size, filter noise, or perform initial feature extraction. This preprocessing can offload some computational tasks from the main processing unit, allowing it to focus on higher-level decision-making.
Communication:
Raspberry Pi can facilitate communication between different components in the autonomous car system. It can transmit data and control signals between sensors, actuators, and the main processing unit.
Onboard Telemetry:
Raspberry Pi can be used to display real-time telemetry data to the user or provide diagnostic information about the vehicle's performance.
Safety Features:
Raspberry Pi can be employed to implement safety mechanisms, such as emergency stop protocols or collision avoidance systems, enhancing the overall safety of the autonomous car.
Prototyping and Testing:
Raspberry Pi is an excellent platform for rapid prototyping and testing of algorithms and software modules before deploying them on more powerful systems.
It's important to note that while Raspberry Pi can serve as a valuable component in an autonomous car project, it may not be sufficient as the primary processing unit for running complex perception, planning, and control algorithms. For higher-level decision-making and control tasks, more powerful hardware, such as specialized AI processors or powerful microprocessors like NVIDIA Jetson boards, are commonly used in commercial autonomous vehicles.
In summary, Raspberry Pi can play a crucial role in autonomous car projects as a sensor interface, data preprocessing unit, communication hub, and onboard telemetry display. It provides a flexible and affordable solution for building and testing various aspects of an autonomous car system, making it a popular choice among hobbyists, researchers, and educational projects.
- Real-time Autonomous car Created by Rasberry pi:- Link How it is useful in CV (Advantages Of Each One)
Arduino, Nvidia Jetson Nano, and Raspberry Pi are three distinct hardware platforms, each with its own strengths and use cases in the field of computer vision (CV). When combined, they can create powerful and versatile CV systems. Here's how each of these components is useful in CV and how they can work together:
1. Arduino:
Arduino is a microcontroller platform known for its simplicity and versatility. It's often used for interfacing with sensors and actuators in CV applications. Here's how Arduino can be useful:
Sensor Integration: Arduino can interface with various sensors such as ultrasonic sensors, infrared sensors, and IMUs (Inertial Measurement Units). These sensors provide critical data for CV tasks, such as object detection, obstacle avoidance, and motion tracking.
Actuator Control: Arduino can control motors and servos, making it useful for robotics applications. It can move cameras, robotic arms, or other components to adjust the field of view or manipulate objects.
2. Nvidia Jetson Nano:
The Nvidia Jetson Nano is a single-board computer (SBC) with a GPU that is well-suited for computationally intensive CV tasks. Here's how it contributes:
GPU Acceleration: The Jetson Nano's GPU is designed for parallel processing and deep learning tasks. It can significantly accelerate image processing, object detection, and neural network inference, enabling real-time CV applications.
Deep Learning: The Jetson Nano supports popular deep learning frameworks like TensorFlow and PyTorch, allowing developers to train and deploy complex neural networks for tasks such as image classification and object recognition.
High-Level Processing: It can handle high-level processing tasks, making it suitable for autonomous navigation, facial recognition, and gesture detection.
3. Raspberry Pi:
Raspberry Pi is another SBC that provides a balance of processing power and affordability. Here's how it can be useful in CV:
Camera Interface: Raspberry Pi can connect to camera modules, enabling it to capture high-quality images and video. This is essential for CV tasks such as image analysis, facial recognition, and surveillance.
Edge Computing: Raspberry Pi can perform edge computing, where CV algorithms are run directly on the device, reducing latency and bandwidth requirements. This is valuable in applications like smart security cameras and home automation.
How They Work Together:
When combined, these components can create a comprehensive CV system:
Sensor Data Collection: Arduino collects data from sensors like ultrasonic sensors and IMUs.
Data Preprocessing: Arduino can preprocess sensor data and send relevant information to the Jetson Nano or Raspberry Pi.
High-Level CV Processing: Jetson Nano handles computationally intensive CV tasks like object detection, tracking, and deep learning-based recognition.
Edge Deployment: Raspberry Pi can be used for edge deployment, displaying results, and interacting with the user. It also facilitates connectivity and remote control.
By combining Arduino, Jetson Nano, and Raspberry Pi, developers can create embedded CV systems that are capable of real-time processing, edge computing, and sensor integration, making them suitable for applications like robotics, smart surveillance, and autonomous vehicles.
Cost comparison?
1. Arduino:
Arduino boards are generally affordable, with prices starting at around $20 for popular models like the Arduino Uno.
The cost may increase depending on the accessories and sensors you need for your project. Basic sensors like ultrasonic sensors or IR sensors are relatively inexpensive.
2. Nvidia Jetson Nano:
The Nvidia Jetson Nano Developer Kit, which includes the SBC, power supply, and accessories, was priced at around $99 at its launch.
You may also need additional accessories like a microSD card, keyboard, mouse, and a compatible camera module, which can add to the overall cost.
3. Raspberry Pi:
Raspberry Pi boards vary in price depending on the model and configuration.
For example:
Raspberry Pi Zero: Starting at about $20.
Raspberry Pi 3 Model B+: Typically priced around $35.
Raspberry Pi 4 Model B: Starting at approximately $35 for the 2GB RAM version, with higher-priced models for more RAM.
Accessories such as microSD cards, power supplies, cases, and camera modules can add to the total cost.
NOTE: THESE PRICES ARE CHANGE EVERY TIME (MAY BE SOMETIMES INCREASE & DECREASE)
Various Alternatives: Link
Acceleration Techniques: Link
Nvidia Jetson Nano Tricks: Link
Which is the best Approach Method for Educational Purposes On Autonomous Cars?
--- My suggestion is both 4-pillar & END-to-END are best methods to learn
1- AWS DEEP RACER is the best starting level to learning END-to-END
2- starting blog images (Mini autonomous cars) are intermediate level to learning 4-Pillar Method
MODELS AVAILABLE PLACES?
- Papers With Code
- Modelzoo.co
- Open Model Zoo
- TensorFlow Model Garden
- TensorFlow Hub
- MediaPipe
- Awesome CoreML models
- Jetson Zoo
- Pinto Model Zoo
- ONNX Model Zoo
- Bonus: Modelplace.AI
LAST WORDS:-
One thing to keep in the MIND Ai and self-driving Car technologies are very vast...! Don't compare yourself to others, You can keep learning..........
Competition And Innovation Are Always happening...!
so you should get really Comfortable with change...
So keep slowly Learning step by step and implement, be motivated and persistent
Thanks for Reading This full blog
I hope you really Learn something from This Blog
Bye....!
BE MY FRIEND🥂
0 Comments
If you have any doubts; Please let me know