MazeBlaze

Greetings, curious minds! We’re excited to embark on this digital journey as we dive into the captivating world of robotics and artificial intelligence. Welcome to the inaugural blog post of Mazeblaze, a project that’s set to unravel the mysteries of maze-solving bots.

Mentors:

– Raj Gupta – Prit Kanadiya

Mentees:

– SURAJ SONAWANE – ATHARVA NERKAR

Our Introduction:

Driven by an unyielding enthusiasm for embedded systems, our goal is clear -to improve MazeBlaze-v2.1’s design and model in order to reach unusual efficiency and achieve remarkable speeds. Join us on this journey as we solve problems, engineer creative solutions, and lead MazeBlaze into a realm of unmatched success.

Why we choose this project:

It’s our passion for embedded systems in action. We’re drawn by the opportunity to innovate, push limits, and convert theory into hands-on experience. MazeBlaze is our playground for problem-solving, our canvas for collaboration, and a roadmap to future success. This project isn’t just about a bot; it’s about cultivating skills, embracing challenges, and shaping a future that thrives on innovation. Join us as we explore MazeBlaze’s chapters, turning every motivation into meaningful milestones.

WHAT IS MAZEBLAZE?

Maze blaze is a line following and maze solving bot,which uses algorithms to plan it’s path around the maze. As we delve into this project, we envision unveiling the secrets behind creating an intelligent machine that can decipher the twists and turns of mazes.This algorithms can be used in different environment like a path for robotic waiter.

The key elements of this project are:

Control Systems: In our blog, we unravel the intricacies of Control Systems, understanding how they shape MazeBlaze’s behavior and navigation.

Embedded C: Explore the realm of Embedded C programming as we share insights into optimizing MazeBlaze’s code for efficient and effective maze-solving.

PCB Designing: Join us in the world of PCB Designing, where we craft the physical foundation of MazeBlaze, ensuring its components work harmoniously to achieve our goals.

Graph Algorithms: Dive into the world of Graph Algorithms, discovering how they empower MazeBlaze to make intelligent decisions while navigating complex mazes.

Electronics: Our blog highlights the crucial role Electronics plays in our project. Explore the components that breathe life into MazeBlaze and drive its pursuit of unmatched performance.

Our Progress Update:

1. LED Blinking with Bit Masking: We began by mastering LED blinking using bit masking—a technique that showcases our coding finesse. for that we referred esp-idf documentation and SRA Wall-E reposatory.

2. Playlist for PCB Design: Our journey expanded into PCB design, with us delving into a comprehensive playlist to optimize MazeBlaze’s physical structure.

4. Mechanical Design Exploration: Our quest for improvement extends to mechanical design. We researched, make sheet, and by waching some videos. To improve design

3. Line Sensing Array PCB Designing: Our technical competence shines as we successfully designed a PCB for the Line Sensing Array using TCRT500.

5. Understanding Wall-E Line Following & creating algorithm for Left Follow Rule (In progress): Currently, we’re working on an algorithm. This will help our bot follow lines using a “left follow” rule.

Challenges Ahead:

1. Best Mechanical Design: Our bot’s physical structure needs a makeover. We’re creating an awesome design that’s not just speedy but also strong and ready to tackle any challenge.

2. Better Hardware: Speeding up means our parts need to work really well together. We’re fine-tuning things like sensors and motors, making sure they work together perfectly.

3. Real-Life Surprises: Real mazes are all sorts of shapes, just like real life is full of surprises. We’re getting ready for anything that might pop up while MazeBlaze roams around.

4. Faster Decisions: MazeBlaze needs to think fast to solve mazes quickly. We’re working on making it decide super quickly without making mistakes.

5. Using Less Power: Going faster shouldn’t mean using more energy. We’re finding smart ways to make MazeBlaze quick without using up too much power.

6. Mixing Everything Together: Making software, hardware, and instructions work together isn’t easy. We’re making sure everything talks nicely to each other, like teamwork.

7. Smarter Algorithms: Making our maze-solving better is tough. We’re diving into making our computer instructions smarter, so our MazeBlaze can tackle even tricky mazes.

8. Surprises Ahead: Robots are full of surprises, just like solving puzzles. We know there will be unexpected problems, but we’re ready to figure them out.

We’ll show you how we tackle these challenges. Each one helps us make MazeBlaze even better. Stay with us as we share our journey, wins, and even the tricky parts.