In 1-2 months this project will go from public to private.

hrr_project

what is hrr? hrr is high school recycle robot

made by grok -> everything in that link -> (https://grok.com/share/bGVnYWN5_5fe3e2f0-9829-4711-bd19-e8ffca285bb5) I made this for a school project. These files are used for domestic purposes and are actually meaningless.

i made virtual environment named myenv(no reason) if you don't want to do then just do pip install -r requirements.txt for making same environment setting if you know well then just see what is in there and choose what you need any question -> ask to me or ask to ai -> me or ai will answer

i use dataset in kaggle(have github link) https://www.kaggle.com/datasets/parohod/warp-waste-recycling-plant-dataset https://github.com/airi-institute/warp

i do (i could misspell so just see )

very important to do in first thing bring all default file from github

1st thing python -m venv myenv

2nd thing .\myenv\Scripts\activate

3rd thing pip install -r requirements.txt

4th thing run main.py code

i made two readme files for no reason

Project idea: Autonomous Robot with SLAM for Mapping and Navigation and recycling waste

Introduction

Hello! I’d like to share with you an exciting robotics project I’m proposing: building an autonomous robot that uses SLAM—Simultaneous Localization and Mapping—with an Arduino and Raspberry Pi. SLAM is a key robotics technique that lets a robot map an unknown space while figuring out where it is within that map. I’m passionate about robotics, and this project will help me learn about sensors, algorithms, and hands-on testing in a meaningful way.

What I Will Do

For this project, I plan to create a robot that can explore and map an indoor space—like a classroom or hallway—without knowing its layout beforehand. It’ll use sensors to spot obstacles and track its own movement, building a map as it goes. Think of how a robot vacuum cleaner learns a room—this project is similar, and it’s a great way for me to explore real-world robotics.

How I Will Do It

Here’s my plan:

• Hardware: I’ll use an Arduino to control the robot’s parts, like:
  • An ultrasonic sensor (HC-SR04) to measure distances to walls or objects.
  • Wheel encoders on the motors to track how far the robot moves by counting wheel turns.
• Data Processing: The Arduino will collect this data—like distances and movement—and send it to a Raspberry Pi 5 through a serial connection.
• SLAM Algorithm: The Raspberry Pi will run a Python program with a simplified SLAM algorithm (probably FastSLAM), using a particle filter to guess the robot’s position and draw a map. It’ll then send commands back to the Arduino—like “go straight” or “turn”—so the robot can explore safely.

How Far I Can Go

If everything works, I think the robot can map a room about 10 meters by 10 meters while dodging obstacles. With more time or resources, I could add:

• Path Planning*: Algorithms like A* to help the robot move more efficiently.
• Visual SLAM: A camera to make the map more detailed.
• Bigger Spaces: Expanding the map as the robot explores larger areas.
  I’m excited to see how far I can take it within our timeline

What Are the Limitations?

There are some challenges I’ll need to tackle:

• Sensor Accuracy: The ultrasonic sensor only reaches about 4 meters and might get confused by noise, which could mess up the map.
• Processing Power: The Raspberry Pi 5 is strong, but a big SLAM algorithm might slow it down, so I’ll need to keep the code efficient.
• *Odometry Drift: Tiny errors in wheel tracking can add up, making the robot’s position less accurate over time.
• Robot Design*: Battery life or size might limit how long it can run, especially in big spaces.
  To handle these, I’ll calibrate the sensors carefully, simplify the algorithm if needed, and maybe add an IMU (but budget issue so may not add) to improve tracking.

Table of Contents for My Proposal

Here’s how I’d organize this for you:

1. Introduction
   • What SLAM is and why it matters in robotics.
   • My goal: a robot that maps and navigates unknown spaces.
2. Objectives
   • Main goal: Build a working SLAM system.
   • Extra goals: Avoid obstacles, maybe add path planning or a camera.
3. Methodology
   • Hardware: Arduino for sensors/motors, Raspberry Pi for computing.
   • Software: Arduino code for data, Python for SLAM on the Pi.
   • Communication: Serial link between the two.
   • Algorithm: Particle filter SLAM with an expandable map.
4. Expected Outcomes
   • Map a room up to 10m x 10m.
   • Show obstacles and free space on the map.
   • Possible extras: better navigation or bigger maps.
5. Challenges and Limitations
   • Sensor range and noise issues.
   • Raspberry Pi speed limits.
   • Wheel tracking errors and fixes.
   • Battery and size constraints.
6. Timeline and Milestones
   • Weeks 1: Set up hardware and motors.
   • Weeks 2: Add sensors and collect data.
   • Weeks 3: Code the SLAM algorithm.
   • Weeks 4: Test and tweak it.
   • Weeks 5: Add extras or wrap up.
7. Preliminary Work and Resources
   • What I’ve started (like hardware tests or code sketches).
   • What I’ll need (sensors, software) and where I’ll get it.
8. Conclusion
   • Why this project is doable and worth doing.
   • My excitement to make it happen with your support.

Conclusion

I think this project is a fantastic chance to grow my robotics skills—learning about sensors, coding, and problem-solving. With your advice and the tools we have, I’m confident I can make this SLAM robot work. I’d really appreciate your approval to get started—it’s a big step for me in exploring robotics. Thanks for listening to my idea!

i get no mentor no budget no high coding skill. I asked my teacher at school, but he refused without even looking, so I feel a little sad. So, if I have manus' help, I think the project will progress more smoothly.

Future plan

https://manus.im/share/cffkZa0fIcnSjnIid694ee?replay=1 i got no coding and thinking skill so i use manus for making something evert future plan in that link

issue

almost everything detected as canister.

Performance is down if the background is not black.

classes

bottle-blue bottle-green bottle-dark bottle-milk bottle-transp bottle-multicolor bottle-yogurt bottle-oil cans juice-cardboard milk-cardboard detergent-color detergent-transparent detergent-box canister bottle-blue-full bottle-transp-full bottle-dark-full bottle-green-full bottle-multicolorv-full bottle-milk-full bottle-oil-full detergent-white bottle-blue5l bottle-blue5l-full glass-transp glass-dark glass-green