• 

  Autodesk Inventor Screenshot of the "waste object" I 3D Printed

• 

  K9 Explorer Banner

• 

  K9 Robot Showcase

• 

  Project Demo 1

• 

  Project Demo 2

• 

  Project Demo 3

K9 Explorer - Beach Cleanup Robot

Brainstorming

1. Robot Name: K9 Vision
2. Idea: Beach Cleanup Robot
3. Objective: Use AI and robotics to identify and collect trash in beach areas.

Goals

1. Purpose:

   • Combat plastic pollution on beaches.
   • Educate the public about ocean conservation.

2. Functionalities:

   • Monitor designated areas for trash.
   • Identify and locate trash items using AI (Computer Vision).
   • Navigate to trash and signal collection readiness.
   • Classify trash items using a trained model.

3. Impact:

   • Enhance beach cleanliness.
   • Raise awareness about plastic pollution.
   • Demonstrate the potential of AI and robotics in environmental conservation.

Final Plan

Prototype Development

1. Hardware:

   • Raspberry Pi-powered robot car.
   • 4 mecanum wheels.
   • Distance sensor, line follower sensor, and camera.
   • LED lights for status indication.

2. Software:

   • OpenCV for vision processing.
   • TensorFlow for object classification.
   • Python for Robot Control

Functional Description

1. Monitoring Mode:

   • Robot circles the designated area using the line follower sensor.
   • Green LED breathing effect to indicate standby mode.

2. Trash Detection:

   • User initiates trash detection via the dashboard.
   • Robot stops circling, scans for grey 3D-printed trash items.
   • Once trash is located, the robot navigates to it and flashes red LEDs.

3. Trash Classification:

   • Uses a trained AI model to classify the trash.

Implementation Plan

1. 3D Printing:

   • Print grey objects to simulate trash.
   • Use existing colored balls for initial setup.

2. AI Integration:

   • Train a model to classify different types of trash.
   • Implement object detection and classification using TensorFlow and OpenCV.

3. Testing:

   • Test the robot's navigation and trash detection in a controlled environment.
   • Fine-tune the AI model for accuracy.

Built With

• opencv
• python
• raspberry-pi