Group 6

Project: Dog Care Robot

Objective:

• Develop a robot to assist in taking care of a dog by detecting its motion and providing interactions such as playing with a ball and delivering messages from the owner.

Simplified Feature Selection:

• Chosen Feature: Motion detection to monitor the dog’s movements.

Deep Learning:

1. Motion Detection Model:

   • Model Choice: Use YOLO (You Only Look Once) for real-time motion detection.

   • Accuracy Target: Aim for a training accuracy of 90-95% by tomorrow.

   • Implementation Steps:

     • Dataset Collection: Collect and annotate videos of dog movements.

     • Model Training: Train the YOLO model on the annotated dataset to detect the dog’s motion.

     • Validation: Test the model to ensure it meets the desired accuracy.

Robotics:

1. Ball Interaction Mechanism:

   • Task: Design a mechanism to push a ball to the dog.

   • Prototype Development:

     • Create a mechanical system capable of pushing a ball.

     • Ensure the system is safe and reliable for interaction with the dog.

2. Voice Message System:

   • Task: Implement a system to play a recorded message from the owner.

   • Implementation Steps:

     • Audio Playback: Equip the robot with a speaker to play recorded messages.

     • Message Storage: Provide a simple interface for the owner to record and store messages.

Implementation Steps:

1. Deep Learning Development:

   • Data Collection: Gather videos of dogs to create a training dataset.

   • Model Training: Train YOLO on the collected dataset, focusing on achieving high accuracy.

   • Model Testing: Validate the model’s performance in real-world scenarios.

2. Robotics Development:

   • Ball Pushing Mechanism: Design, build, and test the mechanism for pushing a ball.

   • Voice Message System: Develop the audio playback system and integrate it with the robot’s control unit.

3. Integration:

   • Communication: Ensure seamless communication between the motion detection model and the robotic mechanisms.

   • Automation: Implement an automated workflow where detected motions trigger interactions (e.g., pushing the ball, playing messages).

Next Steps for Students:

1. Deep Learning Tasks:

   • Dataset Preparation: Collect and label videos of dog movements.

   • Model Training: Train the YOLO model, aiming for 90-95% accuracy.

   • Testing: Validate the model to ensure it detects motion accurately.

2. Robotics Tasks:

   • Mechanism Design: Design and prototype the ball-pushing mechanism.

   • Audio System: Develop the system to play recorded messages from the owner.

   • System Integration: Integrate the motion detection model with the robotic mechanisms.

3. Testing and Iteration:

   • Prototype Testing: Test the complete system with a real dog.

   • Iterative Improvement: Refine the design based on testing feedback, focusing on reliability and safety.

Final Considerations:

• System Safety: Ensure all interactions are safe for the dog.

• Real-Time Performance: Optimize the system for real-time detection and interaction.

• User-Friendly Interface: Design the system to be easily used by the dog owner.

Summary:

• Focus on building a motion detection model using YOLO with high accuracy.

• Develop a mechanism to push a ball for the dog to play with and implement an audio system to play messages from the owner.

• Ensure the system is safe, reliable, and user-friendly.