> For the complete documentation index, see [llms.txt](https://sws3009.gitbook.io/sws3009-tutorial/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sws3009.gitbook.io/sws3009-tutorial/10-july-consultation/group-6.md).

# 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.