Group 9

Project Retrieval Robot

Objective: Develop a robot that can follow a person, recognize specific projects, and retrieve them using a tuned IMU and deep learning techniques.

Deep Learning:

1. Person Following:

   • Task: Implement a model that enables the robot to follow a person accurately.

   • Technique: Utilize deep learning algorithms to identify and track the person.

2. Project Recognition:

   • Task: Recognize specific projects using visual data.

   • Implementation:

     • Use anchor points with printed labels for project identification.

     • Train a model to recognize these labels and distinguish different projects.

3. Gesture Recognition:

   • Requirement: Implement gesture recognition by Friday.

   • Technique: Train a deep learning model to recognize hand gestures and interpret commands.

Robotics and Hardware:

1. IMU Tuning:

   • Algorithm: Use the Kalman Filter Algorithm to tune the IMU for better accuracy.

   • Anchor Points: Utilize anchor points with printed labels to assist in localization and tuning.

2. Mechanism for Retrieval:

   • Task: Design a mechanism to push or retrieve projects.

   • Design Considerations:

     • Ensure the mechanism can handle different types of projects securely.

     • Focus on creating a reliable and efficient retrieval system.

Implementation Steps:

1. Deep Learning Development:

   • Person Following: Collect and annotate a dataset for person tracking.

   • Project Recognition: Create and label a dataset of projects with anchor points.

   • Gesture Recognition: Gather and annotate data for gesture recognition and train the model.

2. IMU Tuning:

   • Kalman Filter Implementation: Develop and test the Kalman Filter for IMU tuning.

   • Anchor Points: Place and label anchor points in the environment to assist with IMU tuning and localization.

3. Mechanism Design:

   • Prototype Development: Design and build a prototype for the project retrieval mechanism.

   • Testing: Test the mechanism's ability to push and retrieve various projects.

Next Steps for Students:

1. Deep Learning Tasks:

   • Dataset Preparation: Collect and label data for person following, project recognition, and gesture recognition.

   • Model Training: Train models for each task and validate their performance.

2. IMU Tuning:

   • Kalman Filter: Implement the Kalman Filter for IMU tuning and validate its accuracy using anchor points.

   • Localization: Test the system's localization accuracy with the tuned IMU.

3. Mechanism Development:

   • Design: Develop a reliable mechanism for pushing and retrieving projects.

   • Integration: Integrate the mechanism with the robot's control system and test its performance.

Final Considerations:

• System Integration: Ensure seamless integration of deep learning models, IMU tuning, and the retrieval mechanism.

• Testing and Iteration: Conduct thorough testing and iterate on the design based on feedback to improve accuracy and reliability.

• User Interaction: Focus on creating an intuitive and user-friendly system for person following and gesture recognition.

Summary:

• Use the Kalman Filter Algorithm to tune the IMU for better localization.

• Implement deep learning models for person following, project recognition, and gesture recognition.

• Design and build a mechanism to push or retrieve projects, ensuring seamless integration and reliable performance.