Group 2

Delivery Robot for Food Distribution to Animals

Hardware Considerations:

1. Picking Mechanism:

   • Requirement: The robot should be able to pick up various food items.

   • Suggestions:

     • Use a robotic arm equipped with a gripper or a magnet, depending on the type of food container.

     • Ensure the gripper can handle different shapes and sizes of food containers securely.

2. Navigation and Localization:

   • Challenge: Localization could be problematic in a dynamic environment.

   • Suggestions:

     • Implement QR codes on shelves and along the path to help with localization.

     • Use a straight-line path where possible to simplify navigation.

     • Utilize a 2D map for the robot to navigate, ensuring it can move efficiently within the mapped area.

Deep Learning:

1. Object and Animal Identification:

   • Objective: Develop a deep learning model to identify different food items and the specific animals.

   • Prototypes:

     • Train the model to recognize various food items.

     • Train the model to identify the xy coordinates of animals and their respective food items on the 2D map.

2. Implementation Steps:

   • Prototype Development: Build a prototype that can:

     • Identify food items using image recognition.

     • Locate animals based on the map coordinates and QR codes.

Operational Flow:

1. Food Delivery Process:

   • Step 1: Identify the food item to be delivered using the trained deep learning model.

   • Step 2: Pick up the food item using the robotic arm or magnet.

   • Step 3: Navigate to the animal’s location using QR codes for precise localization.

   • Step 4: Deliver the food item to the correct animal.

Next Steps for Students:

1. Deep Learning Tasks:

   • Prototype Development: Create a working prototype that can identify food items and determine their coordinates.

   • Training: Collect data and train the deep learning model for accurate identification.

2. Robotics Tasks:

   • Pick-Up Mechanism: Design and implement the robotic arm or magnet to pick up food items.

   • Integration: Integrate the picking mechanism with the robot’s navigation system.

Final Considerations:

• Testing and Validation: Conduct thorough testing to ensure the robot can accurately pick up and deliver food items.

• Iterative Improvements: Based on testing results, iteratively improve the robot’s design, navigation, and deep learning models.

Summary:

• Ensure the robot can handle different food items with a reliable picking mechanism.

• Simplify navigation with QR codes and straight-line paths where possible.

• Develop and train deep learning models for accurate identification and localization of food items and animals.

• Focus on a clear operational flow from food item identification to delivery.