Group 10

Spying and Vacuum Cleaning Robot

Objective: Develop a robot that can find and spy on specific documents while also functioning as a vacuum cleaner. The robot should start spying when no one is in the room or when no one is looking at it, and start vacuuming when it detects people.

Deep Learning:

1. Presence Detection:

   • Task: Detect whether people are in the room or looking at the robot.

   • Technique: Use deep learning models to identify human presence and their gaze direction.

2. Document Identification:

   • Task: Recognize and locate specific documents.

   • Implementation: Train a model to identify target documents based on visual features and keywords.

Robotics and Hardware:

1. Vacuum Cleaning Functionality:

   • Task: Develop a vacuum cleaning mechanism that can operate efficiently.

   • Implementation: Equip the robot with a vacuum cleaner that can be activated based on the presence detection model.

2. Object Manipulation:

   • Task: Design a mechanism for grabbing or vacuuming up items, including documents.

   • Implementation: Ensure the robot can securely grab or suction documents and other small objects.

Implementation Steps:

1. Deep Learning Development:

   • Presence Detection: Collect and annotate data to train models for detecting human presence and gaze direction.

   • Document Identification: Create a dataset of target documents and train the model to recognize and locate them.

2. Robotics Development:

   • Vacuum Mechanism: Design and build a vacuum cleaning system for the robot.

   • Object Manipulation: Develop a mechanism for grabbing or suctioning items.

3. System Integration:

   • Presence-Based Behavior: Integrate the presence detection model to switch between spying and vacuuming modes.

   • Document Retrieval: Implement the document identification model to guide the robot in locating and retrieving target documents.

Next Steps for Students:

1. Deep Learning Tasks:

   • Dataset Preparation: Collect and label data for presence detection and document identification.

   • Model Training: Train and validate models for each task, focusing on accuracy and reliability.

2. Robotics Tasks:

   • Vacuum Design: Design and prototype the vacuum cleaning system.

   • Grabbing Mechanism: Develop and test the mechanism for grabbing or suctioning documents.

3. Testing and Iteration:

   • System Testing: Conduct tests to ensure the robot can switch between spying and vacuuming modes based on presence detection.

   • Iterative Improvement: Refine the design and functionality based on test results.

Final Considerations:

• System Integration: Ensure smooth integration of deep learning models with the robotic hardware.

• Reliability: Focus on making the system reliable in detecting human presence and switching modes accurately.

• User Safety: Design the system to operate safely around humans and avoid any potential hazards.

Summary:

• Develop a robot that can spy on documents and vacuum the room based on the presence of people.

• Use deep learning for human presence detection and document identification.

• Design and integrate a vacuum cleaning system and a mechanism for grabbing or suctioning items.

• Ensure seamless switching between spying and vacuuming modes for effective operation.