Intelligent Home Item-Finding Robot

Professor Introduction

716 Robotics Team | Specializes in intelligent robotics

Project Description

The research focuses on developing an intelligent home robot designed to locate lost items, addressing the inconvenience and frustration of misplaced belongings in daily life. The goal is to design and explore the potential of a home item-finding robot that leverages advanced technologies such as artificial intelligence (AI) and machine learning (ML) to identify and locate various lost items. By integrating computer vision and deep learning algorithms, the robot will be trained to recognize common household items and accurately determine their locations using sensors and cameras. Users can interact with the robot through intuitive interfaces, such as voice commands and mobile applications, to request assistance in finding lost items. The research will also emphasize system optimization and experimental validation to ensure the robot's performance, reliability, and practicality in real-world scenarios. By collecting user feedback and continuously improving the robot's capabilities, the project aims to develop a practical and user-friendly home item-finding robot that saves time and effort, providing a convenient solution for retrieving lost items.

Project Keywords

Project Outline

Part 1 :  Introduction to Intelligent Home Robots
• Overview of Intelligent Robots and Their Applications
• Importance of Item-Finding Robots in Enhancing Daily Life Convenience
• Current Solutions and Their Limitations

Part 2 : Research Objectives and Hypotheses
• Investigating the Feasibility of an Intelligent Home Item-Finding Robot
• Developing and Optimizing the Item-Finding Technology

Part 3 :  Review of Current Research and Methods
• Review of Existing Item-Finding Technologies and Algorithms
• Identification of Gaps and Limitations in Current Solutions

Part 4:  Design and Simulation of the Item-Finding Robot
• Analyzing the Requirements for Item Recognition and Localization
• Designing the Robot's Mechanical and Electronic Components
• Simulating Item-Finding Scenarios and Performance Enhancements

Part 5 :  Development of AI and Machine Learning Models
• Developing Computer Vision Algorithms for Item Recognition
• Training Deep Learning Models for Accurate Item Identification
• Implementing Sensor Integration for Real-Time Item Localization

Part 6 :  Prototype Manufacturing and Testing
• Manufacturing the Robot Prototype and Assembling Components
• Debugging and Initial Testing of the Prototype
• Evaluating the Prototype's Performance in Simulated and Real-World Scenarios

Part 7: Optimization and System Tuning
• Identifying Optimal Parameters for Item Recognition and Localization
• Iteratively Optimizing the AI Models and Control Systems
• Quantitative Analysis of Item-Finding Efficiency and Reliability

Part 8: User Interface Design and Interaction
• Designing Intuitive User Interfaces for Seamless Interaction
• Implementing Voice Command and Mobile Application Control Mechanisms
• Testing User Interaction and Collecting Feedback for Improvements

Part 9:  Results and Discussion
• Graphical Representation of Experimental Results and Performance Metrics
• Interpretation of Results and Discussion of Implications for Home Robotics
• Comparison with Existing Solutions and Discussion of Advantages and Limitations

Part 10: Conclusion and Future Directions
• Summary of Key Findings and Their Significance
• Identification of Research Limitations and Suggestions for Future Research
• Recommendations for Practical Applications in Home Robotics

Part 11: Reporting and Presentation
• Writing a Detailed Research Report with Clear Structure, Concise Language, and Accurate Data Presentation
• Preparing and Delivering a Clear and Engaging Oral Presentation of Research Background, Methods, Results, and Conclusions

Suitable for

High School Students:  
• Interest in Robotics and AI: Students with a strong interest in robotics, artificial intelligence, and practical applications of technology.
• Basic Knowledge : Students with a basic understanding of programming, electronics, and mechanics.

University Students:
• Relevant Major :  Students majoring in mechanical engineering, electrical engineering, computer science, or related fields.
• Proficiency in Software Tools :  Students with skills in programming languages (Python, C++), and familiarity with machine learning frameworks (TensorFlow, PyTorch).