This GitHub repository houses the code and documentation for our Hexapod Robot project, which focuses on autonomous control using a curve detection system. The project delves into cutting-edge robotics technologies, encompassing lane detection, color tracking, and real-time decision-making for a hexapod robot.
- Introduction
- Code Architecture
- Camera Integration
- Lane Curve Analysis
- Color Picker Script
- Histogram Analysis
- Challenges and Learning Experiences
- Future Enhancements
- Contributing
- License
Our project is dedicated to advancing the field of robotics by enabling our hexapod robot to autonomously navigate using a curve detection system. We employ advanced technologies such as OpenCV, Python, and Raspberry Pi to provide real-time decision-making capabilities for the robot.
Our code is organized into various modules that work collaboratively. The main module orchestrates the entire operation, with specialized modules for webcam data handling and image processing. We utilize pixel summation techniques, with extensive support from the OpenCV library, to process the robot's real-time camera feed.
In the Camera Integration section, we explain how we integrated the camera into the hexapod robot's body. We detail the necessary commands and configurations, including overcoming the initial hurdle of enabling the camera.
Our Lane Curve Analysis section provides an in-depth understanding of how we analyze the curvature of the lane in real-time. We describe the process of warping the image, identifying key points, and making adjustments to create a straighter image.
This section delves into the critical role of the color picker script in our project. We explain how this script accurately identifies RGB values associated with the lane's color, which serves as a reference point for lane tracking.
In the Histogram Analysis section, we explore how we use histograms to assess the intensity distribution across images. We describe our approach to calculating the curve value and using this information to guide the robot's movements.
Our journey was not without its challenges, and in this section, we share some of the obstacles we encountered and the valuable lessons we learned. Challenges included precise camera calibration, optimizing the curve detection algorithm, and adapting the robot's behavior to different driving scenarios.
The Future Enhancements section outlines our plans for improving the project. This includes making the system more robust under diverse lighting conditions, enhancing the robot's adaptability to different types of lanes, and further developing our autonomous navigation algorithms.
We welcome contributions from the community to enhance this project. If you're interested in contributing, please refer to our Contributing Guidelines.
This project is open-source and is licensed under the MIT License.