Welcome to the official repository for Team CEAR's Robocon 2026 submission. This repository contains the CAD models, technical documentation, design calculations, and the machine learning modules developed for the competition.
Detailed 3D models and assembly views of our robots.
- Root Files:
Final.STEP: The master assembly file for the robot design.Lifting-Mechanism .mp4: Video demonstration of our custom lifting system.
- R1_Views/: High-resolution renders and technical views of Robot 1 (R1).
- R2_Views/: High-resolution renders and technical views of Robot 2 (R2).
A specialized vision module to identify and classify game elements.
- Location:
FakeReal-box-detection/ - Features:
- Convolutional Neural Network (CNN) built with TensorFlow/Keras.
- Classifies boxes into "Real" and "Fake" categories.
- Optimized for low-latency inference on edge devices.
- Includes a full training pipeline and dataset management.
Scientific grounding for our mechanical designs.
- Location:
Calculations/ - Contents:
Calculations.pdf: Detailed stress analysis, torque requirements, and stability factors.linear actuators calculation.jpeg: Selection criteria and stroke analysis for our actuator systems.
Stage I Document – CEAR.pdf: Our comprehensive project report detailing the strategy, mechanical design, electronics, and software architecture.
- Mechanical Design: SolidWorks / CAD Software (STEP, MP4 Exports)
- Deep Learning: Python, TensorFlow, Keras, NumPy
- Documentation: LaTeX/Markdown, Engineering Analysis
- Robust Vision System: High-accuracy real/fake box classification for autonomous tasks.
- Optimized Lifting Mechanism: Custom-engineered system for efficient object handling.
- Multi-Robot Coordination: Designed for seamless integration between R1 and R2.
| Role | Details |
|---|---|
| Team Name | CEAR |
| Project | ABU Robocon 2026 |
| Focus | Robotics, AI Vision, Mechanical Excellence |
This project is for Robocon 2026 submission purposes. All rights reserved by Team CEAR.