Toyota Innovation Challenge: Automated Guided Vehicles (AGVs)

This repository contains the code and resources developed during the Toyota Innovation Challenge 2024, where our team tackled the challenge of designing and implementing solutions for Automated Guided Vehicles (AGVs) to navigate a manufacturing environment efficiently and reliably.

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🚀 Project Overview

Problem Statement

The main goal was to develop an AGV system that:

• Delivers components from pickup points to delivery stations.
• Avoids collisions with static obstacles, walls, and other AGVs.
• Obeys traffic signals, such as stop signs.
• Minimizes delivery time in a dynamic manufacturing environment.

Requirements

Our AGV system was validated through a series of standardized tests, including:

1. Collision detection with static fixtures.
2. Traffic signal recognition and response.
3. Autonomous navigation in dynamic and static obstacle environments.
4. Fleet control with multiple AGVs operating simultaneously.

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🧪 Features and Testing Levels

We implemented and tested the AGV system across the following levels:

1. Keyboard Control with Safety Features:
   • Manual control with collision detection.
2. Keyboard Control with Awareness:
   • Stop sign detection.
3. Autonomous Control with Static Obstacles:
   • Fully autonomous navigation avoiding static obstacles and obeying traffic signals.
4. Autonomous Control with Dynamic Obstacles:
   • Navigation amidst moving objects (NPCs).
5. Multi-Agent/Fleet Control:
   • Coordinated control of multiple AGVs in a shared space.

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🛠️ Technology and Tools

• Hardware: TurtleBot robots for real-world testing.
• Simulation: Gazebo for validating algorithms in virtual environments.
• Programming Language: Python
• ROS (Robot Operating System): For AGV control and integration.
• OpenCV: For vision-based traffic signal detection.

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📜 Validation and Results

Our AGV system was rigorously tested across various scenarios and achieved the following results:

• Collision Detection: The AGV successfully detected and avoided static obstacles in both simulation and real-world tests.
• Traffic Signal Recognition: Stop signs were accurately identified, and the AGV responded by halting appropriately.
• Autonomous Navigation: Completed all partial and full courses, navigating efficiently with minimal delivery time.
• Dynamic Obstacle Avoidance: Demonstrated reliable performance in environments with moving objects (NPCs).
• Fleet Control: Coordinated operation of multiple AGVs without collisions, showcasing efficient multi-agent communication.