Autonomous Forklift: Logistics Automation 🚜
Building a complete warehouse automation system from scratch using ROS 2 Humble.
The Autonomous Forklift project is finally complete and functional in simulation. This has been a challenging journey into the depths of ROS 2 Navigation (Nav2) and custom robot control.
The Challenge
The goal was to create a system capable of:
- Navigating a warehouse environment autonomously.
- Identifying and picking up pallets.
- Transporting them to specific destination shelves.
- Doing all this while avoiding obstacles and following a strict mission protocol.
The Solution: Graph-Based Navigation
While SLAM is great for mapping, industrial environments often require predictable paths. I implemented a Topological Graph Navigation system.
Using a custom Graph Editor, we defined nodes (Shelves, Home, Waypoints) and edges. The robot uses a BFS algorithm to find the shortest path through these nodes, ensuring it sticks to "safe lanes" within the warehouse.
Key Tech Stack
- ROS 2 Humble: The backbone of the system.
- MVSim: Lightweight 2.5D simulator for fast iteration.
- Nav2: For local planning and obstacle avoidance (AMCL + MPPI).
- Python: For the Mission Control logic and Lift Controller.
What's Next?
The simulation proves the logic works. The next logical step is to deploy this stack onto a real mobile base. I'm looking into using a modified pallet jack with differential drive for the physical implementation.
Check out the full project details on the Project Page.