Multi-Robot Collaborative SLAM
This project, developed at the University of Michigan's Intelligent Robotics and Autonomy Lab, focuses on creating advanced algorithms for collaborative simultaneous localization and mapping (SLAM) using multiple autonomous robots.
Research Objectives
- Develop distributed algorithms for multi-robot coordination
- Optimize communication protocols for real-time data sharing
- Implement robust localization under GPS-denied conditions
- Create scalable solutions for large robot swarms
Technical Approach
Distributed SLAM Algorithm
The system implements a novel distributed SLAM algorithm that allows multiple robots to:
- Share Local Maps: Robots exchange local mapping data using optimized communication protocols
- Consensus Mapping: A distributed consensus algorithm merges individual maps into a global representation
- Coordinated Exploration: Robots coordinate their movements to maximize mapping efficiency
Simulation Framework
Built using ROS2 and Gazebo, the simulation framework supports:
- Up to 20 simulated robots
- Various environment configurations
- Real-time performance monitoring
- Hardware-in-the-loop testing capabilities
Current Results
- 50% improvement in mapping accuracy compared to single-robot SLAM
- Scalable performance up to 15 robots without significant degradation
- Robust operation in challenging environments with limited communication
Applications
This research has applications in:
- Search and rescue operations
- Autonomous warehouse management
- Planetary exploration missions
- Infrastructure inspection and monitoring
Publications
Research findings from this project are being prepared for submission to the International Conference on Robotics and Automation (ICRA) 2025.