Collaborative Simultaneous Localization and Mapping (SLAM) for Low-End Swarm Robotics
Abstract: In the rapidly evolving field of swarm robotics, efficient and accurate navigation is pivotal for successful deployment in dynamic and unstructured environments. This research explores the integration of Collaborative Simultaneous Localization and Mapping (SLAM) techniques in low-end swarm robotic systems. Leveraging the collective intelligence and distributed sensing capabilities of robot swarms, the proposed collaborative SLAM framework enhances spatial awareness and map-building processes. By sharing and merging individual robot data in real time, the swarm achieves robust localization and comprehensive environmental mapping, thereby improving navigation accuracy and resilience against individual sensor failures. The effectiveness of the collaborative SLAM approach is demonstrated through extensive simulations and real-world experiments, highlighting its potential to significantly advance autonomous operations in complex and large-scale environments.
Advisor: Professor Carlo Pinciroli
Location: Zoom - https://wpi.zoom.us/j/96141170608