Title
Maze Discovery using Multiple Robots via Federated Learning
System Model
Abstract
This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes made up with irregular shaped walls. Due to the use of different shapes for the walls, a classification model trained in one maze that captures its structure does not generalize for the other. This issue is resolved by adopting FL framework between the robots that explore only one maze so that the collective knowledge allows them to operate accurately in the unseen maze. This illustrates the effectiveness of FL in real-world applications in terms of enhancing classification accuracy and robustness in maze discovery tasks.
Index Terms
Federated Learning, Maze Discovery.
Acknowledgements
This work was supported by NSF-AKA CRUISE (GA 24304406), VERGE (GA 101096034), 6G-INTENSE (GA 101139266), and Infotech-R2D2. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Afiliation
^1 Centre for Wireless Communication, University of Oulu, Finland
^2 Infortech Oulu, University of Oulu, Finland