Maze Discovery using Multiple Robots via Federated Learning
Ranasinghe, Kalpana; Madushanka, H.P.; Scaciota, Rafaela; Samarakoon, Sumudu; Bennis, Mehdi (2024-10-31)
IEEE
31.10.2024
K. Ranasinghe, H. P. Madushanka, R. Scaciota, S. Samarakoon and M. Bennis, "Maze Discovery using Multiple Robots via Federated Learning," 2024 IEEE Symposium on Computers and Communications (ISCC), Paris, France, 2024, pp. 1-3, doi: 10.1109/ISCC61673.2024.10733625
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412097105
https://urn.fi/URN:NBN:fi:oulu-202412097105
Tiivistelmä
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.
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.
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