Maze Discovery using Multiple Robots via Federated Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917709952843776 |
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| author | Ranasinghe, Kalpana Madushanka, H. P. Scaciota, Rafaela Samarakoon, Sumudu Bennis, Mehdi |
| author_facet | Ranasinghe, Kalpana Madushanka, H. P. Scaciota, Rafaela Samarakoon, Sumudu Bennis, Mehdi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maze Discovery using Multiple Robots via Federated Learning Ranasinghe, Kalpana Madushanka, H. P. Scaciota, Rafaela Samarakoon, Sumudu Bennis, Mehdi Machine Learning Artificial Intelligence Robotics Image and Video Processing 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. |
| title | Maze Discovery using Multiple Robots via Federated Learning |
| topic | Machine Learning Artificial Intelligence Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2407.01596 |