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.

本研究探讨了联邦学习在利用激光雷达传感器装备的机器人发现迷宫方面的应用。通过训练分类模型来准确识别由不规则形状墙壁组成的两个不同方形迷宫内网格区域的形状，解决了由于墙壁形状不同，一个迷宫中的分类模型无法普遍适用于另一个迷宫的问题。通过机器人之间采用联邦学习框架，探索一个迷宫的集体知识使其可以准确地在未知的迷宫中操作，这展示了联邦学习在迷宫发现任务中提高分类准确性和鲁棒性方面的有效性。

利用多个机器人进行迷宫探索的联邦学习