Robotic terrain classification based on convolutional and long short-term memory neural networks

摘要

Robotic mobility remains constrained by complex terrains and technological limitations, hindering real-world applications. This study presents a terrain classification framework integrating Fourier transform, adaptive filtering, and deep learning to enhance adaptability. Leveraging CNNs, LSTMs, and an attention mechanism, the approach improves feature fusion and classification accuracy. Evaluations on the Tampere University dataset demonstrate an 81 % classification accuracy, validating its effectiveness in terrain perception and autonomous navigation. The findings contribute to advancing robotic mobility in unstructured environments.