As robots become more pervasive in the service sector, control in dynamic environment has become an important element in optimising the deployment of mobile robots. A mobile robot should be knowledgeable not only of the barriers, but also of the surface on which the robot navigates to estimate slippage and adaptive control. We note that various terrains/surfaces have different characteristics, which can directly influence the handling, driving, efficiency, and stability of the robot vehicle. Knowledge of the terrain can provide valuable information for establishing effective and secure navigation strategies. We built a mobile robot prototype equipped by Inertial Measurement Unit (IMU) to obtain the terrain data and applied deep learning models to classify the terrain using the data. Three deep learning configurations have been proposed in this paper, i.e. long short-term memory (LSTM), 1D convolutional network (1D CNN), and convolutional neural network-long short-term memory network (CNN-LSTM). The deep learning architectures were trained and evaluated based on the data collected from five different surfaces. It is shown that the CNN-LSTM performs the best with an F1 score of 98.49%. The other two networks also generalize relatively well with the unseen vibration sequences with F1 scores of 97.47% and 95.98% for the 1D CNN and LSTM, respectively. Finally, we investigate the effect of varying input sequence to find the optimal length, so that we are able to obtain the highest accuracy and generalization of the deep learning networks.
|Number of pages
|CEUR Workshop Proceedings
|Published - 2021
|2021 International Semantic Intelligence Conference, ISIC 2021 - New Delhi, India
Duration: 25 Feb 2021 → 27 Feb 2021
- Terrain classification