DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning

TL;DR

This paper demonstrates how deep learning models, including DNNs and CNNs, improve terrain classification and slip estimation for ground robots, outperforming traditional machine learning methods across multiple datasets.

Contribution

It introduces the application of deep neural networks to terrain classification and slip estimation, analyzing the effects of network architecture and tuning parameters.

Findings

Deep learning models outperform traditional methods in accuracy.

Network architecture significantly impacts performance.

Insights on implementing DNNs and CNNs for robotic terrain analysis.

Abstract

Terramechanics plays a critical role in the areas of ground vehicles and ground mobile robots since understanding and estimating the variables influencing the vehicle-terrain interaction may mean the success or the failure of an entire mission. This research applies state-of-the-art algorithms in deep learning to two key problems: estimating wheel slip and classifying the terrain being traversed by a ground robot. Three data sets collected by ground robotic platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot Fitorobot) are employed in order to compare the performance of traditional machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). This work also shows the impact that certain tuning parameters and the network architecture (MLP, DNN and CNN) play on the performance of those methods. This paper also contributes a deep discussion with the lessons learned in the implementation of DNNs and CNNs and how these methods can be extended to solve other problems.