Highlights Increasing terrain awareness of autonomous rough-terrain vehicles. Estimating terrain type using proprioceptive sensors. Different embodiments of deep-learning algorithms developed and tested against standard machine learning. Field evaluation of the system performance on various surfaces.
Abstract The future challenge for field robots is to increase the level of autonomy towards long distance (>1 km) and duration (>1h) applications. One of the key technologies is the ability to accurately estimate the properties of the traversed terrain to optimize onboard control strategies and energy efficient path-planning, ensuring safety and avoiding possible immobilization conditions that would lead to mission failure. Two main hypotheses are put forward in this research. The first hypothesis is that terrain can be effectively detected by relying exclusively on the measurement of quantities that pertain to the robot-ground interaction, i.e., on proprioceptive signals. Therefore, no visual or depth information is required. Then, artificial deep neural networks can provide an accurate and robust solution to the classification problem of different terrain types. Under these hypotheses, sensory signals are classified as time series directly by a Recurrent Neural Network or by a Convolutional Neural Network in the form of higher-level features or spectrograms resulting from additional processing. In both cases, results obtained from real experiments show comparable or better performance when contrasted with standard Support Vector Machine with the additional advantage of not requiring an a priori definition of the feature space.
Recurrent and convolutional neural networks for deep terrain classification by autonomous robots
Journal of Terramechanics ; 96 ; 119-131
2020-12-05
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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