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The dynamics of extraterrestrial rovers is dependent on the terrain. The high-level terrain classification used in most current rovers is often not enough to ensure safe path selection, as the experience with NASA’s Curiosity and Spirit shows.

Adaptive Meta Learning for Identification of Rover Terrain Dynamics

Credits: NASA/JPL-Caltech/MSSS

A recent paper suggests a model of the terrain parameters that govern wheel-terrain interaction. Knowing the terrain may help to predict whether the neighboring regions are traversable, plan the safest route, and prevent damage.

A linear model, which relates terrain parameters (namely cohesion and internal friction angle) and rover dynamics is supplemented by a meta-learned neural network. The interpretability of the model is enhanced by the orthogonality of nominal and meta-learned features. The model is capable of rapid adaptation and provides low estimation errors (the largest error is less than 5%).

Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

Link: https://arxiv.org/abs/2009.10191