Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics

Abstract

This letter presents a prediction algorithm of driving energy for future Mars rover missions. The majority of future Mars rovers would be solar-powered, which would require energy-optimal driving to maximize the range with limited energy. The essential and arguably the most challenging technology for realizing energy-optimal driving is the capability to predict the driving energy, which is needed to construct an energy-aware cost function for path planning. In this letter, we propose vision-based algorithms to remotely predict the driving energy consumption using machine learning. Specifically, we develop and compare two machine-learning models in this letter, namely VeeGer-EnergyNet and Veeger-TerramechanicsNet, respectively. The former is trained directly using recorded power, while the latter estimates terrain parameters from the images using a simplified-terramechanics model, and calculate the power based on the model. The two approaches are fully automated self-supervised learning algorithms. To combine RGB and depth images efficiently with high accuracy, we propose a new network architecture called Two-PNASNet-5, which is based on PNASNet-5. We collected a new dataset to verify the effectiveness of the proposed approaches. Comparison of the two approaches showed that Veeger-TerramechanicsNet had better performance than VeeGer-EnergyNet.