Behavior Mode Selection based on Environment Map Understanding by Deep Learning for Planetary Rover

Abstract

This paper presents a method to select behavior modes autonomously for a planetary rover. In the conventional methods, the behavior modes of a rover are selected by operators. Once the environment changes, however, it takes a long time to drive to the destination, because the intervention by operators is needed. Therefore, autonomous behavior mode selection is required to improve the exploration efficiency. The key idea of the proposed method is to understand the environmental map by deep learning so that a rover can select appropriate behavior mode according to the environment. The simulation study has been conducted to show the validity of the proposed method. The proposed method successfully demonstrates the capability to select behavior modes and to improve the traverse efficiency.