Autonomous Synthesis of Goal-Oriented Behaviors for Planetary Robotic Sampling

Abstract

An intelligent robotic architecture that autonomously synthesizes goal-oriented behaviors, while connecting sensing and action in real-time, is presented with applications to loosely defined planetary sampling missions. This architecture is composed of perception and action nets interconneted in closed loops.