SUBSYMBOLIC PATH-PLANNING FOR SPACE AND PLANETARY EXPLORATION ROBOTS

摘要

Space and planetary exploration and construction can be significantly facilitated by the use of robotic technology, which can provide low risk and low cost means. Especially for remote missions, it seems necessary to use robots able to autonomously pursue mission goals specified beforehand by humans. One key competence that such an autonomous robotic agent should possess is the ability to plan sequences of activities for achieving mission goals. Current technologies base such planning skills on human intervention and/or a priori obtained maps of the operation environment. However, missions to remote unexplored planetary objects will not always have these privileges of pre-supplied maps of the landing and operation terrain and of frequent communication with human operators on earth. On the contrary we need robots able, after landing, to adapt to their environment and learn how to successfully operate in it autonomously for achieving pre-specified mission goals. Achieving such degrees of autonomy requires two capabilities of the robots: first, to be able to learn how to operate in a new environment and second, to be able to generalize over their experience in order to behave properly in novel situations by exploiting previous experience.