Robotic Introspection for Exploration and Mapping of Subterranean Environments

Abstract

This thesis identifies operational uncertainty as a significant problem affecting reliable robot performance in real environments. Operational uncertainty represents ambiguity in a robot's self-perceived state during the execution of a task. This ambiguity is introduced into a robotic system through events such as unanticipated environmental disturbances, failing hardware and software built upon invalid assumptions. Real environments such as forests, caves, oceans and space foster operational ambiguity, which confound robot software and hinder reliable performance. To address operational uncertainty in the general case, a robot is required to assess both the environment and itself to determine the nature of a problem and the appropriate means to react. Environmental assessment (i.e. perception) is a well-understood and highly addressed problem in robotics research. As such, this thesis focuses upon the latter topic: self assessment. This thesis develops a framework called robotic introspection to provide a self-assessment mechanism for field-capable robots. Robotic introspection models and monitors operational state (i.e. a robot's computational state) to assist robotic decision-making. In particular, this research develops an architectural framework for observing, mapping, localizing and planning in the space of operating modes. For this thesis, the subterranean domain is used to describe and illustrate the problem of operational uncertainty and to implement and experiment with robotic introspection. This domain is an ideal medium for conveying these concepts and generalizes well to robots op- erating in other field environments. In addition, the subterranean domain offers exceptional opportunities for highly-reliable robots. Hazardous, remote and space constrained under- ground spaces such as mines, tunnels, caves and sewers are difficult environments for people to access and labor. Information acquired from the subterranean, however, has immense civil and commercial value. Compact, sensory-tailored robotic systems provide practical solutions to subterranean information-gathering efforts by reaching spaces and collecting data on a scale that is not humanly feasible. The work presented in this document is the first to develop robotic introspection for autonomy on a field robotic platform. This introspective framework is shown to effectively handle uncertain situations, thereby justifying its role on autonomous subterranean robots. In addition, this work provides irrefutable evidence establishing robots as capable, thorough, and efficient tools for subterranean data collection. Unique to this research, trials of robot deployment have occurred in an assortment of underground environments across a spectrum of conditions including flooded, dry, muddy, confined, open, smoke-filled, borehole entry, and portal entry. From these trials, this work has produced an unrivaled repository of subterranean data, including the largest 3-D metric models of interior underground surfaces known to date