David I. Ferguson

A system for volumetric robotic mapping of abandoned mines

This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.

Towards Topological Exploration of Abandoned Mines

The need for reliable maps of subterranean spaces too hazardous for humans to occupy has motivated the use of robotic technology as mapping tools. As such, we present a systemic approach to autonomous topological exploration of a mine environment to facilitate the process of mapping. This approach focuses upon the interaction of three high-level processes: topological planning, intersection identification and local navigation. Topological planning tasks the robot to investigate stretches of mine corridor for the purpose of collecting data. Intersection identification converts sensory input into topological components used to construct an online topological map and provide the robot with a global sense of position. Local navigation transforms topological exploration objectives into robot actuation enabling traversal of mine corridors. These processes are described in detail with results presented from experiments conducted at a research coal mine near Pittsburgh, PA.

Focussed processing of MDPs for path planning

We present a heuristic-based algorithm for solving restricted Markov decision processes (MDPs). Our approach, which combines ideas from deterministic search and recent dynamic programming methods, focusses computation towards promising areas of the state space. It is thus able to significantly reduce the amount of processing required to produce a solution. We demonstrate this improvement by comparing the performance of our approach to the performance of several existing algorithms on a robotic path planning domain.