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.

This paper presents a real-time implementation of a topological localization method based on matching image features. This work is supported by a unique sensor pod design that provides stand-alone sensing and computing for localizing a vehicle on a previously traveled road. We report extensive field test results from outdoor environments, with the sensor pod mounted on both a small and a large all-terrain vehicle. Off-line analysis of the approach is also presented to evaluate the robustness of the various image features tested against different weather and lighting conditions.

Unknown, unexplored and abandoned subterranean voids threaten mining operations, surface developments and the environment. Hazards within these spaces preclude human access to create and verify extensive maps or to characterize and analyze the environment. To that end, we have developed a mobile robot capable of autonomously exploring and mapping abandoned mines. To operate without communications in a harsh environment with little chance of rescue, this robot must have a robust electro-mechanical platform, a reliable software system, and a dependable means of failure recovery. Presented are the mechanisms, algorithms, and analysis tools that enable autonomous mine exploration and mapping along with extensive experimental results from eight successful deployments into the abandoned Mathies coal mine near Pittsburgh, PA.

Outlined is a new approach to sensor space planning and its application to the construction industry. The software planning tool described here generates sensor placements automatically for use in assessing deviations in construction environments. The first step is to separate construction information goals into clusters, simplifying the planning space in order to reduce computational complexity. For each cluster, the planner generates the space of potential sensor placements for a set of information goals and selects a minimal set of subspaces to take advantage of views that can achieve multiple goals simultaneously. Sensing locations are chosen that maximize the probability of achieving each goal and a path is generated to minimize the transit cost between the various sensing locations within each cluster. Finally, paths are generated that minimize the transit cost between clusters. This method is demonstrated on a desktop computer and shown to support LIDAR information goal sensor planning within a construction site.

We present a robust method for detecting and recognizing topological features in underground mines. Our method involves performing Delaunay triangulations on range scans to extract points of interest, such as intersecting corridors. By combining these interest points into a topological map, we have a valuable tool for navigation and localization in large scale, highly cyclic environments. We present results from a research coal mine near Pittsburgh, PA.

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.

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random £elds. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy.

A new concept for coordinating large-scale autonomous robot exploration teams having populations in the thousands is introduced along with several illustrative scenarios. Inspired by constructs found within the human immune system, the Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) was developed so that exploratory actions are refined and followed by specific and mediated responses. The foundations of the IDARA model are derived from the immune network model; in particular, interactions derived from the innate immune system are integrated to yield a stronger first-order, all-purpose search strategy. Using this architecture as a foundation, the work develops methods for kilorobotic exploration in dynamic environments. As characterized via computer simulations with robot populations of up to 1,500 robots, IDARA-based exploration proved to be a robust and compact method for largescale multirobot coordination that combines reflexive and deliberative control methods in an opportunistic fashion, mimicking the human immune system.

Presents an approach to efficient multirobot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to being able to withstand communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are given.

This paper presents a new concept called the Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) for the manipulation of "kilorobots" (large multi-robot colonies) modeled on the actions of the human immune system. The paper presents the development of the IDARA algorithms for the control and coordination of kilorobots for robot exploration tasks in four mapping scenarios. As characterized via computer simulations with robot populations of up to 1,500, IDARA-based exploration proved to be an efficient, robust, and compact method for large-scale multirobot control that combines the speed of reflexive methods with the precision of deliberative control.

We present a stereoscopic mapping system for use in post-nuclear accident operations by the Pioneer robot. First we discuss a radiation shielded sensor array designed to tolerate extended cumulative dose using 4/spl times/ shielding. Next, we outline procedures to ensure timely, accurate range estimation using trinocular stereo. Finally, we review the implementation of a system for the integration of range information into a 3-D, textured, metrically accurate surface mesh.

A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of stereo-ranging, particularly those involving manipulation. Given these properties, a solution that selects the set of optimal design parameters, in the sense of accuracy, under the given constraints is presented. Results of this algorithm are discussed and analyzed in terms of the optimality of the minimum-area technique used in the derivation.