Organizational Unit:
Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link
https://hdl.handle.net/1853/70657
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Includes Organization(s)
Organizational Unit
Organizational Unit
ArchiveSpace Name Record

Filters

2011 - 2013 4
4
3 1
4
4
Reset filters

Settings
Author: Howard, Ayanna M. × Author: Parker, Lonnie T. × search.filters.applied.f.resourcesubtype: Post-print ×

Publication Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Estimation-informed, Resource-aware Robot Navigation for Environmental Monitoring Applications
    (Georgia Institute of Technology, 2013-05) Parker, Lonnie T. ; Coogle, Richard A. ; Howard, Ayanna M.
    Environmental monitoring of spatially-distributed geo-physical processes (e.g., temperature, pressure, or humidity) requires efficient sampling schemes, particularly, when employing an autonomous mobile agent to execute the sampling task. Many approaches have considered optimal sampling strategies which specialize in minimizing estimation error, while others emphasize reducing resource usage, yet rarely are both of these performance parameters used concurrently to influence the navigation. This work discusses how a spatial estimation process and resource awareness are integrated to generate an informed navigation policy for collecting useful measurement information. We also enable a direct comparison between this informed navigation method and more common approaches using two performance metrics. We show that our informed navigation outperforms these approaches based on performance evaluation as a function of estimation error and resource usage for a useful range of coverage within the sampling area.
  • Thumbnail Image
    Item
    Terrain Reconstruction of Glacial Surfaces via Robotic Surveying Techniques
    (Georgia Institute of Technology, 2012-12) Williams, Stephen ; Parker, Lonnie T. ; Howard, Ayanna M.
    The capability to monitor natural phenomena using mobile sensing is a benefit to the Earth science community given the potentially large impact that we, as humans, can have on naturally occurring processes. Observable phenomena that fall into this category of interest range from static to dynamic in both time and space (i.e. temperature, humidity, and elevation). Such phenomena can be readily monitored using networks of mobile sensor nodes that are tasked to regions of interest by scientists. In our work, we hone in on a very specific domain, elevation changes in glacial surfaces, to demonstrate a concept applicable to any spatially distributed phenomena. Our work leverages the sensing of a vision-based SLAM odometry system and the design of robotic surveying navigation rules to reconstruct scientific areas of interest, with the goal of monitoring elevation changes in glacial regions. We validate the output from our methodology and provide results that show the reconstructed terrain error complies with acceptable mapping standards found in the scientific community.
  • Thumbnail Image
    Item
    Real Conversion of GIS Contour Maps into Surface Disigital Elevation Models for Robotic Surveying
    (Georgia Institute of Technology, 2011-10) Mei, Henry (Heqing) ; Parker, Lonnie T. ; Howard, Ayanna M.
    With the advent of new technologies, robotic surveying systems are being developed to facilitate the collection of ground-based information to validate and complement data collected by traditional and satellite-based instruments. The development of such systems necessitates an accurate set of reference data. Given the limitations of current in situ measurement methods to aid remote sensing, this paper outlines a method for the creation of three-dimensional data from the most common public data source, 2D contour maps. Using image processing and interpolation techniques, this method was first tested against data collected by a robotic survey system and against methods that a human expert would use. Comparatively, our method yielded vertical RMSE in the range of (0.006066 - 0.39) [m] for different horizontal spatial resolutions. Twenty additional sample contour maps were identified to further vet our method against that of a human expert as a function of the 3D interpolation method selected. These tests provided errors in the centimeter range and also revealed that the linear triangular mesh interpolator is the best choice for this type of image input data.
  • Thumbnail Image
    Item
    Adaptive Robot Navigation Protocol for Estimating Variable Terrain Elevation Data
    (Georgia Institute of Technology, 2011-10) Parker, Lonnie T. ; Howard, Ayanna M.
    Efficiently measuring environmental phenomena (e.g., elevation, chemical composition, and mineral density) is a task typically reserved for the geoscience community. Recent robotic systems with the potential for addressing the task of sampling currently exist, yet their navigation strategies (and subsequently sampling strategies) are seldom a function of the spatial change in the measured phenomena of interest. Solutions are especially void for intelligent systems to which resource constraints are applied (i.e., battery power and experimentation time) while complete coverage of an area is expected. In this paper, we discuss the implementation of a custom navigation strategy based on immediately-sensed data that, when combined with spatial interpolation techniques, yields a re-creation of the surveyed space with root mean squared error that meets accepted mapping standards. Our methodology employs an adaptive coverage algorithm which succeeds in lowering the RMS error when compared to other navigation techniques. Our results are validated in simulation by considering: 1) randomly-generated terrains and 2) realistic digital elevation map (DEM) data transposed from publically available terrain contour maps.