Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics

Philip A. Frederick; Bernard L. Theisen; Derek Ward

doi:10.1117/12.685390

2 October 2006 Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics

Philip A. Frederick, Bernard L. Theisen, Derek Ward

Proceedings Volume 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision; 638405 (2006) https://doi.org/10.1117/12.685390
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.

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Philip A. Frederick, Bernard L. Theisen, and Derek Ward "Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics", Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 638405 (2 October 2006); https://doi.org/10.1117/12.685390

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KEYWORDS

Roads

Databases

Earth observing sensors

Vegetation

Data modeling

Robotics

Unmanned ground vehicles

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