Bias-Tolerant Terrain Following Method for a Field Deployed Manipulator. Freese, M., Singh, S. P. N., Fukushima, E., & Hirose, S. May 2006. Paper abstract bibtex Manipulator operations are complicated by sensor noise, mechanical compliance, and system bias. These uncertainties are compounded in field environments, such as those encountered in humanitarian demining. By taking advantage of the generally flat and static structure of the terrain, a series of adaptive corrections and filters refine a sensed topographical model and generate a trajectory that is robust to inherent inaccuracies and modeling errors. Experimental testing on a mobile robot using stereo-vision as modeling sensor indicates that this method provides a tracking precision of �5 mm on relatively flat ground. As such, it will keep the attached mine sensors close to the ground, improving effectiveness.
@CONFERENCE{icra.2006,
author = {M. Freese and S. P. N. Singh and E. Fukushima and S. Hirose},
title = {{Bias-Tolerant Terrain Following Method for a Field Deployed Manipulator}},
booktitle = {International Conference on Robotics and Automation},
year = {2006},
pages = {175-180},
month = may,
abstract = {Manipulator operations are complicated by sensor noise, mechanical
compliance, and system bias. These uncertainties are compounded in
field environments, such as those encountered in humanitarian demining.
By taking advantage of the generally flat and static structure of
the terrain, a series of adaptive corrections and filters refine
a sensed topographical model and generate a trajectory that is robust
to inherent inaccuracies and modeling errors. Experimental testing
on a mobile robot using stereo-vision as modeling sensor indicates
that this method provides a tracking precision of �5 mm on relatively
flat ground. As such, it will keep the attached mine sensors close
to the ground, improving effectiveness.},
url = {http://ieeexplore.ieee.org/iel5/10932/34383/01641180.pdf?tp=&arnumber=1641180&isnumber=34383}
}
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By taking advantage of the generally flat and static structure of the terrain, a series of adaptive corrections and filters refine a sensed topographical model and generate a trajectory that is robust to inherent inaccuracies and modeling errors. Experimental testing on a mobile robot using stereo-vision as modeling sensor indicates that this method provides a tracking precision of �5 mm on relatively flat ground. As such, it will keep the attached mine sensors close to the ground, improving effectiveness.","url":"http://ieeexplore.ieee.org/iel5/10932/34383/01641180.pdf?tp=&arnumber=1641180&isnumber=34383","bibtex":"@CONFERENCE{icra.2006,\r\n author = {M. Freese and S. P. N. Singh and E. Fukushima and S. 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