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LexTOR: Lexicographic Teach Optimize and Repeat Based on User Preferences
Mladen Mazuran, Christoph Sprunk, Wolfram Burgard, Gian Diego Tipaldi Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA) 2015
mazuran15icra.pdf
Notes: In the last years, many researchers started to
consider teach-and-repeat approaches for reliable autonomous
navigation. The paradigm, in all its proposed forms, is deeply
rooted in the idea that the robot should autonomously follow a
route that has been demonstrated by a human during a teach
phase. However, human demonstrations are often inefficient
in terms of execution time or may cause premature wear
of the robot components due to jittery behavior or strong
accelerations. In this paper, we propose the concept of teach,
optimize and repeat, which introduces a trajectory optimization
step between the teach and repeat phases. To address this
problem, we further propose LexTOR, a constrained trajectory
optimization method for teach and repeat problems, where
the constraints are defined according to user preferences. At
its core, LexTOR optimizes both the execution time and the
trajectory smoothness in a lexicographic sense. The experiments
show that LexTOR is very effective, both qualitatively and
quantitatively, in terms of execution time, smoothness, accuracy
and bound satisfaction.
BibTeX:
@inproceedings{mazuran15icra,
author = {Mladen Mazuran and Christoph Sprunk and Wolfram Burgard and Gian Diego Tipaldi},
title = {Lex{TOR}: Lexicographic Teach Optimize and Repeat Based on User Preferences},
booktitle = {Proc.~of the IEEE Int.~Conf.~on Robotics \&
Automation (ICRA)},
year = 2015,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/mazuran15icra.pdf},
address = {Seattle}
}
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