Florian T. Pokorny
Assistant Professor, School of Computer Science and Communication, KTH Royal Institute of TechnologyData-Driven Topological Motion Planning with Persistent Cohomology
In Proceedings of Robotics: Science and Systems, 2015
Abstract
In this work, we present an approach to topological motion planning which is fully data-driven in nature and which
relies solely on the knowledge of samples in the free configuration space. For this purpose, we discuss the use of
persistent cohomology with coefficients in a finite field to compute a basis which allows us to efficiently solve the
path planning problem. The proposed approach can be used both in the case where a part of a configuration space is
well-approximated by samples and, more generally, with arbitrary filtrations arising from real-world data sets.
Furthermore, our approach can generate motions in a subset of the configuration space specified by the sub- or
superlevel set of a filtration function such as a cost function or probability distribution. Our experiments show that
our approach is highly scalable in low dimensions and we present results on simulated PR2 arm motions as well as GPS
trace and motion capture data.
Bibtex
@inproceedings{pokorny2015a,
author = {Pokorny, Florian T. and Kragic, Danica},
title = {Data-Driven Topological Motion Planning with Persistent Cohomology},
booktitle = {Proceedings of Robotics: Science and Systems},
year = {2015},
address = {Rome, Italy},
month = {July}
}