Kinodynamic Motion Planning on Gaussian Mixture Fields

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Luigi Palmieri, Tomasz Kucner, Martin Magnusson, Achim J. Lilienthal, and Kai O. Arras
Kinodynamic Motion Planning on Gaussian Mixture Fields
IEEE International Conference on Robotics and Automation (ICRA), 2017

Abstract

We present a mobile robot motion planning approach under kinodynamic constraints that exploits learned perception priors in the form of continuous Gaussian mixture fields. Our Gaussian mixture fields are statistical multi-modal motion models of discrete objects or continuous media in the environment that encode e.g. the dynamics of air or pedestrian flows. We approach this task using a recently proposed circular linear flow field map based on semi-wrapped GMMs whose mixture components guide sampling and rewiring in an RRT* algorithm using a steer function for non-holonomic mobile robots. In our experiments with three alternative baselines, we show that this combination allows the planner to very efficiently generate high-quality solutions in terms of path smoothness, path length as well as natural yet minimum control effort motions through multi-modal representations of Gaussian mixture fields.

@InProceedings{palmieri2017kinodynamic,
 author = {Luigi Palmieri and Tomasz Kucner and Martin Magnusson and Achim J. Lilienthal and Kai O. Arras},
 title = {Kinodynamic Motion Planning on {G}aussian Mixture Fields},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2017},
 pages = {6176--6181},
 month = may,
 }