Plan-Time Multi-Model Switching for Motion Planning

Breelyn Kane Styler and Reid Simmons
Conference Paper, Proceedings of the Twenty Seventh International Conference on Automated Planning and Scheduling (ICAPS 2017), pp. 558 - 566, June, 2017

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Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are
promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable
within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan-time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.

author = {Breelyn Kane Styler and Reid Simmons},
title = {Plan-Time Multi-Model Switching for Motion Planning},
booktitle = {Proceedings of the Twenty Seventh International Conference on Automated Planning and Scheduling (ICAPS 2017)},
year = {2017},
month = {June},
pages = {558 - 566},
} 2018-03-08T10:33:17-04:00