Online High-Level Model Estimation for Efficient Hierarchical Robot Navigation

We would like to enable a robot to navigate efficiently and robustly in known, structured environments that are large enough to cause traditional planning approaches to incur considerable computational cost. Hierarchical planners are a promising way to increase planning efficiency in such environments because high-level abstract plans can be used to reduce the size of the search space over which detailed planning occurs. However, useful high-level representations of planning problems can be challenging to generate without prior domain knowledge. In this work, we propose a high-level planning representation which can be learned from previous plans considered in the environment and used online during hierarchical, multi-query robot navigation. We treat previous planning results as noisy measurements of high-level navigation properties, then update these properties over time using recursive estimation. We test our approach in standard and risk-aware hierarchical planning schemes, and demonstrate up to an 86% decrease in the number of nodes expanded and a 66% decrease in wallclock time as compared to a baseline A* planner while finding plans that are only 2-10% more expensive.