# Environment Model Adaptation for Mobile Robot Exploration

In this work, we propose a methodology to adapt a mobile robot’s
environment model during exploration as a means of decreasing the
computational complexity associated with information metric evaluation
and consequently increasing the speed at which the system is able to
plan actions and travel through an unknown region given finite
computational resources. Recent advances in exploration compute
control actions by optimizing information-theoretic metrics on the
robot’s map. These metrics are generally computationally expensive to
evaluate, limiting the speed at which a robot is able to explore. To
reduce computational cost, we propose keeping two representations of
the environment: one full resolution representation for planning and
collision checking, and another with a coarse resolution for rapidly
evaluating the informativeness of planned actions. To generate the
coarse representation, we employ the Principal of Relevant Information
from rate distortion theory to compress a robot’s occupancy grid
map. We then propose a method for selecting a coarse representation
that sacrifices a minimal amount of information about expected future
sensor measurements using the Information Bottleneck Method. We
outline an adaptive strategy that changes the robot’s environment
representation in response to its surroundings to maximize the
computational efficiency of exploration. On computationally
constrained systems, this reduction in complexity enables planning
over longer predictive horizons, leading to faster navigation. We
simulate and experimentally evaluate mutual information based
exploration through cluttered indoor environments with exploration
rates that adapt based on environment complexity leading to an
order-of-magnitude increase in the maximum rate of exploration in
contrast to non-adaptive techniques given the same finite
computational resources.

## People

PhD, 2020. Now a Postdoctoral Fellow in the Air Lab at Carnegie Mellon University.