Autonomous Adaptive Exploration using Online Topic Modelling

Exploration of dangerous environments, such as underwater coral reefs and shipwrecks, is a difficult and potentially life threatening tasks for humans, which naturally makes the use of an autonomous robotic system very appealing. Exploration through the use of an autonomous agent can find uses in many different scenarios.This paper presents such an autonomous system, which is capable of autonomous exploration, and shows its use in a series of experiments to collect image data in challenging underwater marine environments. We presents novel contributions on three fronts. First, we present an online topic-modeling based technique to describe what is being observed using a low dimensional semantic descriptor. This descriptor attempts to be invariant to observations of different corals belonging to the same species, or observations of similar types rocks observed from different viewpoints. Second, we use the topic descriptor to compute the surprise score of the current observation. This is done by maintaining an online summary of observations thus far, and then computing the surprise score as the distance of the current observation to the summary, in the topic space. Finally, we present a novel control strategy for an underwater robot thats allows for intelligent traversal; hovering over surprising observations, and swimming quickly over previously seen corals and rocks.