An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction

TL;DR

This paper presents an information-theoretic method combining low rank modeling and path planning to efficiently monitor large environments with robots, predicting unobserved regions from limited samples.

Contribution

It introduces a novel approach that integrates low rank models with information-maximizing path planning for environment monitoring, applicable across various attributes and platforms.

Findings

Outperforms baseline samplers in Fisher information gain

Achieves comparable reconstruction error to baselines

Effective in large-scale real-world datasets

Abstract

Robots can be used to collect environmental data in regions that are difficult for humans to traverse. However, limitations remain in the size of region that a robot can directly observe per unit time. We introduce a method for selecting a limited number of observation points in a large region, from which we can predict the state of unobserved points in the region. We combine a low rank model of a target attribute with an information-maximizing path planner to predict the state of the attribute throughout a region. Our approach is agnostic to the choice of target attribute and robot monitoring platform. We evaluate our method in simulation on two real-world environment datasets, each containing observations from one to two million possible sampling locations. We compare against a random sampler and four variations of a baseline sampler from the ecology literature. Our method outperforms the baselines in terms of average Fisher information gain per samples taken and performs comparably for average reconstruction error in most trials.