Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments

TL;DR

This paper compares information-theoretic and coverage-based volumetric objectives for multi-robot exploration in 3D environments, revealing coverage can outperform information methods and demonstrating efficient distributed planning with multiple robots.

Contribution

It provides the first comparison between information-based and coverage-based exploration objectives and extends randomized sequential partitioning to both classes for multi-robot systems.

Findings

Coverage objectives outperform information-based objectives in practice.

Randomized Sequential Partitions apply to both objective types.

Simulations with up to 32 robots demonstrate scalability.

Abstract

Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions -- a method for efficient distributed sensor planning -- applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.