Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments
This work addresses exploration efficiency for robotics in unknown 3D spaces, presenting an incremental analysis of existing methods.
The paper tackles the problem of multi-robot exploration in 3D environments by comparing volumetric objectives, finding that coverage objectives outperform information-based ones in practice, with simulation results for up to 32 robots.
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.