Mapping-Guided Task Discovery and Allocation for Robotic Inspection of Underwater Structures

TL;DR

This paper presents a method for autonomous underwater multi-robot inspection that generates and optimizes inspection tasks using SLAM data, adaptable to unknown geometries and environmental conditions.

Contribution

It introduces a novel task discovery and allocation approach based on SLAM meshes, optimizing coverage and defect detection without prior environment knowledge.

Findings

Effective in-water validation demonstrating adaptability to unknown geometries.

Outperforms traditional coverage patterns like Voronoi and boustrophedon in coverage efficiency.

Maintains high coverage while focusing on defect-prone areas.

Abstract

Task generation for underwater multi-robot inspections without prior knowledge of existing geometry can be achieved and optimized through examination of simultaneous localization and mapping (SLAM) data. By considering hardware parameters and environmental conditions, a set of tasks is generated from SLAM meshes and optimized through expected keypoint scores and distance-based pruning. In-water tests are used to demonstrate the effectiveness of the algorithm and determine the appropriate parameters. These results are compared to simulated Voronoi partitions and boustrophedon patterns for inspection coverage on a model of the test environment. The key benefits of the presented task discovery method include adaptability to unexpected geometry and distributions that maintain coverage while focusing on areas more likely to present defects or damage.