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

Abstract

This paper introduces Mapping-based Tasks for Inspection: Discovery and Allocation (Map-TIDAL), a method for generating environmentally informed tasks and distributing them in a heterogeneous multi-robot system for visual inspection of underwater structures. Map-TIDAL leverages the individual robot maps generated during SLAM (without prior knowledge of the environment) and tasks from all the robots through a communication-aware auction process to determine additional inspection locations as the structures are further explored by the robots. This allows the method to adaptively focus on geometrically interesting areas that need detailed inspection while still maintaining good overall coverage with a reasonably small number of inspection tasks. Experiments on both saline and fresh water tanks show that Map-TIDAL yields better coverage while inspecting areas with interesting geometric features more thoroughly, using equal or fewer inspection locations compared to prevalent coverage methods using Voronoi distributions and boustrophedon patterns.