Inspection planning under execution uncertainty

TL;DR

This paper introduces IRIS-U^2, an innovative inspection planning algorithm that accounts for execution uncertainty, providing statistical guarantees on coverage, path length, and collision probability, demonstrated through bridge inspection case studies.

Contribution

It extends the deterministic IRIS framework to uncertain environments using Monte Carlo sampling, offering the first inspection planning method with statistical guarantees under localization errors.

Findings

IRIS-U^2 improves expected coverage in uncertain conditions.

It reduces collision probability compared to existing methods.

The approach's statistical guarantees improve with more Monte Carlo samples.

Abstract

Autonomous inspection tasks necessitate path-planning algorithms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty, posing challenges to successfully completing such tasks. Unfortunately, existing algorithms for inspection planning do not explicitly account for execution uncertainty, which can hinder their performance. To bridge this gap, we present IRIS-under uncertainty (IRIS-U^2), the first inspection-planning algorithm that offers statistical guarantees regarding coverage, path length, and collision probability. Our approach builds upon IRIS -- our framework for deterministic inspection planning, which is highly efficient and provably asymptotically-optimal. The extension to the much more involved uncertain setting is achieved by a refined search procedure that estimates POI coverage probabilities using Monte Carlo (MC) sampling. The efficacy of IRIS-U^2 is demonstrated through a case study focusing on structural inspections of bridges. Our approach exhibits improved expected coverage, reduced collision probability, and yields increasingly precise statistical guarantees as the number of MC samples grows. Furthermore, we demonstrate the potential advantages of computing bounded sub-optimal solutions to reduce computation time while maintaining statistical guarantees.