実行不確実性下における検査計画
Inspection planning under execution uncertainty
位置推定誤差による実行不確実性を考慮し、被覆率・経路長・衝突確率の統計的保証を提供する初の検査計画アルゴリズムIRIS-U²を提案し、橋梁構造検査の事例で有効性を示した。
著者: Shmuel David Alpert, Kiril Solovey, Itzik Klein, Oren Salzman
分類: cs.RO
原文アブストラクト
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.
