The article presents IRIS-U2, a novel algorithm for offline inspection planning that accounts for execution uncertainty. The key insights are:
Existing inspection planning algorithms do not explicitly consider execution uncertainty, which can lead to missed POIs and reduced efficiency.
IRIS-U2 extends the IRIS algorithm, a highly efficient approach for deterministic inspection planning, to the uncertain setting. It uses Monte Carlo sampling to estimate POI coverage probabilities, collision probabilities, and path lengths, and integrates these estimates within the search process to guide the exploration of the search space.
IRIS-U2 provides statistical guarantees on the desired performance criteria (coverage, collision probability, path length) within a user-specified confidence interval, which becomes tighter as the number of Monte Carlo samples increases.
The authors demonstrate the effectiveness of IRIS-U2 through a simulated case study of structural inspections of bridges using a UAV in an urban environment, showing improved expected coverage, reduced collision probability, and tighter statistical guarantees compared to prior work.
The authors also highlight the potential benefits of computing bounded sub-optimal solutions to reduce computation time while maintaining statistical guarantees.
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by Shmuel David... о arxiv.org 04-12-2024
https://arxiv.org/pdf/2309.06113.pdfГлибші Запити