Autonomous inspection tasks necessitate effective path-planning mechanisms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty, posing challenges to the successful completion of such tasks. To tackle these challenges, we present IRIS-under uncertainty (IRIS-U^2), an extension of the incremental random inspection-roadmap search (IRIS) algorithm, that addresses the offline planning problem via an A*-based approach, where the planning process occurs prior the online execution. The key insight behind IRIS-U^2 is transforming the computed localization uncertainty, obtained through Monte Carlo (MC) sampling, into a POI probability. IRIS-U^2 offers insights into the expected performance of the execution task by providing confidence intervals (CI) for the expected coverage, expected path length, and collision probability, which becomes progressively tighter as the number of MC samples increase. 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 CIs as the number of MC samples grows. Furthermore, we emphasize the potential advantages of computing bounded sub-optimal solutions to reduce computation time while still maintaining the same CI boundaries.

翻译：自主检查任务需要有效的路径规划机制，以便从兴趣点高效收集观测数据。然而，城市环境中常见的定位误差会引入执行不确定性，给此类任务的顺利完带来挑战。为解决这些问题，我们提出IRIS-U²（不确定性下的增量随机检查路径搜索扩展算法），这是增量随机检查路径搜索的扩展，通过基于A*的方法解决离线规划问题，其中规划过程发生在在线执行之前。IRIS-U²的核心思想是将通过蒙特卡洛采样计算得到的定位不确定性转化为兴趣点概率。该算法通过提供预期覆盖率、预期路径长度和碰撞概率的置信区间，揭示执行任务的预期性能，且随着蒙特卡洛样本数量的增加，置信区间逐渐收紧。通过聚焦桥梁结构检查的案例研究，验证了IRIS-U²的有效性。我们的方法展现出更高的预期覆盖率、更低的碰撞概率，并随着蒙特卡洛样本数的增长生成更精确的置信区间。此外，我们强调计算有界次优解以减少计算时间、同时保持相同置信区间边界的潜在优势。