基于SLAM数据自动发现并分配水下机器人巡检任务，适应未知结构。
Mapping-Guided Task Discovery and Allocation for Robotic Inspection of Underwater Structures
- 利用SLAM网格生成任务，结合硬件与环境参数优化
- 通过关键点评分和距离剪枝提升覆盖率与效率
- 适合复杂多变的水下结构巡检，尤其缺陷高发区
在缺乏现有几何信息的情况下，可通过分析同时定位与地图构建（SLAM）数据，实现水下多机器人巡检任务的生成与优化。结合硬件参数和环境条件，从SLAM网格中生成一组任务，并通过预期关键点得分和基于距离的剪枝进行优化。通过在水下测试验证算法有效性并确定合适参数。结果与模拟的Voronoi分割和蛇形扫描模式在测试环境模型上的覆盖效果进行对比。所提任务发现方法的主要优势在于对意外几何结构的适应性，能在保持全覆盖的同时，聚焦于更可能存有缺陷或损伤的区域。
原文摘要 · Abstract (English)
Task generation for underwater multi-robot inspections without prior knowledge of existing geometry can be achieved and optimized through examination of simultaneous localization and mapping (SLAM) data. By considering hardware parameters and environmental conditions, a set of tasks is generated from SLAM meshes and optimized through expected keypoint scores and distance-based pruning. In-water tests are used to demonstrate the effectiveness of the algorithm and determine the appropriate parameters. These results are compared to simulated Voronoi partitions and boustrophedon patterns for inspection coverage on a model of the test environment. The key benefits of the presented task discovery method include adaptability to unexpected geometry and distributions that maintain coverage while focusing on areas more likely to present defects or damage.
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