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Rapid Coverage of Regions of Interest for Environmental Monitoring

机译：对环境监测感兴趣区域的快速覆盖

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In this paper, we present a framework to solve the problem of rapidly determining regions of interest (ROIs) from an unknown intensity distribution, especially in radiation fields. The vast majority of existing literature on robotics area coverage does not report the identification of ROIs. In a radiation field, ROIs limit the range of exploration to mitigate the monitoring problem. However, considering the limited resources of Unmanned Aerial Vehicle (UAV) as a mobile measurement system, it is challenging to determine ROIs in unknown radiation fields. Given the target area, we attempt to plan a path that facilitates the localization of ROIs with a single UAV, while minimizing the exploration cost. To reduce the complexity of exploration of large scale environment, initially whole areas are adaptively decomposed by the hierarchical method based on Voronoi based subdivision. Once an informative decomposed sub area is selected by maximizing a utility function, the robot heuristically reaches to contaminated areas and then a boundary estimation algorithm is adopted to estimate the environmental boundaries. Finally, the detailed boundaries are approximated by ellipses, called the ROIs of the target area and whole procedures are iterated to sequentially cover the all areas. The simulation results demonstrate that our framework allows a single UAV to efficiently and explore a given target area to maximize the localization rate of ROIs.
机译：在本文中，我们提出了一个框架来解决从未知强度分布的迅速确定感兴趣区域（ROI），特别是在辐射场中的问题。绝大多数现有文献在机器人面积覆盖范围内没有报告罗伊斯的识别。在辐射场中，ROI限制了减轻监测问题的探索范围。然而，考虑无人驾驶飞行器（UAV）作为移动测量系统的有限资源，确定在未知的辐射场中的乐河挑战。鉴于目标区域，我们试图规划一条促进ROI本地化的路径，同时最大限度地减少勘探成本。为了降低大规模环境探索的复杂性，最初通过基于Voronoi基于细分的分层方法自适应地分解。一旦通过最大化公用事业功能选择了信息分解的子区域，机器人启发式地达到污染区域，然后采用边界估计算法来估计环境边界。最后，详细边界由椭圆近似，称为目标区域的ROI和整个过程迭代以顺序地覆盖所有区域。仿真结果表明，我们的框架允许单个UAV有效地探索给定的目标区域，以最大化ROI的定位率。

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