We present a framework for rapidly determining regions of interest (ROIs) from an unknown intensity distribution, particularly in radiation fields. The vast majority of studies on area coverage path planning for mobile robots do not investigate the identification of ROIs. In a radiation field, the use of ROIs can limit the required range of exploration and mitigate the monitoring problem. However, considering that an unmanned aerial vehicle (UAV) has limited resources as a mobile measurement system, it is challenging to determine ROIs in unknown radiation fields. Given a 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 a large-scale environment exploration, entire areas are initially adaptively decomposed using two hierarchical methods based on recursive quadratic subdivision and Voronoi-based subdivision. Once an informative decomposed subarea is selected by maximizing a utility function, the robot heuristically reaches contaminated areas, and a boundary estimation algorithm is adopted to estimate the environmental boundaries. The properties of this boundary estimation algorithm are theoretically analyzed in this paper. Finally, the detailed boundaries of the ROIs of the target area are approximated by ellipses, and a set of procedures are iterated to sequentially cover all areas. The simulation results demonstrate that our framework allows a single UAV to efficiently explore a given target area and maximize the localization rate for ROIs.

中文翻译：

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快速覆盖感兴趣的区域以进​​行环境监控

我们提出了一个框架，用于根据未知的强度分布快速确定感兴趣的区域（ROI），尤其是在辐射场中。关于移动机器人的区域覆盖路径规划的绝大多数研究都没有研究ROI的识别。在辐射场中，使用ROI可以限制所需的探测范围并减轻监控问题。但是，考虑到无人驾驶飞机（UAV）作为移动测量系统的资源有限，确定未知辐射场中的ROI面临挑战。在给定目标区域的情况下，我们尝试规划一条路径，以使用单个无人机促进ROI的定位，同时将勘探成本降至最低。为了减少大规模环境探索的复杂性，整个区域最初使用两种基于递归二次细分和基于Voronoi的细分的分层方法进行自适应分解。一旦通过最大化效用函数选择了信息丰富的分解区域，机器人就会启发式到达污染区域，然后采用边界估计算法来估计环境边界。从理论上分析了该边界估计算法的性质。最终，目标区域的ROI的详细边界由椭圆近似，并且迭代一组过程以依次覆盖所有区域。仿真结果表明，我们的框架允许单个无人机有效地探索给定的目标区域并最大化ROI的定位率。