Abstract
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.
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Acknowledgements
The authors would like to thank Ministry of Education, Culture, Sport, Science and Technology (MEXT) - Japan, for the financial support through MEXT scholarship. In addition, this work was supported by the Industrial Convergence Core Technology Development Program (No. 10063172) funded by MOTIE, Korea.
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Pinkam, N., Newaz, A.A.R., Jeong, S., Chong, N.Y. (2019). Rapid Coverage of Regions of Interest for Environmental Monitoring. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_17
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DOI: https://doi.org/10.1007/978-3-319-78452-6_17
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