空氣汙染對人體健康造成危害，其細懸浮微粒會堆積在心肺，長期下來造成更高的死亡風險，然而管制空汙的成效受限於稽查、取締的效率，人員的蒐證在時間與空間上皆有所限制，因此，本研究提出了一套以無人機自動搜尋空汙排放源的方法，透過感測器網路的輔助資訊，結合無人機搜尋演算法來一步步接近排放源，並精準的將其定位出來。為了達到蒐證目的，必須能在空汙事件發生時即進行搜索，並在無人機續航力內完成搜尋，定位結果需要足夠的精準度以確定目標。我們使用LASS感測網路觀察即時的PM2.5資訊，在空汙目標區域取得背景濃度與過去一小時最高濃度作為指標，無人機依據指標來調整每一步的飛行距離，以在靠近排放源時增加搜尋密度，完成搜尋時，使用最高濃度的量測地點作為排放源定位結果。我們提出的飛行演算法一共有三種：(一)貪婪飛行法以鋸齒狀的飛行軌跡左右偵測並轉向濃度高的一側；(二)動態飛行法在行進前執行濃度取樣，判斷行進的方向，並依據濃度調整每一步的距離；(三)混和飛行法結合前兩者的優勢，透過門檻值調整兩者的切換時機。研究的實驗結果顯示，無人機能在20分鐘續航力內，完成距離誤差2公尺內的定位精準度。

Living in an environment filled with air pollution will affect our body health, cause chronic illness, and increase the fatality rate. However, it is difficult to regulated air pollution effectively, due to the need to accurately gathering evidence to prove any illegal emission. In this research, we propose a method to exploit the ability of a drone to locate air pollution (i.e., particulate matter) emission source quickly. Utilizing the information provided by an existing sensor network, the drone is able to make correct decisions when searching for pollution sources. In the proposed system, Location Aware Sensing System (LASS) provides the continuous monitoring information of PM 2.5 (Particulate Metter 2.5) to initialization searching plan by limiting a searching area. In the beginning, our drone utilizes the PM2.5 concentration information provided by LASS to adjust its searching direction and distance to an intermediate point. After an intermediate location point is reached, our drone will stop and sense the current PM2.5 concentration. Next, the drone continues to adjust the searching path with its searching direction and distance when the concentration level increased, respectively. The three searching path strategies are proposed - Greedy, Dynamic, and Hybrid Approach. The searching process repeats itself until ten of the continuous sensed PM2.5 concentration levels dropped below a threshold or the power of drone fell lower than the maximum flight time (with the reserved power for the return home distance). Once the searching process is finished, the location of the air pollution emission source is estimated by the highest contraction level measured by drone. The three of proposed strategies are compared with Random Way-Point and Space-Filling Curve. Experiment results show our proposed techniques are able to achieve estimation error below 2 meters within 20 minutes.

[ 1 ] Lilienthal, A., & Duckett, T. (2003, June). A stereo electronic nose for a mobile inspection robot. In Robotic Sensing, 2003. ROSE'03. 1st International Workshop on (pp. 6-pp). IEEE.
[ 2 ] Pashami, S., Asadi, S., & Lilienthal, A. J. (2010). Integration of openfoam flow simulation and filament-based gas propagation models for gas dispersion simulation. In Proceedings of the Open Source CFD International Conference (p. 2).
[ 3 ] Villa, T. F., Salimi, F., Morton, K., Morawska, L., & Gonzalez, F. (2016). Development and validation of a UAV based system for air pollution measurements. Sensors, 16(12), 2202.
[ 4 ] Villa, T. F., Gonzalez, F., Miljievic, B., Ristovski, Z. D., & Morawska, L. (2016). An overview of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors, 16(7), 1072.
[ 5 ] Tucker, W. G. (2000). An overview of PM2. 5 sources and control strategies. Fuel Processing Technology, 65, 379-392.
[ 6 ] Holmes, N. S., & Morawska, L. (2006). A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmospheric environment, 40(30), 5902-5928.
[ 7 ] Hutchinson, M., Oh, H., & Chen, W. H. (2017). A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion, 36, 130-148.
[ 8 ] Li, J. G., Meng, Q. H., Li, F., Zeng, M., & Popescu, D. (2009, December). Mobile robot based odor source localization via particle filter. In Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on (pp. 2984-2989). IEEE.
[ 9 ] Ishida, H., Wada, Y., & Matsukura, H. (2012). Chemical sensing in robotic applications: A review. IEEE Sensors Journal, 12(11), 3163-3173.
[ 10 ] Stachniss, C., Plagemann, C., Lilienthal, A. J., & Burgard, W. (2008). Gas distribution modeling using sparse Gaussian process mixture models. In Robotics: science and systems conference 2008, Zürich, Switzerland, June 25-28 (pp. 310-317). MIT press.
[ 11 ] Rossi, M., & Brunelli, D. (2016). Autonomous gas detection and mapping with unmanned aerial vehicles. IEEE Transactions on Instrumentation and Measurement, 65(4), 765-775.
[ 12 ] Croizé, P., Archez, M., Boisson, J., Roger, T., & Monsegu, V. (2015). Autonomous measurement drone for remote dangerous source location mapping. International Journal of Environmental Science and Development, 6(5), 391.
[ 13 ] Neumann, P., Bartholmai, M., Schiller, J. H., Wiggerich, B., & Manolov, M. (2010, October). Micro-drone for the characterization and self-optimizing search of hazardous gaseous substance sources: A new approach to determine wind speed and direction. In Robotic and Sensors Environments (ROSE), 2010 IEEE International Workshop on (pp. 1-6). IEEE.
[ 14 ] Neumann, P., Asadi, S., Schiller, J. H., Lilienthal, A. J., & Bartholmai, M. (2011). An artificial potential field based sampling strategy for a gas-sensitive micro-drone. In IROS Workshop on Robotics for Environmental Monitoring (WREM) (pp. 34-38).
[ 15 ] Neumann, P. P., Asadi, S., Bennetts, V. H., Lilienthal, A. J., & Bartholmai, M. (2013). Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs). Energy Procedia, 37, 4182-4190.
[ 16 ] Neumann, P. P., Hernandez Bennetts, V., Lilienthal, A. J., Bartholmai, M., & Schiller, J. H. (2013). Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms. Advanced Robotics, 27(9), 725-738.
[ 17 ] Han, J., & Chen, Y. (2014). Multiple UAV formations for cooperative source seeking and contour mapping of a radiative signal field. Journal of Intelligent & Robotic Systems, 74(1-2), 323-332.
[ 18 ] Özalp, N., & Sahingoz, O. K. (2013, May). Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms. In Unmanned Aircraft Systems (ICUAS), 2013 International Conference on (pp. 308-317). IEEE.
[ 19 ] Gade, S., & Joshi, A. (2013, December). Heterogeneous UAV swarm system for target search in adversarial environment. In Control Communication and Computing (ICCC), 2013 International Conference on (pp. 358-363). IEEE.
[ 20 ] Hafez, A. T., Givigi, S. N., Ghamry, K. A., & Yousefi, S. (2015, June). Multiple cooperative uavs target tracking using learning based model predictive control. In Unmanned aircraft systems (icuas), 2015 international conference on (pp. 1017-1024). IEEE.
[ 21 ] Craft, T. L., Cahill, C. F., & Walker, G. W. (2014, May). Using an unmanned aircraft to observe black carbon aerosols during a prescribed fire at the RxCADRE campaign. In Unmanned Aircraft Systems (ICUAS), 2014 International Conference on (pp. 77-82). IEEE.
[ 22 ] Cliff, O. M., Fitch, R., Sukkarieh, S., Saunders, D., & Heinsohn, R. (2015, July). Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System. In Robotics: Science and Systems.
[ 23 ] Zickler, S., & Veloso, M. (2010, May). RSS-based relative localization and tethering for moving robots in unknown environments. In Robotics and Automation (ICRA), 2010 IEEE International Conference on (pp. 5466-5471). IEEE.
[ 24 ] Bayram, H., Doddapaneni, K., Stefas, N., & Isler, V. (2016, August). Active localization of VHF collared animals with aerial robots. In Automation Science and Engineering (CASE), 2016 IEEE International Conference on (pp. 934-939). IEEE.
[ 25 ] Liu, C., Yang, J., & Wang, F. (2013). Joint TDOA and AOA location algorithm. Journal of Systems Engineering and Electronics, 24(2), 183-188.
[ 26 ] Dehghan, S. M. M., Moradi, H., & Shahidian, S. A. A. (2014, October). Optimal path planning for DRSSI based localization of an RF source by multiple UAVs. In Robotics and Mechatronics (ICRoM), Second RSI/ISM International Conference on IEEE(pp. 558-563).
[ 27 ] Shahidian, S. A. A., & Soltanizadeh, H. (2016). Optimal trajectories for two UAVs in localization of multiple RF sources. Transactions of the Institute of Measurement and Control, 38(8), 908-916.
[ 28 ] Kim, S., Kim, Y., Park, S. Y., Kim, R., & Hyuk, L. (2015, November). Poster: Drone Can Find Lost Smartphones. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (pp. 433-434). ACM.
[ 29 ] Chen, L. J., Ho, Y. H., Lee, H. C., Wu, H. C., Liu, H. M., Hsieh, H. H., Huang, Y. T. & Lung, S. C. C. (2017). An open framework for participatory PM2. 5 monitoring in smart cities. IEEE Access, 5, 14441-14454.
[ 30 ] U.S.EPA. (2010). Reference Method For The Determination Of Fine Particulate Matter As PM2.5 In The Atmosphere. 40 CFR Appendix L to Part 50.
[ 31 ] Schwartz, J., Dockery, D. W., & Neas, L. M. (1996). Is daily mortality associated specifically with fine particles?. Journal of the Air & Waste Management Association, 46(10), 927-939.