Abstract：In order to facilitate research on odor source localization or odor plume mapping, a three-dimensional (3D) robot active olfaction simulator (RAOS) is designed. RAOS is mainly designed for the active olfaction research of rotor drones, and also supports the simulation of ground mobile robots. It can mainly simulate 3D scenes, robots, wind fields, odor diffusion and sensors. 3D scenes are generated by computer-aided design software and then imported into the simulator. The free wind field is simulated with a computational fluid dynamics (CFD) software. The wake-induced wind field is calculated based on the aero-olfactory effect model of rotor drone, where a fully-connected network is introduced to approximate the induced wind field, which can improve the real-time calculation. The odor diffusion is simulated by the environmental wind field and the filament diffusion model in CFD. A simplified simulation model is proposed for the tunable diode laser absorption spectroscopy (TDLAS) sensor. To verify the consistency of the odor diffusion in the simulator and in real environment, two criteria (Frechet distance and the earth mover’s distance) are employed to quantitatively evaluate the similarities of odor diffusion profile and concentration distribution, respectively. Kolmogorov-Smirnov (KS) test is introduced for consistency determination. By comparing with the actual experiment and wind tunnel data, the consistency of the odor plume distribution characteristics between RAOS and real environment is verified, which shows that RAOS can provide a consistent and repeatable simulation platform for the active olfaction research in 3D environments.
 孟庆浩，李飞. 主动嗅觉研究现状[J].机器人， 2006， 28(1)： 89-96. Meng Q H, Li F. Review of active olfaction[J]. Robot, 2006, 28(1): 89-96.  Chen X X, Huang J. Odor source localization algorithms on mobile robots: A review and future outlook[J]. Robotics and Autonomous Systems, 2019, 112: 123-136.  Burgués J, Hernández V, Lilienthal A J, et al. Smelling nano aerial vehicle for gas source localization and mapping[J]. Sensors, 2019, 19(3): 478-502.  Chen X X, Huang J. Combining particle filter algorithm with bio-inspired anemotaxis behavior: A smoke plume tracking method and its robotic experiment validation[J]. Measurement, 2020, 154. DOI: 10.1016/j.measurement.2020.107482.  骆德汉，邹宇华，庄家俊. 基于修正蚁群算法的多机器人气味源定位策略研究[J].机器人， 2008， 30(6)： 536-541. Luo D H, Zou Y H, Zhuang J J. Multi-robot odor source localization strategy based on a modified ant colony algorithm[J]. Robot, 2008, 30(6): 536-541.  李吉功，杨静，周洁勇，等. 室外环境下基于证据理论的多气味源测绘及定位[J].机器人， 2019， 41(6)： 771-778, 787. Li J G, Yang J, Zhou J Y, et al. Mapping and localization of multiple odor sources with evidence theory in outdoor environments[J]. Robot, 2019, 41(6): 771-778,787.  张思齐，崔荣鑫，徐德民. 稀疏环境中信息趋向性搜索算法性能分析[J].机器人， 2013， 35(4)： 432-438. Zhang S Q, Cui R X, Xu D M. Performance analysis on the infotaxis algorithm for searching in dilute environments[J]. Robot, 2013, 35(4): 432-438.  Farrell J A, Murlis J, Long X, et al. Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes[J]. Environmental Fluid Mechanics, 2002, 2: 143-169.  Pashami S, Asadi S, Lilienthal A. Integration of OpenFOAM flow simulation and filament-based gas propagation models for gas dispersion simulation[C/OL]//Open Source CFD International Conference. 2010. [2020-07-01]. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.8455.  Jiang X B, Meng Q H, Wang Y, et al. Numerical simulation of odor plume in indoor ventilated environments for studying odor source localization with mobile robots[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, USA: IEEE, 2012: 1029-1033.  Sutton J, Li W. Development of CPT M3D for multiple chemical plume tracing and source identification[C]//7th International Conference on Machine Learning and Applications. Piscataway, USA: IEEE, 2008: 470-475.  Monroy J, Hernandez-Bennetts V, Fan H, et al. GADEN: A 3D gas dispersion simulator for mobile robot olfaction in realistic environments[J]. Sensors, 2017, 17(7): 1479-1494.  Eu K S, Yap K M. An exploratory study of quadrotor’s propellers impact using 3D gas dispersion simulator[C]//ISOCS/ IEEE International Symposium on Olfaction and Electronic Nose. Piscataway, USA: IEEE, 2017: 1-3.  Eu K S, Yap K M. Chemical plume tracing: A threedimensional technique for quadrotors by considering the altitude control of the robot in the casting stage[J]. International Journal of Advanced Robotic Systems, 2018, 15(1). DOI: 10. 1177/1729881418755877.  Luo B, Meng Q H, Wang J Y, et al. Simulate the aerodynamic olfactory effects of gas-sensitive UAVs: A numerical model and its parallel implementation[J]. Advances in Engineering Software, 2016, 102: 123-133.  Alt H, Godau M. Computing the Fréhet distance between two polygonal curves[J]. International Journal of Computational Geometry & Applications, 1995, 5(1/2): 75-91.  Rubner Y, Tomasi C, Guibas L J. The earth mover’s distance as a metric for image retrieval[J]. International Journal of Computer Vision, 2000, 40(2): 99-121.  Hornik K. Approximation capabilities of multilayer feedforward networks[J]. Neural Networks, 1991, 4(2): 251-257.  Ma Y J, Yu D H, Wu T, et al. PaddlePaddle: An open-source deep learning platform from industrial practice[J]. Frontiers of Data and Computing, 2019, 1(1): 105-115.  Crimaldi J P, Wiley M B, Koseff J R. The relationship between mean and instantaneous structure in turbulent passive scalar plumes[J]. Journal of Turbulence, 2002, 3. DOI: 10.1088/1468- 5248/3/1/014.  Neumann P P, Kohlhoff H, Hullmann D, et al. Bringing mobile robot olfaction to the next dimension – UAV-based remote sensing of gas clouds and source localization[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2017: 3910-3916.  Vergara A, Fonollosa J, Mahiques J, et al. On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines[J]. Sensors and Actuators B: Chemical, 2013, 185: 462-477.  Vergara A, Fonollosa J, Trincavelli M, et al. Gas sensor arrays in open sampling settings data set[DB/OL]. [2020-07-01]. https://archive.ics.uci.edu/ml/datasets/Gas+sensor+arrays+in+open+sampling+settings.  TJU-IRAS. RAOS – Simulation framework for robot active olfaction[DB/OL]. [2020-07-01]. https://github.com/TJU-IRAS/RAOS.