A Simulation Study of Micro-Drone Chemical Plume Tracking Performance in Tree Farm Environments

  • Kok Seng EuEmail author
  • Kian Meng Yap
  • Wan Chew Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)


Chemical plume tracking (CPT) technology is the mean of tracking the flow of specific chemical plume in the air, to locate the source. Nowadays, CPT technology, for instance, a micro-drone based chemical plume tracking robot, has great potential in identifying hidden explosives, illegal drugs and blood for police and military purposes. However, environmental factors such as obstacles on site can change the wind vectors will cause inconsistent odor plume propagation. With most of the previous work conducted from numerous researchers carried out in empty open space, this paper studies the influence of obstacles on site towards CPT’s performance, which the simulation focus in one specific environment, a tree farm, with different density of trees or trees’ spacing. For this paper, we developed a 3D gas dispersion simulator with mobile robot olfaction (MRO) capability. Through the simulation, correlation between the impacts of tree farm density factor to CPT’s performance is found out, where higher tree density (or smaller tree spacing distance) can significantly reduce the performance of CPT. This study is an important fundamental contribution for drone’s CPT operation in agriculture application beneficial to future use, such as smell tracking of mature fruits in tree farm.


Chemical plume dispersion simulation Mobile robot olfaction Chemical plume tracking Tree farm environment 



This work was supported by the Sunway Internal Grant Scheme (Grant No: INT-FST-CIS-2016-03) at Sunway University, Malaysia.


  1. 1.
  2. 2.
    Lindsey, K.: 7 Things You Don’t Know about Moths. Accessed 5 Sept 2017
  3. 3.
    Jatmiko, W., Sekiyama, K., Fukuda, T.: A mobile robots PSO-based for odor source localization in dynamic advection-diffusion environment. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4527–4532 (2006)Google Scholar
  4. 4.
    Khaliq, A., Pashami, S., Schaffernicht, E., Lilienthal, A., Bennetts, V.H.: Bringing artificial olfaction and mobile robotics closer together – an integrated 3D gas dispersion simulator in ROS. In: 16th International Symposium on Electronic Nose, p. 78 (2015)Google Scholar
  5. 5.
    Cabrita, G., Sousa, P., Marques, L.: PlumeSim-player/stage plume simulator. In: ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments (2010)Google Scholar
  6. 6.
    Liu, Z., Lu, T.-F.: A simulation framework for plume-tracing research. In: Australasian Conference on Robotics and Automation, pp. 3–5 (2005)Google Scholar
  7. 7.
    Gong, D.W., Zhang, Y., Qi, C.-L.: Localising odour source using multi-robot and anemotaxis-based particle swarm optimisation. IET Control Theory Appl. 6, 1661 (2012)CrossRefGoogle Scholar
  8. 8.
    Monroy, J.G., Blanco, J.-L., González-Jiménez, J.: An open source framework for simulating mobile robotics olfaction. In: 15th International Symposium on Olfaction and Electronic Nose (ISOEN), pp. 2–3 (2013)Google Scholar
  9. 9.
    Villarreal, B.L., Olague, G., Gordillo, J.L.: Synthesis of odor tracking algorithms with genetic programming. Neurocomputing 175, 1019–1032 (2016)CrossRefGoogle Scholar
  10. 10.
    Gao, B., Li, H., Li, W., Sun, F.: 3D Moth-inspired chemical plume tracking and adaptive step control strategy. Adapt. Behav. 24, 52–65 (2016)CrossRefGoogle Scholar
  11. 11.
    Neumann, P.P., Bennetts, V.H., Lilienthal, A.J., Bartholmai, M.: From insects to micro air vehicles—a comparison of reactive plume tracking strategies. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds.) Intelligent Autonomous Systems 13. AISC, vol. 302, pp. 1533–1548. Springer, Cham (2016). doi: 10.1007/978-3-319-08338-4_110 CrossRefGoogle Scholar
  12. 12.
    Marjovi, A., Marques, L.: Multi-robot olfactory search in structured environments. Rob. Auton. Syst. 59, 867–881 (2011)Google Scholar
  13. 13.
    FAO Corporate Document Repository. Accessed 5 Sept 2017
  14. 14.
    Horticulture: Plantation Crops : Coconut - Spacing and Planting. Accessed 5 Sept 2017
  15. 15.
    Rubber Board: Land Preparation. Accessed 5 Sept 2017
  16. 16.
    Colella, P., Woodward, P.R.: The piecewise parabolic method (PPM) for gas-dynamical simulations. J. Comput. Phys. 54, 174–201 (1984)CrossRefzbMATHGoogle Scholar
  17. 17.
    Eu, K.S., Yap, K.M.: An exploratory study of quadrotor’s propellers impact using 3D gas dispersion simulator. In: The International Symposium on Olfaction and Electronic Nose (ISOEN), Canada (2017)Google Scholar
  18. 18.
    López, L.L.: Moth-like chemo-source localisation and classification on an indoor autonomous robot. In: On Biomimetics, pp. 453–466 (2005)Google Scholar
  19. 19.
    Li, W., Farrell, J.A., Cardé, R.T.: Tracking of fluid-advected odor plumes: strategies inspired by insect orientation to pheromone. Adapt. Behav. 9, 143–170 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Science and TechnologySunway UniversityPetaling JayaMalaysia

Personalised recommendations