3D Moth-inspired chemical plume tracking and adaptive step control strategy

Abstract

This paper presents an adaptive-step-control (ASC) strategy for autonomous chemical plume tracking (CPT) in both 2D and 3D spaces. The aim of CPT is tracking a target chemical flow back to its source and declaring its location. While the majority of related works are based on wheeled robots in 2D, the rapid development of rotorcrafts makes 3D plume tracking possible. Hence, in this paper, we first discuss the generalized form of moth-inspired CPT work flow, then extend the orthodox moth-inspired CPT from 2D to 3D. More importantly, we also propose an ASC strategy to deal with the “passing-by” problem and to gain better control performance. Simulation results are given to demonstrate that ASC can solve the inflexibility caused by “hard” step length in diffusive airflow, because it can autonomously adjust the step lengths of the robot according to the status of the environment, enabling our robot to stride confidently in the low chemical concentration areas and focus on highly suspicious areas.

Keywords

Autonomous chemical plume tracking rotorcraft step control adaptive system robotics

Get full access to this article

View all access options for this article.

References

Allison

J. D.

Cardé

R. T.

(2008). Male pheromone blend preference function measured in choice and nochoice wind tunnel trials with almond moths, Cadra cautella. Animal Behaviour, 75(1), 259–266.

Bashi-Azghadi

S. N.

Kerachian

Bazargan-Lari

M. R.

Solouki

(2010). Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. Expert Systems with Applications, 37(10), 7154–7161.

Bauchau

Bottasso

Nikishkov

(2001). Modeling rotorcraft dynamics with finite element multibody procedures. Mathematical and Computer Modelling, 33(10), 1113–1137.

Baxt

W. G.

Moody

(1983). The impact of a rotorcraft aeromedical emergency care service on trauma mortality. JAMA, 249(22), 3047–3051.

Belanger

J. H.

Willis

(1998). Biologically inspired search algorithms for locating unseen odor sources. In Proceedings of Intelligent Control (ISIC), IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS) (pp. 265–270).

Cardé

R. T.

Mafra-Neto

(1997). Mechanisms of flight of male moths to pheromone. In Insect pheromone research (pp. 275–290). Berlin, Germany: Springer.

Castillo

Dzul

Lozano

(2004). Realtime stabilization and tracking of a four-rotor mini rotorcraft. IEEE Transactions on Control Systems Technology, 12(4), 510–516.

Duistermars

B. J.

Chow

D. M.

Frye

M. A.

(2009). Flies require bilateral sensory input to track odor gradients in flight. Current Biology, 19(15), 1301–1307.

Edwards

Rutkowski

A. J.

Quinn

R. D.

Willis

(2005). Moth-inspired plume tracking strategies in three-dimensions. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005 (pp. 1669–1674).

10.

Efe

M. Ö

. (2007). Robust low altitude behavior control of a quadrotor rotorcraft through sliding modes. In Mediterranean Conference on Control & Automation, Med’07 (pp. 1–6).

11.

Farrell

Pang

(2005). Chemical plume tracing via an autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 30(2), 428–442.

12.

Finney

D. J.

(1947). Probit analysis; A statistical treatment of the sigmoid response curve. Cambridge, UK: Cambridge University Press.

13.

Gazit

Terkel

(2003). Explosives detection by sniffer dogs following strenuous physical activity. Applied Animal Behaviour Science, 81(2), 149–161.

14.

Gong

D.-W.

Zhang

C.-L.

(2012). Localising odour source using multi-robot and anemotaxis based particle swarm optimisation. IET Control Theory & Applications, 6(11), 1661–1670.

15.

Grasso

F. W.

Atema

(2002). Integration of flow and chemical sensing for guidance of autonomous marine robots in turbulent flows. Environmental Fluid Mechanics, 2(1–2), 95–114.

16.

Harvey

D. J.

T.-F.

Keller

(2008). Comparing insect-inspired chemical plume tracking algorithms using a mobile robot. IEEE Transactions on Robotics, 24(2), 307–317.

17.

Hassell

Lawton

Beddington

(1977). Sigmoid functional responses by invertebrate predators and parasitoids. The Journal of Animal Ecology, 249–262.

18.

Hoffmann

Rajnarayan

D. G.

Waslander

S. L.

Dostal

Jang

J. S.

Tomlin

C. J.

(2004). The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC). In The 23rd Digital Avionics Systems Conference, DASC 04 (Vol. 2, pp. 12–E).

19.

Ishida

Kagawa

Nakamoto

Moriizumi

(1996). Odor-source localization in the clean room by an autonomous mobile sensing system. Sensors and Actuators B: Chemical, 33(1), 115–121.

20.

Ishida

Nakamoto

Moriizumi

Kikas

Janata

(2001). Plume-tracking robots: A new application of chemical sensors. The Biological Bulletin, 200(2), 222–226.

21.

Ishida

Wada

Matsukura

(2012). Chemical sensing in robotic applications: A review. IEEE Sensors Journal, 12(11), 3163–3173.

22.

Kang

(2012). Moth-inspired plume tracing via multiple autonomous vehicles under formation control. Adaptive Behavior, 20(2), 131–142.

23.

Kowadlo

Russell

R. A.

(2008). Robot odor localization: A taxonomy and survey. The International Journal of Robotics Research, 27(8), 869–894.

24.

J.-G.

Yang

Cui

S.-G.

Geng

L.-H.

(2011). Speed limitation of a mobile robot and methodology of tracing odor plume in airflow environments. Procedia Engineering, 15, 1041–1045.

25.

Farrell

Pang

Arrieta

R. M.

. (2006). Moth-inspired chemical plume tracing on an autonomous underwater vehicle. IEEE Transactions on Robotics, 22(2), 292–307.

26.

Farrell

J. A.

Card

R. T.

(2001). Tracking of fluid-advected odor plumes: strategies inspired by insect orientation to pheromone. Adaptive Behavior, 9(3–4), 143–170.

27.

Lin

H.-T.

Lin

C.-J.

(2003). A study on sigmoid kernels for svm and the training of non-PSD kernels by smotype methods. Submitted toNeural Computation, 1–32.

28.

T.-F.

(2013). Indoor odour source localisation using robot: Initial location and surge distance matter?Robotics and Autonomous Systems, 61(6), 637–647.

29.

Mafra-Neto

Cardé

R. T.

(1994). Fine-scale structure of pheromone plumes modulates upwind orientation of flying moths. Nature, 369(6476), 142–144.

30.

Rafferty

Shellito

Hyman

N. H.

Buie

W. D.

(2006). Practice parameters for sigmoid diverticulitis. Diseases of the Colon & Rectum, 49(7), 939–944.

31.

Russell

R. A.

(2006). Tracking chemical plumes in 3-dimensions. In: IEEE International Conference on Robotics and Biomimetics, ROBIO’06 (pp. 31–36).

32.

Russell

R. A.

Thiel

Deveza

Mackay-Sim

(1995). A robotic system to locate hazardous chemical leaks. In Proceedings of the 1995 IEEE International Conference on Robotics and Automation (Vol. 1, pp. 556–561).

33.

Shim

D. H.

Kim

H. J.

Sastry

(2000). Hierarchical control system synthesis for rotorcraft based unmanned aerial vehicles. In: AIAA Guidance, Navigation and Control Conference.

34.

Sutton

(2008). Development of CPT M3D for multiple chemical plume tracing and source identification. In Seventh International Conference on Machine Learning and Applications, ICMLA’08 (pp. 470–475).

35.

Takasaki

Namiki

Kanzaki

(2012). Use of bilateral information to determine the walking direction during orientation to a pheromone source in the silkmothBombyx mori. Journal of Comparative Physiology A, 198(4), 295–307.

36.

Tian

Zhang

(2014). From simulation to validation: Moth inspired chemical plume tracing with an autonomous underwater vehicle. In: Proceedings of the Oceans’ 14 MTS/IEEE Conference, St. John’s, Canada (pp. 1–10).

37.

Tian

Zhang

(2015). Moth-inspired chemical plume tracing via autonomous underwater vehicle with only a pair of separated chemical sensors. In: Oceans 15 MTS/IEEE Conference, Washington DC.

38.

Tian

Zhang

(2014). A bio-inspired strategy for deepsea hydrothermal non-buoyant plume tracing with autonomous underwater vehicles. In: 11th World Congress on Intelligent Control and Automation (WCICA) (pp. 5335–5342).

39.

Vickers

N. J.

(2000). Mechanisms of animal navigation in odor plumes. The Biological Bulletin, 198(2), 203–212.

40.

Waphare

Gharpure

Shaligram

Botre

(2010). Implementation of 3-nose strategy in odor plume-tracking algorithm. In International Conference on Signal Acquisition and Processing, ICSAP’10 (pp. 337–341).

41.

Webster

Volyanskyy

Weissburg

(2012). Bioinspired algorithm for autonomous sensor-driven guidance in turbulent chemical plumes. Bioinspiration & Biomimetics, 7(3), 036023.

42.

Witten

I. H.

Frank

(2005). Data mining: Practical machine learning tools and techniques. New York: Morgan Kaufmann.

43.

Yuan

Oswald

(2015). Autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments using Petri nets. Expert Systems with Applications, 42(1), 527–538.

44.

Zhou

Huang

G. H.

Chan

C. W.

(2004). Development of an intelligent decision support system for air pollution control at coal-fired power plants. Expert Systems with Applications, 26(3), 335–356.