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Perceptive communicating capsules for fluid flow measurement and visualisation

Published online by Cambridge University Press:  18 February 2015

Robert Logan Stewart ,
Ilija Denis Šutalo  and
Petar Liovic
Robert Logan Stewart*
Affiliation:
Fluid Dynamics Group, Manufacturing Flagship, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Highett, VIC, 3190, Australia
Ilija Denis Šutalo
Affiliation:
Fluid Dynamics Group, Manufacturing Flagship, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Highett, VIC, 3190, Australia
Petar Liovic
Affiliation:
Mineral Resources Flagship, CSIRO, Clayton, VIC, 3168, Australia
*
*Corresponding author. E-mail: [email protected]
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Summary

A new approach to flow measurement and visualisation in fluid dynamics based on a group of perceptive communicating capsules has been developed. Experiments were carried out with fluid-mobilised and stationary capsules deployed in a fluid flow test rig (raceway pond). Each capsule contains a microcontroller, battery, infra-red and visible LEDs and other electronics. Using optical communications, capsules can record encounters with one another. From the resulting interaction patterns, fluid flow speed and path-frequency measurements were obtained. Additionally, the capsules have shown the capacity for distributed sensing, and their streaklines provide a valuable means of external visualisation.

Type
Articles
Information
Robotica , Volume 35 , Issue 1 , January 2017 , pp. 143 - 165
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Prenel, J.-P. and Ambrosini, D., “Flow visualization and beyond,” Opt. Lasers Eng. 50, 1–7 (2012).CrossRefGoogle Scholar
2. Ball, P., Flow, Nature's Patterns, a Tapestry in Three Parts (Oxford University Press, New York, 2009).Google Scholar
3. Adrian, R. J., “Twenty years of particle image velocimetry,” Exp. Fluids 39, 159–169 (2005).Google Scholar
4. Foreman, J. W. Jr., George, E. W. and Lewis, R. D., “Measurement of localized flow velocities in gases with a laser Doppler flowmeter,” Appl. Phys. Lett. 7, 77–78 (1965).Google Scholar
5. Dickey, T. D., Hartman, B., Hurst, E. and Isenogle, S., “Measurment of fluid flow using streak photography,” Am. J. Phys. 52, 216–219 (1984).Google Scholar
6. Şutalo, I. D., Paterson, D. and Rudman, M., “Flow visualisation and computational prediction in thickener rake models,” Miner. Eng. 16, 93–102 (2003).CrossRefGoogle Scholar
7. Metcalfe, G., Lester, D., Ord, A., Kulkarni, P., Rudman, M., Trefry, M., Hobbs, B., Regenaur-Lieb, K. and Morris, J., “An experimental and theoretical study of the mixing characteristics of a periodically reoriented irrotational flow,” Phil Trans. R. Soc. A 368, 2147–2162 (2010).Google Scholar
8. Gamio, J. C., Castro, J., Rivera, L., Alamilla, J., Garcia-Nocetti, F. and Aguilar, L., “Visualisation of gasoil two-phase flows in pressurised pipes using electrical capacitance tomography,” Flow Meas. Instrum. 16, 129–134 (2005).CrossRefGoogle Scholar
9. Takeda, Y., “Velocity profile measurement by ultrasound doppler shift method,” Int. J. Heat Fluid Flow 7, 313–318 (1986).Google Scholar
10. Warneke, B., Last, M., Liebowitz, B. and Pister, K. S. J., “Smart dust: Communicating with a cubic-millimeter computer,” Computer 34, 44–51 (2001).Google Scholar
11. Wark, T., Hu, W., Corke, P., Hodge, J., Keto, A., Mackey, B., Foley, G., Sikka, P. and Brünig, M., “Springbrook: Challenges in Developing a Long-Term, Rainforest Wireless Sensor Network,” Proceedings of ISSNIP 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Sydney, Australia (2008) pp. 599–604.Google Scholar
12. Stoianov, I., Nachman, L., Madden, S. and Tokmouline, T., “PIPENET: A Wireless Sensor Network for Pipeline Monitoring,” Proceedings of the 6th International Symposium on Information Processing in Sensor Networks (IPSN) Cambridge, MA, USA (2007) pp. 264–273.Google Scholar
13. Smith, R. N., Prentis, P., Langendoen, K. and Corke, P., “Pseudoseeds: Investigating Long-Distance, Ocean Seed Dispersal with Wireless Sensors,” Proceedings of 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotic Environmental Monitoring, San Francisco, CA, USA (2011).Google Scholar
14. Wark, T., Corke, P., Sikka, P., Klingbeil, L., Guo, Y., Crossman, C., Valencia, P., Swain, D. and Bishop-Hurley, G., “Transforming agriculture through pervasive wireless sensor networks,” Pervasive Comput. 6 (2), 50–57 (2007).CrossRefGoogle Scholar
15. Hedley, M., Hoschke, N., Johnson, M., Lewis, C., Murdoch, A., Price, D., Prokopenko, M., Scott, A., Wang, P. and Farmer, A., “Sensor Network for Structural Health Monitoring,” Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP), Melbourne, Australia (2004) pp. 361–366.Google Scholar
16. Ramos, C., Augusto, J. C. and Shapiro, D., “Ambient intelligence – the next step for artificial intelligence,” IEEE Intell. Syst. 23 (2), 15–18 (2008).Google Scholar
17. Ding, D., Cooper, R. A., Pasquina, P. F. and Fici-Pasquina, L., “Sensor technology for smart homes,” Maturitas 69, 131–136 (2011).Google Scholar
18. Correll, N. and Martinoli, A., “Multirobot inspection of industrial machinery,” IEEE Robot. Autom. Mag. 16, 103–112 (2009).Google Scholar
19. Baronti, P., Pillai, P., Chook, V. W. C., Chessa, S., Gotta, A. and Hu, Y. F., “Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards,” Comput. Commun. 30, 1655–1695 (2007).Google Scholar
20. Kusy, B., Richter, C., Hu, W., Afanasyev, M., Jurdak, R., Br, M.ünig, Abbott, D., Huynh, C. and Ostry, D., “Radio Diversity for Reliable Communication in WSNs,” Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Chicago, IL, USA (2011) pp. 270–281.Google Scholar
21. Yick, J., Mukherjee, B. and Ghosal, D., “Wireless sensor network survey,” Comput. Networks 52, 2292–2330 (2008).Google Scholar
22. Hu, W., Corke, P., Shih, W. C. and Overs, L., “secFleck: A Public Key Technology Platform for Wireless Sensor Networks,” Proceedings of the 6th European Conference of Wireless Sensor Networks (EWSN) Cork, Ireland (2009) pp. 296–311.Google Scholar
23. Fukuda, T. and Nakagawa, S., “Approach to the dynamically reconfigurable robotic system,” J. Intell. Robot. Syst. 1, 55–72 (1988).CrossRefGoogle Scholar
24. Kamimura, A., Kurokawa, H., Yoshida, E., Murata, S., Tomita, K. and Kokaji, S., “Automatic locomotion design and experiments for a modular robotic system,” IEEE/ASME Trans. Mechatronics 10, 314–325 (2005).Google Scholar
25. Brandt, D. and Christensen, D. J., “A New Meta-Module for Controlling Large Sheets of ATRON Modules,” Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) San Diego, CA, USA (2007) pp. 2375–2380.Google Scholar
26. Gilpin, K., Knaian, A. and Rus, D., “Robot Pebbles: One Centimeter Modules for Programmable Matter through Self-Disassembly,” Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA) Anchorage, AK, USA (2010) pp. 2485–2492.Google Scholar
27. Bishop, J., Burden, S., Klavins, E., Kreisberg, R., Malone, W., Napp, N. and Nguyen, T., “Programmable Parts: A Demonstration of the Grammatical Approach to Self-Organization,” Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Edmonton, Canada (2005) pp. 3684–3691.Google Scholar
28. Tolley, M. T. and Lipson, H., “Programmable 3D Stochastic Fluidic Assembly of cm-Scale Modules,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) San Francisco, CA, USA (2011) pp. 4366–4371.Google Scholar
29. Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999).CrossRefGoogle Scholar
30. Kube, C. R. and Zhang, H., “Task modelling in collective robotics,” Auton. Robots 4, 53–72 (1997).Google Scholar
31. Krieger, M. J. B. and Billeter, J.-B., “The call of duty: Self-organised task allocation in a population of up to twelve mobile robots,” Robot. Auton. Syst. 30, 65–84 (2000).Google Scholar
32. Stewart, R. L., “A Stigmergy Rule set Compiler for Collective Construction using the Distance Transform,” Proceedings of IROS 2009 Workshop on Biologically-Inspired Robotics, St. Louis, MO, USA (2009) pp. 69–76.Google Scholar
33. Petersen, K., Nagpal, R. and Werfel, J., “TERMES: An Autonomous Robotic System for Three-Dimensional Collective Construction,” Proceedings of Robotics: Science and Systems VII, Los Angeles, CA, USA (2011) pp. 257–264.Google Scholar
34. Phan, T. A. and Russell, R. A., “An Effective Collaboration Algorithm for Swarm Robots Communicating by Sematectonic Stigmergy,” Proceedings of the 2010 11th International Conference on Control, Automation, Robotics and Vision (ICARCV) Singapore (2010) pp. 390–397.Google Scholar
35. Mondada, F., Pettinaro, G. C., Guignard, A., Kwee, I. W., Floreano, D., Deneubourg, J.-L., Nolfi, S., Gambardella, L. M. and Dorigo, M., “Swarm-bot: A new distributed robotic concept,” Auton. Robots 17, 193–221 (2004).Google Scholar
36. Stewart, R. L. and Russell, R. A., “A distributed feedback mechanism to regulate wall construction by a robotic swarm,” Adapt. Behav. 14, 21–51 (2006).Google Scholar
37. Şahin, E. and Franks, N. R., “Measurement of Space: From Ants to Robots,” Proceedings of WGW 2002: EPSRC/BBSRC International Workshop Biologically-Inspired Robotics: The Legacy of W. Grey Walter Bristol, UK (2002) pp. 241–247.Google Scholar
38. Şahin, E., Girgin, S. and Uğur, E., “Area measurement of large closed regions with a mobile robot,” Auton. Robots 21, 255–266 (2006).Google Scholar
39. Rothermich, J. A., Ecemiş, M. Ihsan and Gaudiano, P., “Distributed Localization and Mapping with a Robotic Swarm,” Proceedings of the Swarm Robotics SAB 2004 International Workshop, Santa Monica, CA, USA (July 17, 2004), Revised selected papers, Lecture Notes in Computer Science (LNCS) (Şahin, E. and Spears, W. M., eds.) (Springer-Verlag, Berlin, 2005), vol. 3342, pp. 58–69.CrossRefGoogle Scholar
40. Gutiérrez, Àlvaro, Campo, A., Santos, F. C., Pinciroli, C. and Dorigo, M., “Social Odometry in Populations of Autonomous Robots,” Proceedings of the Ant Colony Optimization and Swarm Intelligence, Lecture Notes in Computer Science (LNCS) (M. Dorigo, Birattari, M., Blum, C., Clerc, M., Stützle, T. and Winfield, A. F. T., eds.) (Springer-Verlag, Berlin, 2008), vol. 5217, pp. 371–378.Google Scholar
41. Parker, C. A. and Zhang, H., “Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem,” Swarm Intell. 4, 199–220 (2010).Google Scholar
42. Yu, H., Tang, C.-M. and Bashirullah, R., “An Asymmetric RF Tagging IC for Ingestible Medication Compliance Capsules,” Proceedings of the 2009 IEEE Radio Frequency Integrated Circuits Symposium (RFIC) Boston, MA, USA (2009) pp. 101–104.Google Scholar
43. Troyk, P. R., “Injectable electronic identification, monitoring and stimulation systems,” Annu. Rev. Biomed. Eng. 1, 177–209 (1999).Google Scholar
44. Watson, B., Friend, J. and Yeo, L., “Modelling and testing of a piezoelectric ultrasonic micro-motor suitable for in vivo micro-robotic applications,” J. Micromech. Microeng. 20, 115018 (2010).Google Scholar
45. Nagy, Z., Oung, R., Abbott, J. J. and Nelson, B. J., “Experimental Investigation of Magnetic Self-Assembly for Swallowable Modular Robots,” Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Nice, France (2008) pp. 1915–1920.Google Scholar
46. Dario, P., Stefanini, C. and Menciassi, A., “Modeling and Experiments on a Legged Microrobot Locomoting in a Tubular, Compliant and Slippery Environment,” Experimental Robotics IX, Springer Tracts in Advanced Robotics (, M. H. A. Jr. and Khatib, O., eds.) (Springer-Verlag, Berlin, 2006), vol. 21, pp. 165–174.Google Scholar
47. Kósa, G., Jakab, P., Hata, N., Jólesz, F., Neubach, Z., Shoham, M., Zaaroor, M. and Székely, G., “Flagellar Swimming for Medical Micro Robots: Theory, Experiments and Application,” Proceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) Scottsdale, AZ, USA (2008) pp. 258–263.Google Scholar
48. Carpi, F., Kastelein, N., Talcott, M. and Pappone, C., “Magnetically controllable gastrointestinal steering of video capsules,” IEEE/ASME Trans. Biomed. Eng. 58, 231–234 (2011).Google Scholar
49. Requicha, A. A. G., “Nanorobots, nems and nanoassembly,” Proc. IEEE 91, 1922–1933 (2003).Google Scholar
50. Corke, P., Detweiler, C., Dunbabin, M., Hamilton, M., Rus, D. and Vasilescu, I., “Experiments with Underwater Robot Localization and Tracking,” Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA) Roma, Italy (2007) pp. 4556–4561.Google Scholar
51. Das, J., Py, F., Maughan, T., OReilly, T., Messi, M.é, Ryan, J., Sukhatme, G. S. and Rajan, K., “Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters,” Int. J. Robot. Res. 31, 626–646 (2012).Google Scholar
52. Zhang, Y. and Bellingham, J. G., “An efficient method of selecting ocean observing locations for capturing the leading modes and reconstructing the full field,” J. Geophys. Res. 113, C04005 (2008).Google Scholar
53. Benelli, G., Pozzebon, A., Raguseo, G., Bertoni, D. and Sarti, G., “An RFID Based System for the Underwater Tracking of Pebbles on Artificial Coarse Beaches,” Proceedings of the 3rd International Conference on Sensor Technologies and Applications Athens, Greece (2009) pp. 294–299.Google Scholar
54. Lai, T. T.-T., Chen, W.-J., Li, K.-H., Huang, P. and Chu, H.-H., “TriopusNet: Automating Wireless Sensor Network Deployment and Replacement in Pipeline Monitoring,” Proceedings of the 11th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Beijing, China (2012) pp. 61–71.Google Scholar
55. Kularatna, N., Wijeratne, C. and Melville, B., “Mixed Signal Approach for Rapid Prototyping of a Compact Smart Pebble for Sediment Transport Monitoring in River Beds,” Proceedings of 2005 IEEE Sensors, Irvine, CA, USA (2005) pp. 1128–1132.Google Scholar
56. Abeywardana, D. K., Hu, A. P. and Kularatna, N., “Design Enhancements of the Smart Sediment Particle for Riverbed Transport Monitoring,” Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications (ICIEA) Xi'an, China (2009) pp. 336–341.Google Scholar
57. Gordon, D. M. and Mehdiabadi, N. J., “Encounter rate and task allocation in harvester ants,” Behav. Ecology Sociobiol. 45, 370–377 (1999).CrossRefGoogle Scholar
58. Greene, M. J. and Gordon, D. M., “Interaction rate informs harvester ant task decisions,” Behav. Ecology 18, 451–455 (2007).Google Scholar
59. Pratt, S. C., “Quorum sensing by encounter rates in the ant Temnothorax albipennis ,” Behav. Ecology 16, 488–496 (2005).Google Scholar
60. Jeanne, R. L., “Group Size, Productivity, and Information Flow in Social Wasps,” In: Information Processing in Social Insects (Detrain, C., Deneubourg, J.-L. and Pasteels, J., eds.) (Birkhäuser Verlag, Basel, Switzerland, 1999).Google Scholar
61. Atmel Corporation, AVR410: RC5 IR Remote Control Receiver (2002) application note.Google Scholar
62. Atmel Corporation, AVR415: RC5 IR Remote Control Transmitter (2003) application note.Google Scholar
63. Paterson, D. A., Bandopadhayay, P. and Liffman, K., “Design of Energy-Efficient Algal Ponds,” Proceedings of Chemeca 2010: Engineering at the Edge, Adelaide, Australia (2010) pp. 411–420.Google Scholar
64. Campbell, P. K., Beer, T. and Batten, D., “Life cycle assessment of biodiesel production from microalgae in ponds,” Bioresource Technol. 102, 50–56 (2011).Google Scholar
65. Liffman, K., Paterson, D. A., Liovic, P. and Bandopadhayay, P., “Comparing the energy efficiency of different high rate algal raceway pond designs using computational fluid dynamics,” Chem. Eng. Res. Des. 91, 221–226 (2013).Google Scholar
66. Wang, S., Metcalfe, G., Stewart, R. L., Wu, J., Ohmura, N., Feng, X., and Yang, C., “Solid-liquid separation by particle-flow-instability,” Energy and Environmental Science, 7 (12), pp. 3982–3988 (2014).Google Scholar
67. Tina, G. M., Cristina, V., Paolo, A., Luca, P., Dario, G. A. and Massimo, P., “Design Considerations about a Photovoltaic Power System to Supply a Mobile Robot,” Proceedings of the 2010 IEEE International Symposium on Industrial Electronics (ISIE) Bari, Italy (2010) pp. 1829–1834.Google Scholar
68. Silvola, A. and Russell, R. A., “Robot Communication via Substrate Vibrations,” Proceedings of the 2005 Australasian Conference on Robotics and Automation (ACRA) Sydney, Australia (2005) pp. 239–244.Google Scholar
69. Mintchev, S., Stefanini, C., Girin, A., Marrazza, S., Orofino, S., Lebastard, V., Manfredi, L., Dario, P. and Boyer, F., “An Underwater Reconfigurable Robot with Bioinspired Electric Sense,” Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA) Saint Paul, MN, USA (2012) pp. 1149–1154.Google Scholar
70. Rubenstein, M., Cornejo, A. and Nagpal, R., “Programmable self-assembly in a thousand-robot swarm,” Science 345, 795–799 (2014).Google Scholar
71. Stewart, R. L., Şutalo, I. D., and Wong, C. Y., “Three-dimensional tracking of sensor capsules mobilised by fluid flow,” Measurement Science and Technology (accepted for publication) (2015).Google Scholar