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Design and implementation of a millirobot for swarm studies – mROBerTO

Published online by Cambridge University Press:  30 July 2018

Justin Y. Kim ,
Zendai Kashino Open the ORCID record for Zendai Kashino [Opens in a new window] ,
Tyler Colaco ,
Goldie Nejat  and
Beno Benhabib
Justin Y. Kim
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada. E-mails: justinyonghui.kim@mail.utoronto.ca, tyler.colaco@mail.utoronto.ca, nejat@mie.utoronto.ca, benhabib@mie.utoronto.ca
Zendai Kashino*
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada. E-mails: justinyonghui.kim@mail.utoronto.ca, tyler.colaco@mail.utoronto.ca, nejat@mie.utoronto.ca, benhabib@mie.utoronto.ca
Tyler Colaco
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada. E-mails: justinyonghui.kim@mail.utoronto.ca, tyler.colaco@mail.utoronto.ca, nejat@mie.utoronto.ca, benhabib@mie.utoronto.ca
Goldie Nejat
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada. E-mails: justinyonghui.kim@mail.utoronto.ca, tyler.colaco@mail.utoronto.ca, nejat@mie.utoronto.ca, benhabib@mie.utoronto.ca
Beno Benhabib
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada. E-mails: justinyonghui.kim@mail.utoronto.ca, tyler.colaco@mail.utoronto.ca, nejat@mie.utoronto.ca, benhabib@mie.utoronto.ca
*
*Corresponding author. E-mail: zendkash@mie.utoronto.ca
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Summary

The use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.

In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. The milli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2 footprint. mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.

Extensive experiments, some of which are presented herein, have illustrated the capability of mROBerTO units for use in implementing a variety of commonly proposed swarm algorithms.

Keywords

Swarm roboticsDesignMulti-robot systemsControl of robotic systemsRobot localization
Type
Articles
Information
Robotica , Volume 36 , Issue 11 , November 2018 , pp. 1591 - 1612
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Paprotny, I. and Bergbreiter, S., “Small-Scale Robotics: An Introduction,” In: Small-Scale Robotics from Nano-to-Millimeter-Sized Robotic Systems and Applications (Paprotny, I. and Bergbreiter, S., eds.) (Springer, New York, NY, 2014) ch. 1, p. 2.Google Scholar
2. Lopes, Y. K., Leal, A. B. and Dodd, T. J., “Application of Supervisory Control Theory to Swarms of E-Puck and Kilobot Robots,” In: Swarm Intelligence (Dorigo, M., Birattari, M., Garnier, S., Hamann, H., Montes de Oca, M., Solnon, C., and Stützle, T., eds.) (Springer, New York, NY, 2014) pp. 62–73.Google Scholar
3. Correll, N., Rutishauser, S. and Martinoli, A., “Comparing Coordination Schemes for Miniature Robotic Swarms: A Case Study in Boundary Coverage of Regular Structures,” In: Springer Tracts in Advanced Robots (Khatib, O., Kumar, V. and Rus, D., eds.) (Springer, Berlin, Germany, 2008) vol. 39, pp. 471–480.Google Scholar
4. Fyler, D., Sullivan, B. and Raptis, I. A., “Distributed Object Manipulation Using a Mobile Multi-Agent System,” IEEE International Conference on Technologies for Practical Robot Applications, Woburn, MA (2015) pp. 1–6.Google Scholar
5. Christensen, D. L., Hawkes, E. W., Suresh, S. A., Ladenheim, K. and Cutkosky, M. R., “μTugs: Enabling Microrobots to Deliver Macro Forces with Controllable Adhesives,” IEEE International Conference on Robotic and Automation, Seattle, WA (2015) pp. 4048–4055.Google Scholar
6. Bruhwiler, R., Goldberg, B., Doshi, N., Ozcan, O., Jafferis, N., Karpelson, M. and Wood, R. J., “Feedback Control of a Legged Microrobot with On-board Sensing,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany (2015) pp. 5727–5733.Google Scholar
7. Haldane, D. W. and Fearing, R. S., “Running Beyond the Bio-Inspired Regime,” IEEE International Conference on Robotic and Automation, Seattle, WA (2015) pp. 4539–4546.Google Scholar
8. Yim, S. and Kim, S., “Origami-Inspired Printable Tele-Micromanipulation System,” IEEE International Conference on Robotic and Automation, Seattle, WA (2015) pp. 2704–2709.Google Scholar
9. El-Moukaddem, F., Torng, E., Xing, G., Torng, E., Xing, G. and Xing, G., “Mobile relay configuration in data-intensive wireless sensor networks,” IEEE Trans. Mob. Comput. 12 (2), 261–273 (2013).Google Scholar
10. Dantu, K., Rahimi, M., Shah, H., Babel, S., Dhariwal, A. and Sukhatme, G. S., “Robomote: Enabling Mobility in Sensor Networks,” 4th International Symposium on IPSN, Piscataway, NJ (2005) pp. 404–409.Google Scholar
11. Vartholomeos, P., Vlachos, K. and Papadopoulos, E., “Analysis and motion control of a centrifugal-force microrobotic platform,” IEEE Trans. Autom. Sci. Eng. 10 (3), 545–553 (2013).Google Scholar
12. Mahoney, A. W. and Abbott, J. J., “Five-degree-of-freedom manipulation of an untethered magnetic device in fluid using a single permanent magnet with application in stomach capsule endoscopy,” Int. J. Robot. Res. 35 (2015). doi:10.1177/0278364914558006.Google Scholar
13. Yim, S., Gultepe, E., Gracias, D. H. and Sitti, M., “Biopsy using a magnetic capsule endoscope carrying, releasing, and retrieving untethered microgrippers,” IEEE Trans. Biomed. Eng. 61 (2), 513–521 (2014).Google Scholar
14. Suzuki, T., Sugizaki, R., Kawabata, K., Hada, Y. and Tobe, Y., “Autonomous deployment and restoration of sensor network using mobile robots,” Int. J. Adv. Robot. Syst. 7 (2010). doi:10.5772/9696.Google Scholar
15. Liu, Y. and Nejat, G., “Multirobot cooperative learning for semiautonomous control in urban search and rescue applications,” J. Field Robot. 33 (4), 512–536 (2015).Google Scholar
16. Doroodgar, B., Liu, Y. and Nejat, G., “A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims,” IEEE Trans. Cybern. 44 (12), 2719–2732 (2014).Google Scholar
17. Zhang, Z., Nejat, G., Guo, H. and Huang, P., “A novel 3D sensory system for robot-assisted mapping of cluttered urban search and rescue environments,” Intel. Serv. Robot. 4 (2), 119–134, (2011).Google Scholar
18. Arezoumand, R., Mashohor, S. and Marhaban, M. H., “Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system,” Intel. Serv. Robot. 9 (2), 163–175 (2016).Google Scholar
19. Fearing, R. S., “Challenges for Effective Millirobots,” International Symposium on Micro-Nano Mechatronics and Human Science, Nagoya (2006) pp. 1–5.Google Scholar
20. Kashino, Z., Kim, J. Y., Nejat, G. and Benhabib, B., “Spatiotemporal adaptive optimization of a static-sensor network via a non-parametric estimation of target location likelihood,” IEEE Sensors J. 17 (5), 1479–1492 (2017).Google Scholar
21. Macwan, A., Vilela, J., Nejat, G. and Benhabib, B., “A multirobot path-planning strategy for autonomous wilderness search and rescue,” IEEE Trans. Cybern. 45 (9), 1784–1797 (2015).Google Scholar
22. Macwan, A., Nejat, G. and Benhabib, B., “Target-motion prediction for robotic search and rescue in wilderness environments,” IEEE Trans. Syst. Man Cybern. Part B 41 (5), 1287–1298 (2011).Google Scholar
23. Couceiro, M. S., Portugal, D., Rocha, R. P. and Ferreira, N. M. F., “Marsupial teams of robots: Deployment of miniature robots for swarm exploration under communication constraints,” Robotica 32 (7), 1017–1038 (2014).Google Scholar
24. Bergbreiter, S., “Effective and Efficient Locomotion for Millimeter-Sized Microrobots,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008) pp. 4030–4035.Google Scholar
25. Mackay, M. D., Fenton, R. G. and Benhabib, B., “Multi-camera active surveillance of an articulated human form – An implementation strategy,” Comput. Vis. Image Underst. 115 (10), 1395–1413 (2011).Google Scholar
26. Bakhtari, A., Mackay, M. and Benhabib, B., “Active-vision for the autonomous surveillance of dynamic, multi-object environments,” J. Intell. Robot Syst. 54 (4), 567 (2009).Google Scholar
27. Bakhtari, A. and Benhabib, B., “An active vision system for multitarget surveillance in dynamic environments,” IEEE Trans. Syst. Man Cybern. Part B 37 (1), 190–198 (2007).Google Scholar
28. Bakhtari, A., Naish, M. D., Eskandari, M., Croft, E. A. and Benhabib, B., “Active-vision-based multisensor surveillance - an implementation,” IEEE Trans. Syst., Man Cybern. Part C 36 (5), 668–680 (2006).Google Scholar
29. Shinde, V., Dutta, A. and Saxena, A., “Experiments on multi-agent capture of a stochastically moving object using modified projective path planning,” Robotica 31 (2), 267–284 (2013).Google Scholar
30. Liu, W. and Winfield, A. F. T., “Open-hardware e-puck linux extension board for experimental swarm robotics research,” Microprocess. Microsyst. 35 (1), 60–67 (2011).Google Scholar
31. Gross, R., Bonani, M., Mondada, F. and Dorigo, M., “Autonomous self-assembly in swarm-bots,” IEEE Trans. Robot. 22 (6), 1115–1130 (2006).Google Scholar
32. McLurkin, J., McMullen, A., Robbins, N., Habibi, G., Becker, A., Chou, A., Li, H., John, M., Okeke, N., Rykowski, J., Kim, S., Xie, W., Vaughn, T., Zhou, Y., Shen, J., Chen, N., Kaseman, Q., Langford, L., Hunt, J., Boone, A. and Koch, K., “A Robot System Design for Low-cost Multi-Robot Manipulation,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL (2014) pp. 912–918.Google Scholar
33. Habibi, G., Kingston, Z., Xie, W., Jellins, M. and McLurkin, J., “Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems,” IEEE International Conference on Robotics and Automation, Seattle, WA (2015) pp. 1282–1288.Google Scholar
34. Lembke, K., Kietlinski, L., Golanski, M. and Schoeneich, R., “RoboMote: Mobile Autonomous Hardware Platform for Wireless Ad-hoc Sensor Networks,” IEEE International Symposium on Industrial Electronics, Gdansk, Poland (2011) pp. 940–944.Google Scholar
35. Caprari, G. and Siegwart, R., “Mobile Micro-Robots Ready to Use: Alice,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 3295–3300.Google Scholar
36. Kornienko, S., Kornienko, O. and Levi, P., “Minimalistic approach towards communication and perception in microrobotic swarms,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005), pp. 2228–2234.Google Scholar
37. Farshad Arvin, K. S., “Development of a miniature robot for swarm robotic application,” Int. J. Comput. Electr.Eng. 1, 436–442 (2009). doi:10.7763/IJCEE.2009.V1.67.Google Scholar
38. Kettler, A., Szymanski, M. and Wörn, H., “The Wanda Robot and Its Development System for Swarm Algorithms,” In: Advances in Autonomous Mini Robots (Rückert, U., Joaquin, S. and Felix, W., eds.) (Springer, Berlin, Germany, 2012) pp. 133–146.Google Scholar
39. Jang, H. B., Villalba, R. D., Paley, D. and Bergbreiter, S., “RSSI-based Rendezvous on the Tiny Terrestrial Robotic Platform (TinyTeRP),” Institute for Systems Research and Technical Report, Univ. Maryland (2013).Google Scholar
40. Sabelhaus, A. P., Mirsky, D., Hill, L. M., Martins, N. C. and Bergbreiter, S., “TinyTeRP: A Tiny Terrestrial Robotic Platform with Modular Sensing,” IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 2600–2605.Google Scholar
41. Pickem, D., Lee, M. and Egerstedt, M., “The GRITSBot in its natural habitat - a multi-robot testbed,” IEEE International Conference on Robotics and Automation, Seattle, WA (2015) pp. 4062–4067.Google Scholar
42. Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A. and Nagpal, R., “Kilobot: A low cost robot with scalable operations designed for collective behaviors,” Robot. Auton. Syst. 62 (7), 966–975 (2014).Google Scholar
43. Arvin, F., Murray, J., Zhang, C. and Yue, S., “Colias: An autonomous micro robot for swarm robotic applications,” Int. J. Adv. Robot. Syst. 11 (1), 1–10 (2014).Google Scholar
44. Le Goc, M., Kim, L. H., Parsaei, A., Fekete, J.-D., Dragicevic, P. and Follmer, S., “Zooids: Building blocks for swarm user interfaces,” Annual Symposium User Interface Software Technology, New York, NY (2016) pp. 97–109.Google Scholar
45. Gctronic, “Mobile Robot Products.” [Online]. Available:http://www.gctronic.com/products.php. [Accessed: 17-Feb-2016].Google Scholar
46. K-Team Corporation, “K-Team Mobile Robot Products.” [Online]. Available:http://www.k-team.com/mobile-robotics-products. [Accessed: 17-Feb-2016].Google Scholar
47. Kim, J. Y., Colaco, T., Kashino, Z., Nejat, G. and Benhabib, B., “mROBerTO: A Modular Millirobot for Swarm-Behavior Studies,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, Korea (2016) pp. 2109–2114.Google Scholar
48. Nordic Semiconductor. (2018). “nRF51422/ANT™/Products/Home - Ultra Low Power Wireless Solutions from NORDIC SEMICONDUCTOR.” [Online] Available at:https://www.nordicsemi.com/eng/Products/ANT/nRF51422. [Accessed: 5 Jan. 2018].Google Scholar
49. Precisionmicrodrives.com. (2018). “4mm DC Motor - 8mm Type|Precision Microdrives.” [Online] Available at:https://www.precisionmicrodrives.com/product/104-001-4mm-dc-motor-8mm-type. [Accessed 5 Jan. 2018].Google Scholar
50. Drisdelle, R., Kashino, Z., Pineros, L., Kim, J. Y., Nejat, G. and Benhabib, B., “Motion Control of a Wheeled Millirobot,” Proceedings of the International Conference on Control, Dynamic Systems, Robotics, Toronto, ON, Canada (2017) pp. 124-1–124-6.Google Scholar
51. Farrow, N., Klingner, J., Reishus, D. and Correll, N., “Miniature Six-Channel Range and Bearing System: Algorithm, Analysis and Experimental Validation,” IEEE International Conference on Robotics and Automation, Hong Kong, China (2014) pp. 6180–6185.Google Scholar
52. Developer.nordicsemi.com. (2018). “Download.recurser.com.” [Online] Available at:http://developer.nordicsemi.com/nRF5_SDK/. [Accessed 5 Jan. 2018].Google Scholar
53. Developer.nordicsemi.com. (2018). “nRF5 SDK Documentation.” [Online] Available at:http://developer.nordicsemi.com/nRF5_SDK/doc/. [Accessed 5 Jan. 2018].Google Scholar
54. Bayındır, L., “A review of swarm robotics tasks,” Neurocomputing 172, 292–321 (2016).Google Scholar
55. Barca, J. C. and Sekercioglu, Y. A., “Swarm robotics reviewed,” Robotica 31 (3), 345–359 (2013).Google Scholar
56. Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M., “Swarm robotics: A review from the swarm engineering perspective,” Swarm Intell. 7 (1), 1–41 (2013).Google Scholar
57. Spears, W. M., Spears, D. F., Hamann, J. C. and Heil, R., “Distributed, physics-based control of swarms of vehicles,” Auton. Robots 17 (2–3), 137–162 (2004).Google Scholar
58. Jin, Y. and Meng, Y., “Morphogenetic robotics: An emerging new field in developmental robotics,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41 (2), 145–160 (2011).Google Scholar
59. Trianni, V., Groß, R., Labella, T. H., Şahin, E. and Dorigo, M., “Evolving Aggregation Behaviors in a Swarm of Robots,” In: Advances in Artificial Life (Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P. and Kim, J. T., eds.) (Springer, Berlin, Heidelberg, 2003) pp. 865–874.Google Scholar
60. Francesca, G., Brambilla, M., Trianni, V., Dorigo, M. and Birattari, M., “Analysing an Evolved Robotic Behaviour Using a Biological Model of Collegial Decision Making,” In: Animals to Animats 12 (Ziemke, T., Balkenius, C. and Hallam, J., eds.) (Springer, Berlin, Heidelberg, 2012) pp. 381–390.Google Scholar
61. Gauci, M., Chen, J., Dodd, T. J. and Groß, R., “Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors,” In: Distributed Autonomous Robotic Systems (Hsieh, M. A. and Chirikjian, G., eds.) (Springer, Berlin, Heidelberg, 2014) pp. 355–367.Google Scholar
62. Soysal, O. and Sahin, E., “Probabilistic Aggregation Strategies in Swarm Robotic Systems,” IEEE Symposium on Swarm Intelligence, Pasadena, CA (2005) pp. 325–332.Google Scholar
63. Nouyan, S., Groß, R., Bonani, M., Mondada, F. and Dorigo, M., “Teamwork in self-organized robot colonies,” IEEE Trans. Evol. Comput. 13 (4), 695–711 (2009).Google Scholar
64. Howard, A., Matarić, M. J. and Sukhatme, G. S., “Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem,” In: Distribution Autonomous Robotics Systems, 5 (Asama, H., Arai, T., Fukuda, T. and Hasegawa, T., eds.) (Springer, Japan, 2002) pp. 299–308.Google Scholar
65. McLurkin, J. and Yamins, D., “Dynamic Task Assignment in Robot Swarms|Multi-Robot Systems Lab - Rice University, Houston TX.” [Online]. Available: http://mrsl.rice.edu/papers/dynamic-task-assignment-robot-swarms. [Accessed: 21-Apr-2016].Google Scholar
66. Doriya, R., Mishra, S. and Gupta, S., “A Brief Survey and Analysis of Multi-Robot Communication and Coordination,” International Conference on Computer, Communication Automation, Uttar Pradesh (2015) pp. 1014–1021.Google Scholar
67. AbuKhalil, T., Sobh, T. and Patil, M., “Survey on Decentralized Modular Robots and Control Platforms,” In: Innovations and Advances in Computing Informatics, Systems Science, Networks and Engineering (Sobh, T. and Elleithy, K., eds.) (Springer International Publishing, Berlin, 2015) pp. 165–175.Google Scholar
68. Camazine, S., Deneubourg, J., Franks, N., Sneyd, J., Theraula, G. and Bonabeau, E., Self-Organization in Biological Systems (Princeton University Press, Princeton, NJ, 2003).Google Scholar
69. Okubo, A., “Dynamical aspects of animal grouping: Swarms, schools, flocks, and herds,” Adv. Biophys. 22, 1–94 (1986).Google Scholar
70. Parrish, J. K., Viscido, S. V. and Grünbaum, D., “Self-organized fish schools: An examination of emergent properties,” Biol. Bull. 202 (3), 296–305 (2002).Google Scholar
71. Soysal, O. and Şahin, E., “A Macroscopic Model for Self-organized Aggregation in Swarm Robotic Systems,” In: Swarm Robotics (Şahin, E., Spears, W. M. and Winfield, A. F. T., eds.) (Springer, Berlin, Heidelberg, 2006) pp. 27–42.Google Scholar
72. Hereford, J., “Analysis of BEECLUST Swarm Algorithm,” IEEE Symposium on Swarm Intelligence, Paris, France (2011) pp. 1–7.Google Scholar
73. Santos, V. G. and Chaimowicz, L., “Cohesion and segregation in swarm navigation,” Robotica 32 (2), 209–223 (2014).Google Scholar
74. Ekanayake, S. W. and Pathirana, P. N., “Formations of robotic swarm: An artificial force based approach,” Int. J. Adv. Robot. Syst., 6 (1), 7–24 (2009).Google Scholar
75. Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robots 13 (3), 207–222 (2002).Google Scholar
76. Derr, K. and Manic, M., “Extended virtual spring mesh (EVSM): The distributed self-organizing mobile ad hoc network for area exploration,” IEEE Trans. Ind. Electron. 58 (12), 5424–5437 (2011).Google Scholar
77. Spears, W. M. and Spears, D. F., Physicomimetics: Physics-Based Swarm Intelligence (Springer, Heidelberg, NY, 2012.Google Scholar
78. Dimarogonas, D. V. and Kyriakopoulos, K. J., “Connectedness preserving distributed swarm aggregation for multiple kinematic robots,” IEEE Trans. Robot. 24 (5), 1213–1123 (2008).Google Scholar
79. Gazi, V., “Swarm aggregations using artificial potentials and sliding-mode control,” IEEE Trans. Robot. 21 (6), 1208–1214 (2005).Google Scholar
80. Hamann, H., Worn, H., Crailsheim, K. and Schmick, T., “Spatial Macroscopic Models of a Bio-Inspired Robotic Swarm Algorithm,” Nice, France (2008) pp. 1415–1420.Google Scholar
81. Yang, B., Ding, Y., Jin, Y. and Hao, K., “Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis,” Robot. Auton. Syst. 72, 83–92 (2015).Google Scholar
82. Arkin, R. C., Behavior-Based Robotics (A Bradford Book, Cambridge, MA, 1998).Google Scholar
83. Nouyan, S., Campo, A. and Dorigo, M., “Path formation in a robot swarm,” Swarm Intell. 2 (1), 1–23 (2007).Google Scholar
84. Maxim, P. M., Spears, W. M. and Spears, D. F., “Robotic Chain Formations,” Proceedings IFAC Workshop on Networked Robotics, Golden, CO (2009) pp. 19–24.Google Scholar
85. Sperati, V., Trianni, V. and Nolfi, S., “Self-organised path formation in a swarm of robots,” Swarm Intell. 5 (2), 97–119 (2011).Google Scholar
86. Lee, S. M. and Myung, H., “Receding horizon particle swarm optimisation-based formation control with collision avoidance for non-holonomic mobile robots,” IET Control Theory Appl. 9 (14), 2075–2083 (2015).Google Scholar
87. Osherovich, E., Yanovki, V., Wagner, I. A. and Bruckstein, A. M., “Robust and efficient covering of unknown continuous domains with simple, ant-like A(ge)nts,” Int. J. Robot. Res. 27 (7), 815–831 (2008).Google Scholar
88. Kuyucu, T., Tanev, I. and Shimohara, K., “Evolutionary Optimization of Pheromone-Based Stigmergic Communication,” In: Applications of Evolutionary Computing (Chio, C. D., et al., eds.) (Springer, Berlin, Heidelberg, 2012) pp. 63–72.Google Scholar
89. Wagner, I. A., Lindenbaum, M. and Bruckstein, A. M., “Distributed covering by ant-robots using evaporating traces,” IEEE Trans. Robot. Autom. 15 (5), 918–933 (1999).Google Scholar
90. Svennebring, J. and Koenig, S., “Building terrain-covering ant robots: A feasibility study,” Auton. Robots 16 (3), 313–332 (2004).Google Scholar
91. Ranjbar-Sahraei, B., Weiss, G. and Nakisaee, A., “A Multi-Robot Coverage Approach Based on Stigmergic Communication,” In: Multiagent Systems Technologies (Timm, I. J. and Guttmann, C., eds.) (Springer, Berlin, Heidelberg, 2012) pp. 126–138.Google Scholar
92. Poduri, S. and Sukhatme, G. S., “Constrained coverage for mobile sensor networks,” IEEE Trans. Robot. Autom. 1, 165–171 (2004).Google Scholar
93. Ugur, E., Turgut, A. E. and Sahin, E., “Dispersion of a Swarm of Robots Based on Realistic Wireless Intensity Signals,” 22nd International Symposium on Computer and Information Science, Melbourne, Australia (2007) pp. 1–6.Google Scholar
94. Castello, E., Yamamoto, T., Libera, F. D., Liu, W., A. Winfield, F. T., Nakamura, Y. and Ishiguro, H., “Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach,” Swarm Intell. 1–31 (2016).Google Scholar
95. Hecker, J. P., Carmichael, J. C. and Moses, M. E., “Exploiting Clusters for Complete Resource Collection in Biologically-Inspired Robot Swarms,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany (2015) pp. 434–440.Google Scholar
96. Hoff, N. R., Sagoff, A., Wood, R. J. and Nagpal, R., “Two Foraging Algorithms for Robot Swarms Using Only Local Communication,” IEEE International Conference on Robotics Biomimetics, Tianjin, RPC (2010) pp. 123–130.Google Scholar
97. Lerman, K., Jones, C., Galstyan, A. and Matarić, M. J., “Analysis of dynamic task allocation in multi-robot systems,” Int. J. Robot. Res. 25 (3), 225–241 (2006).Google Scholar
98. Ramaithitima, R., Whitzer, M., Bhattacharya, S. and Kumar, V., “Sensor Coverage Robot Swarms Using Local Sensing Without Metric Information,” IEEE International Conference on Robotics and Automation, Seattle, WA (2015) pp. 3408–3415.Google Scholar
99. Ou, Y., Kang, P., Kim, M. J. and Julius, A. A., “Algorithms for Simultaneous Motion Control of Multiple T. Pyriformis Cells: Model Predictive Control and Particle Swarm Optimization,” IEEE International Conference on Robotics and Automation, Seattle, WA (2015) pp. 3507–3512.Google Scholar
100. Barnes, L., Fields, M. -A. and Valavanis, K., “Unmanned Ground Vehicle Swarm Formation Control Using Potential Fields,” Mediterranean Conference on Control Automation (2007) pp. 1–8.Google Scholar
101. Oikawa, R., Takimoto, M. and Kambayashi, Y., “Distributed Formation Control for Swarm Robots Using Mobile Agents,” IEEE 10th Jubilee International Symposium on Application Computational Intelligence and Informatics, Timisoara, Romania (2015) pp. 111–116.Google Scholar
102. Goertzel, G., “An algorithm for the evaluation of finite trigonometric series,” Am. Math. Mon. 65 (1), 34–35 (1958).Google Scholar
103. Pugh, J. and Martinoli, A., “Relative Localization and Communication Module for Small-Scale Multi-Robot Systems,” IEEE International Conference on Robotics and Automation, Orlando, FL (2006) pp. 188–193.Google Scholar
104. Gutierrez, A., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F. and Magdalena, L., “Open E-puck Range & Bearing Miniaturized Board for Local Communication in Swarm Robotics,” IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 3111–3116.Google Scholar
105. Li, P., Scalabrino, N., Fang, Y., Gregori, E. and Chlamtac, I., “How to effectively use multiple channels in wireless mesh networks,” IEEE Trans. Parallel Distrib. Syst. 20 (11), 1641–1652 (2009).Google Scholar
106. Bracewell, R. N., “The fast hartley transform,” Proc. IEEE 72 (8), 1010–1018 (1984).Google Scholar

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