single-rb.php

JRM Vol.35 No.4 pp. 890-895
doi: 10.20965/jrm.2023.p0890
(2023)

Review:

Review of Interdisciplinary Approach to Swarm Intelligence

Takeshi Kano ORCID Icon

Research Institute of Electrical Communication, Tohoku University
2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan

Received:
May 9, 2023
Accepted:
June 23, 2023
Published:
August 20, 2023
Keywords:
swarm intelligence, swarm robot, collective behavior, interdisciplinary approach
Abstract

Swarm intelligence is intelligence produced by multiple agents interacting with each other according to a simple set of rules, resulting in a system-wide intelligence. Such intelligence is found in a wide range of biological and social systems, and attempts have been made to understand the underlying principles through analytical approaches by biologists and sociologists and synthetic approaches by mathematical scientists and engineers. On the other hand, there are also attempts to construct artificial swarm intelligence systems that are not necessarily based on real-world phenomena. This review describes recent interdisciplinary research on swarm intelligence and its future prospects.

Interdisciplinary approaches to swarm intelligence

Interdisciplinary approaches to swarm intelligence

Cite this article as:
T. Kano, “Review of Interdisciplinary Approach to Swarm Intelligence,” J. Robot. Mechatron., Vol.35 No.4, pp. 890-895, 2023.
Data files:
References
  1. [1] J. Krause, G. D. Ruxton, and S. Krause, “Swarm Intelligence in Animals and Humans,” Trends in Ecology and Evolution, Vol.25, pp. 28-34, 2010. https://doi.org/10.1016/j.tree.2009.06.016
  2. [2] R. Suzuki, M. Ito, S. Kodera, K. Nishimoto, and T. Arita, “An Online Experimental Framework for Cooperative Relationships With a Real-Time Decision-Making and Rewarding Environment,” Frontiers in Ecology and Evolution, Vol.6, Article No.74, 2018. https://doi.org/10.3389/fevo.2018.00074
  3. [3] T. Miura, K. Oguchi, H. Yamaguchi, M. Nakamura, D. Sato, K. Kobayashi, N. Kutsukake, K. Miura, Y. Hayashi, M. Hojo, K. Maekawa, S. Shigenobu, T. Kano, and A. Ishiguro, “Understanding of Superorganisms: Collective Behavior, Differentiation and Social Organization,” Artificial Life and Robotics, Vol.27, No.2, pp. 204-212, 2022. https://doi.org/10.1007/s10015-022-00754-x
  4. [4] O. Shishkov and O. Peleg, “Social Insects and Beyond: The Physics of Soft, Dense Invertebrate Aggregations,” Collective Intelligence, Vol.1, No.2, 2022. https://doi.org/10.1177/26339137221123758
  5. [5] E. Bonabeau, M. Dorigo, and G. Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems,” Oxford Academic, 2020. https://doi.org/10.1093/oso/9780195131581.001.0001
  6. [6] T. Sugi, H. Ito, M. Nishimura, and K. H. Nagai, “C. elegans collectively forms dynamical networks,” Nat. Commun., Vol.10, Article No.683, 2019. https://doi.org/10.1038/s41467-019-08537-y
  7. [7] O. Yamanaka, M. Shiraishi, A. Awazu, and H. Nishimori, “Verification of mathematical models of response threshold through statistical characterisation of the foraging activity in ant societies,” Sci. Rep., Vol.9, Article No.8845, 2019. https://doi.org/10.1038/s41598-019-45367-w
  8. [8] T. Schmickl and K. Crailsheim, “Cannibalism and early capping: strategy of honeybee colonies in times of experimental pollen shortages,” J. Comp. Physiol., Vol.187, pp. 541-547, 2001. https://doi.org/10.1007/s003590100226
  9. [9] J. Liu, A. Prindle, J. Humphries, J. Humphries, M. Gabalda-Sagarra, M. Asally, D.-y. D. Lee, S. Ly, J. Garcia-Ojalvo, and G. M. Süel, “Metabolic co-dependence gives rise to collective oscillations within biofilms,” Nature, Vol.523, pp. 550-554, 2015. https://doi.org/10.1038/nature14660
  10. [10] T. Hirashima, E. G. Rens, and R. M. H. Merks, “Cellular Potts modeling of complex multicellular behaviors in tissue morphogenesis,” Development, Growth & Differentiation, Vol.59, No.5, pp. 329-339, 2017. https://doi.org/10.1111/dgd.12358
  11. [11] T. Lämmermann, P. V. Afonso, B. R. Angermann, J. M. Wang, W. Kastenmüller, C. A. Parent, and R. N. Germain, “Neutrophil swarms require LTB4 and integrins at sites of cell death in vivo,” Nature, Vol.498, pp. 371-375, 2013. https://doi.org/10.1038/nature12175
  12. [12] A. Takamatsu, R. Tanaka, H. Yamada, T. Nakagaki, T. Fujii, and I. Endo, “Spatiotemporal Symmetry in Rings of Coupled Biological Oscillators of Physarum Plasmodial Slime Mold,” Phys. Rev. Lett., Vol.87, Article No.078102, 2001. https://doi.org/10.1103/PhysRevLett.87.078102
  13. [13] T. Nakagaki, H. Yamada, and Á Tóth, “Maze-solving by an amoeboid organism,” Nature, Vol.407, Article No.470, 2000. https://doi.org/10.1038/35035159
  14. [14] Y. H. Tee, W. J. Goh, X. Yong et al., “Actin polymerisation and crosslinking drive left-right asymmetry in single cell and cell collectives,” Nat. Commun., Vol.14, Article No.776, 2023. https://doi.org/10.1038/s41467-023-35918-1
  15. [15] S. Kriegman, D. Blackiston, M. Levin, and J. Bongard, “A scalable pipeline for designing reconfigurable organisms,” Proc. of the National Academy of Sciences, Vol.117, No.4, pp. 1853-1859, 2020. https://doi.org/10.1073/pnas.1910837117
  16. [16] S. Hauert and S. N. Bhatia, “Mechanisms of cooperation in cancer nanomedicine: Towards systems nanotechnology,” Trends in Biotechnology, Vol.32, No.9, pp. 448-455, 2014. https://doi.org/10.1016/j.tibtech.2014.06.010
  17. [17] T. Gregor, K. Fujimoto, N. Masaki, and S. Sawai, “The Onset of Collective Behavior in Social Amoebae,” Science, Vol.328, No.5981, pp. 1021-1025, 2010. https://doi.org/10.1126/science.1183415
  18. [18] K. Sato, T. Hiraiwa, E. Maekawa, A. Isomura, T. Shibata, and E. Kuranaga, “Left–right asymmetric cell intercalation drives directional collective cell movement in epithelial morphogenesis,” Nat. Commun., Vol.6, Article No.10074, 2015. https://doi.org/10.1038/ncomms10074
  19. [19] G. Beni, “From Swarm Intelligence to Swarm Robotics,” E. Sahin and W. M. Spears (Eds.), “Swarm Robotics,” pp. 1-9, Berlin, Heidelberg: Springer, 2005. https://doi.org/10.1007/978-3-540-30552-1_1
  20. [20] R. Pfeifer and J. Bongard, “How the Body Shapes the Way We Think: A New View of Intelligence,” Cambridge, MA: MIT Press, 2006. https://doi.org/10.7551/mitpress/3585.001.0001
  21. [21] G. Beni and J. Wang, “Swarm Intelligence in Cellular Robotic Systems,” P. Dario, G. Sandini, and P. Aebischer (Eds.), “Robots and Biological Systems: Towards a New Bionics?,” pp. 703-712, Berlin, Heidelberg: Springer, 1993. https://doi.org/10.1007/978-3-642-58069-7_38
  22. [22] A. Schumann, “Swarm Intelligence: From Social Bacteria to Humans,” CRC Press, 2021. https://doi.org/10.1201/9780429028618
  23. [23] F. Matsuno, “From Understanding of Swarm Behaviors to Creation of Swarm Intelligence,” J. of The Society of Instrument and Control Engineers, Vol.59, No.2, pp. 141-144, 2020 (in Japanese). https://doi.org/10.11499/sicejl.59.141
  24. [24] X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, and M. Karamanoglu, “Swarm Intelligence and Bio-Inspired Computation: Theory and Applications,” Elsevier, 2013. https://doi.org/10.1016/B978-0-12-405163-8.00001-6
  25. [25] A. E. Hassanien and E. Emary, “Swarm Intelligence: Principles, Advances, and Applications,” CRC Press, 2016. https://doi.org/10.1201/9781315222455
  26. [26] J. Kennedy, R. C. Eberhart, and Y. Shi, “Swarm Intelligence,” Elsevier, 2001. https://doi.org/10.1016/B978-1-55860-595-4.X5000-1
  27. [27] D. Floreano and C. Mattiussi, “Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies,” MIR Press, 2008.
  28. [28] H. Hamann, “Swarm Robotics: A Formal Approach,” Springer, 2018. https://doi.org/10.1007/978-3-319-74528-2
  29. [29] M. Rubenstein, C. Ahler, N. Hoff, A. Cabrera, and R. Nagpal, “Kilobot: A low cost robot with scalable operations designed for collective behaviors,” Robotics and Autonomous Systems, Vol.62, No.7, pp. 966-975, 2014. https://doi.org/10.1016/j.robot.2013.08.006
  30. [30] M. Dorigo, V. Trianni, E. Sahin, R. Groß, T. H. Labella, G. Baldassarre, S. Nolfi, J.-L. Deneubourg, F. Mondada, D. Floreano, and L. M. Gambardella, “Evolving Self-Organizing Behaviors for a Swarm-bot,” Autonomous Robots, Vol.17, pp. 223-245, 2004. https://doi.org/10.1023/B:AURO.0000033973.24945.f3
  31. [31] N. Funayama, Y. Sato, K. Matsumoto, T. Ogura, and Y. Takahashi, “Coelom formation: Binary decision of the lateral plate mesoderm is controlled by the ectoderm,” Development, Vol.126, No.18, pp. 4129-4138, 1999. https://doi.org/10.1242/dev.126.18.4129
  32. [32] K. Shirai, K. Shimamura, A. Koubara, S. Shigaki, and R. Fujisawa, “Development of a behavioral trajectory measurement system (Bucket-ANTAM) for organisms moving in a two-dimensional plane,” Artificial Life and Robotics, Vol.27, pp. 698-705, 2022. https://doi.org/10.1007/s10015-022-00811-5
  33. [33] T. Vicsek, A. Czirok, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel Type of Phase Transition in a System of Self-Driven Particles,” Physical Review Letters, Vol.75, No.6, pp. 1226-1229, 1995. https://doi.org/10.1103/PhysRevLett.75.1226
  34. [34] C. W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavior Model,” Computer Graphics, Vol.21, No.4, pp. 25-34, 1987.
  35. [35] H. Sayama, “Swarm chemistry,” Artificial Life, Vol.15, No.1, pp. 105-114, 2009. https://doi.org/10.1162/artl.2009.15.1.15107
  36. [36] T. Kano, K. Osuka, T. Kawakatsu, and A. Ishiguro, “Mathematical Analysis for Non-reciprocal-interaction-based Model of Collective Behavior,” J. of the Physical Society of Japan, Vol.86, Article No.124004, 2017. https://doi.org/10.7566/JPSJ.86.124004
  37. [37] T. Kano, M. Iwamoto, and D. Ueyama, “Decentralised Control of Multiple Mobile Agents for Quick, Smooth, and Safe Movement,” Physica A: Statistical Mechanics and its Applications, Vol.572, Article No.125898, 2021. https://doi.org/10.1016/j.physa.2021.125898
  38. [38] M. Akter and A. Kakugo, “Biomolecular Motor-Based Swarm Robot: An Innovation in Molecular Delivery,” J. Robot. Mechatron., Vol.35, No.4, pp. 1047-1051, 2023. https://doi.org/10.20965/jrm.2023.p1047
  39. [39] N. Kaneko and T. Ishimaru, “Swarm Behavior of Adult-Born Neurons During Migration in a Non-Permissive Environment,” J. Robot. Mechatron., Vol.35, No.4, pp. 896-900, 2023. https://doi.org/10.20965/jrm.2023.p0896
  40. [40] R. Okada, H. Ikeno, H. Aonuma, M. Sakura, and E. Ito, “Honey Bee Waggle Dance as a Model of Swarm Intelligence,” J. Robot. Mechatron., Vol.35, No.4, pp. 901-910, 2023. https://doi.org/10.20965/jrm.2023.p0901
  41. [41] S. Nomoto, Y. Hattori, and D. Kurabayashi, “Swarm Search Algorithm Based on Chemotactic Behaviors of Caenorhabditis elegans Nematodes,” J. Robot. Mechatron., Vol.35, No.4, pp. 911-917, 2023. https://doi.org/10.20965/jrm.2023.p0911
  42. [42] C. Oosawa, “Group Chase and Escape with Chemotaxis,” J. Robot. Mechatron., Vol.35, No.4, pp. 918-921, 2023. https://doi.org/10.20965/jrm.2023.p0918
  43. [43] H. Murakami, M. S. Abe, and Y. Nishiyama, “Toward Comparative Collective Behavior to Discover Fundamental Mechanisms Underlying Behavior in Human Crowds and Nonhuman Animal Groups,” J. Robot. Mechatron., Vol.35, No.4, pp. 922-930, 2023. https://doi.org/10.20965/jrm.2023.p0922
  44. [44] D. Umetsu, S. Yamaji, D. Wakita, and T. Kano, “Quantitative Analysis of the Coordinated Movement of Cells in a Freely Moving Cell Population,” J. Robot. Mechatron., Vol.35, No.4, pp. 931-937, 2023. https://doi.org/10.20965/jrm.2023.p0931
  45. [45] Y. Sueoka, W. J. Yong, N. Takebe, Y. Sugimoto, and K. Osuka, “Effect of Robotic Pile-Up Mechanism on Cooperative Transportation for Versatile Objects,” J. Robot. Mechatron., Vol.35, No.4, pp. 938-947, 2023. https://doi.org/10.20965/jrm.2023.p0938
  46. [46] Y. Sueoka, M. Okada, Y. Tsunoda, Y. Sugimoto, and K. Osuka, “Exploration of a Simple Navigation Method for Swarm Robots Pioneered by Heterogeneity,” J. Robot. Mechatron., Vol.35, No.4, pp. 948-956, 2023. https://doi.org/10.20965/jrm.2023.p0948
  47. [47] Y. Tsunoda, L. T. Nghia, Y. Sueoka, and K. Osuka, “Experimental Analysis of Shepherding-Type Robot Navigation Utilizing Sound-Obstacle-Interaction,” J. Robot. Mechatron., Vol.35, No.4, pp. 957-968, 2023. https://doi.org/10.20965/jrm.2023.p0957
  48. [48] K. Yamagishi and T. Suzuki, “Cooperative Passing Based on Chaos Theory for Multiple Robot Swarms,” J. Robot. Mechatron., Vol.35, No.4, pp. 969-976, 2023. https://doi.org/10.20965/jrm.2023.p0969
  49. [49] D. Morimoto, Y. Iwamoto, M. Hiraga, and K. Ohkura, “Generating Collective Behavior of a Multi-Legged Robotic Swarm Using Deep Reinforcement Learning,” J. Robot. Mechatron., Vol.35, No.4, pp. 977-987, 2023. https://doi.org/10.20965/jrm.2023.p0977
  50. [50] M. Hiraga, D. Morimoto, Y. Katada, and K. Ohkura, “When Less Is More in Embodied Evolution: Robotic Swarms Have Better Evolvability with Constrained Communication,” J. Robot. Mechatron., Vol.35, No.4, pp. 988-996, 2023. https://doi.org/10.20965/jrm.2023.p0988
  51. [51] Y. Katada, T. Hirokawa, M. Hiraga, and K. Ohkura, “MBEANN for Robotic Swarm Controller Design and the Behavior Analysis for Cooperative Transport,” J. Robot. Mechatron., Vol.35, No.4, pp. 997-1006, 2023. https://doi.org/10.20965/jrm.2023.p0997
  52. [52] R. Asad, T. Hayakawa, and T. Yasuda, “Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm,” J. Robot. Mechatron., Vol.35, No.4, pp. 1007-1015, 2023. https://doi.org/10.20965/jrm.2023.p1007
  53. [53] M. Kubo, H. Sato, and A. Yamaguchi, “Consensus Building in Box-Pushing Problem by BRT Agent that Votes with Frequency Proportional to Profit,” J. Robot. Mechatron., Vol.35, No.4, pp. 1016-1027, 2023. https://doi.org/10.20965/jrm.2023.p1016
  54. [54] T. Murayama and A. Iwasaki, “Bi-Connectivity Control for Multi-Robot Network Considering Line-of-Sight Communication,” J. Robot. Mechatron., Vol.35, No.4, pp. 1028-1037, 2023. https://doi.org/10.20965/jrm.2023.p1028
  55. [55] Y. Sugimoto, K. Naniwa, D. Nakanishi, and K. Osuka, “Tension Control of a McKibben Pneumatic Actuator Using a Dynamic Quantizer,” J. Robot. Mechatron., Vol.35, No.4, pp. 1038-1046, 2023. https://doi.org/10.20965/jrm.2023.p1038

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024