A model is proposed for group chase and escape using chemotactic movements only. In the proposed model, the movement depends on the concentration of the chemical substances released by each agent. Chemotaxis-based interactions propagate slower and later, and exist locally between agents, making groups chase and escape under more uncertain circumstances than in cases where agent distance measurements use electromagnetic waves, such as visible light. Numerical results with the model demonstrate that maintaining a longer distance between the chasers and targets is a better strategy for each group.

1. [1] C. W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model,” ACM SIGGRAPH Computer Graphics, Vol.21, No.4, pp. 25-34, 1987. https://doi.org/10.1145/37402.37406
2. [2] T. Vicsek, A. Czirók, 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
3. [3] T. Kida, Y. Sueoka, H. Shigeyoshi, Y. Tsunoda, Y. Sugimoto, and K. Osuka, “Verification of Acoustic-Wave-Oriented Simple State Estimation and Application to Swarm Navigation,” J. Robot. Mechatron., Vol.33, No.1, pp. 119-128, 2021. https://doi.org/10.20965/jrm.2021.p0119
4. [4] D. Kurabayashi, T. Choh, J. Cheng, and T. Funato, “Adaptive Formation Transition of a Swarm of Mobile Robots Based on Phase Gradient,” J. Robot. Mechatron., Vol.22, No.4, pp. 467-474, 2010. https://doi.org/10.20965/jrm.2010.p0467
5. [5] M. Naruoka, Y. Goto, H. Weimerskirch, T. Mukai, T. Sakamoto, K. Sakamoto, and K. Sato, “Application of Inertial and GNSS Integrated Navigation to Seabird Biologging,” J. Robot. Mechatron., Vol.33, No.3, pp. 526-536, 2021. https://doi.org/10.20965/jrm.2021.p0526
6. [6] B. L. Bassler and R. Losick, “Bacterially speaking,” Cell, Vol.125, pp. 237-246, 2006. https://doi.org/10.1016/j.cell.2006.04.001
7. [7] S. Mukherjee and B. L. Bassler, “Bacterial quorum sensing in complex and dynamically changing environments,” Nature Reviews Microbiology, Vol.17, pp. 371-382, 2019. https://doi.org/10.1038/s41579-019-0186-5
8. [8] L. Tweedy, O. Susanto, and R. H. Insall, “Self-generated chemotactic gradients-cells steering themselves,” Current Opinion Cell Biology, Vol.42, pp. 46-51, 2016. https://doi.org/10.1016/j.ceb.2016.04.003
9. [9] B. Petri and M.-J. Sanz, “Neutrophil chemotaxis,” Cell Tissue Research, Vol.371, pp. 425-436, 2018. https://doi.org/10.1007/s00441-017-2776-8
10. [10] A. Kimura and T. Ohira, “Group chase and escape,” New J. of Physics, Vol.12, Article No.053013, 2010. https://doi.org/10.1088/1367-2630/12/5/053013
11. [11] A. Kimura and T. Ohira, “Group Chase and Escape: Fusion of Pursuits-Escapes and Collective Motions (Theoretical Biology),” Springer, 2019.
12. [12] C. Oosawa, “A model of camphor-type self-driven particle,” Proc. of the 26th Symposium on Traffic Flow and Self-driven Particles, pp. 55-58, 2020.
13. [13] C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a 2-dimensional cellular automaton,” Physica A, Vol.295, pp. 507-525, 2001. https://doi.org/10.1016/S0378-4371(01)00141-8
14. [14] A. Kirchner, K. Nishinari, and A. Schadschneider, “Friction effects and clogging in a cellular automaton model for pedestrian dynamics,” Physical Review E, Vol.67, Article No.056122, 2003. https://doi.org/10.1103/PhysRevE.67.056122
15. [15] C. Muro, R. Escobedo, L. Spector, and R. P. Coppinger, “Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations,” Behavioural Processes, Vol.88, pp. 192-197, 2011. https://doi.org/10.1016/j.beproc.2011.09.006
16. [16] J. E. Berlman and J. R. Kirby, “Deciphering the hunting strategy of a bacterial wolfpack,” FEMS Microbiology Reviews, Vol.33, pp. 942-957, 2009. https://doi.org/10.1111/j.1574-6976.2009.00185.x
17. [17] F. Soto, J. Wang, R. Ahmed, and U. Demirci, “Medical Micro/Nanorobots in Precision Medicine,” Advanced Science, Vol.7, Article No.2002203, 2020. https://doi.org/10.1002/advs.202002203
18. [18] K. Nakamura and T. J. Kobayashi, “A connection between bacterial chemotactic network and optimal filtering,” Physical Review Letters, Vol.126, Article No.128102, 2021. https://doi.org/10.1103/PhysRevLett.126.128102
19. [19] K. Nakamura and T. J. Kobayashi, “Optimal sensing control of run-and-tumble chemotaxis,” Physical Review Research, Vol.4, Article No.013120, 2022. https://doi.org/10.1103/PhysRevResearch.4.013120
20. [20] S. Murata, A. Konagaya, S. Kobayashi, H. Saito, and M. Hagiya, “Molecular Robotics: A New Paradigm for Artifacts,” New Generation Computing, Vol.31, pp. 27-45, 2013. https://doi.org/10.1007/s00354-012-0121-z
21. [21] M. Hagiya, A. Konagaya, S. Kobayashi, H. Saito, and S. Murata, “Molecular Robots with Sensors and Intelligence,” Accounts of Chemical Research, Vol.47, No.6, pp. 1681-1690, 2014. https://doi.org/10.1021/ar400318d