Abstract
Multi-agent systems are applied to a variety of scenarios, in which target entrapment has become a primary research area in recent decades. In order to solve the problem of intelligent swarm behavior control, the hierarchical gene regulation network (H-GRN) is proposed. However, the networks in H-GRN rely solely on target information for behavioral control, and interaction with surrounding partners only involves avoiding physical collisions. To benefit from the cooperation with partners, we design a cooperation-based gene regulatory network (C-GRN) for target entrapment. Following the hierarchical gene regulatory network, we use the agent’s own sensor to get the companion information, and add information to the network by controlling changes in the corresponding protein concentration. In addition, a self-organizing obstacle avoidance control method is also proposed. A series of empirical evaluations index comparison show that C-GRN can cooperate with partners. The experimental results indicate that the total time to complete task and average thickness of the target’s encirclement is obviously optimized in a simulation experiment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baxter, J., Burke, E., Garibaldi, J., et al.: Multirobot search and rescue: a potential field based approach. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 9–16 (2007)
Bakhshipour, M., Jabbari Ghadi, M., Namdari, F.: Swarm robotics search & rescue: a novel artificial intelligence-inspired optimization approach. Appl. Soft Comput. 57, 708–726 (2017). 2017:S1568494617301072
Martinoli, A., Easton, K., Agassounon, W.: Modeling swarm robotic systems: a case study in collaborative distributed manipulation. Int. J. Robot. Res. 23(4), 415–436 (2004)
Chen, J., Gauci, M., Li, W., et al.: Occlusion-based cooperative transport with a swarm of miniature mobile robots. IEEE Trans. Robot. 31(2), 307–321 (2017)
Madhavan, R., Fregene, K., Parker, L.E.: Distributed heterogeneous outdoor multi-robot localization. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pp. 374–381 (2002)
Arnold, R.D., Yamaguchi, H., Tanaka, T.: Search and rescue with autonomous flying robots through behavior-based cooperative intelligence. J. Int. Humanit. Action 3(1), 18 (2018)
Dierks, T., Jagannathan, S.: Neural network output feedback control of robot formations. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(2), 383–399 (2010)
Arezoumand, R., Mashohor, S., Marhaban, M.H.: Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system. Intell. Serv. Robot. 9(2), 163–175 (2016)
Loria, A., Dasdemir, J., Jarquinalvarez, N.: LeaderCFollower formation and tracking control of mobile robots along straight paths. IEEE Trans. Control Syst. Technol. 24(2), 727–732 (2016)
Dingjiang, Z., Zijian, W., Mac, S.: Agile coordination and assistive collision avoidance for quadrotor swarms using virtual structures. IEEE Trans. Robot. 34, 1–8 (2018)
Doctor, S., Venayagamoorthy, G.K., Gudise, V.G.: Optimal PSO for collective robotic search applications. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1390–1395. IEEE (2004)
Scharf, D.P., Hadaegh, F.Y., Ploen, S.R.: A survey of spacecraft formation flying guidance and control. Part II: control. In: American Control Conference, vol. 4. pp. 2976–2985. IEEE (2004)
Beard, R.W., Lawton, J., Hadaegh, F.Y., et al.: A coordination architecture for spacecraft formation control. IEEE Trans. Control Syst. Technol. 9(6), 777–790 (2001)
Yeom, K.: Bio-inspired automatic shape formation for swarms of self-reconfigurable modular robots. In: IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp. 469–476 (2010)
Kondacs, A.: Biologically-inspired self-assembly of two-dimensional shapes using global-to-local compilation. In: International Joint Conference on Artificial Intelligence, pp. 633–638 (2003)
Nagpal, R., Kondacs, A., Chang, C.: Programming methodology for biologically-inspired self-assembling systems. In: AAAI Spring Symposium on Computational Synthesis, pp. 173–180 (2003)
Schroeder, A.M., Kumar, M.: Design of decentralized chemotactic control law for area coverage using swarm of mobile robots. IEEE Trans. 261, pp. 189–192 (2016)
Lee, I.-H., Cho, U.-I.: Pattern formations with turing and HOPF oscillating pattern in a discrete reaction-diffusion system. Bull. Korean Chem. Soc. 21, 1213–1216 (2000)
Jin, Y., 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)
Taylor, T., Ottery, P., Hallam, J.: Pattern formation for multi-robot applications: robust, self-repairing systems inspired by genetic regulatory networks and cellular self-organisation. Informatics Research Report (2006)
Guo, H., Meng, Y., Jin, Y.: A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems 98(3), 193–203 (2009)
Guo, H., Jin, Y., Meng, Y.: A morphogenetic framework for self-organized multirobot pattern formation and boundary coverage. ACM Trans. Auton. Adapt. Syst. 7(1), 1–23 (2012)
Werfel, J.: Biologically realistic primitives for engineered morphogenesis. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 131–142. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15461-4_12
Jin, Y., Guo, H., Meng, Y.: A hierarchical gene regulatory network for adaptive multirobot pattern formation. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(3), 805 (2012). A Publication of the IEEE Systems Man & Cybernetics Society
Oh, H., Jin, Y.: Adaptive swarm robot region coverage using gene regulatory networks. In: Mistry, M., Leonardis, A., Witkowski, M., Melhuish, C. (eds.) TAROS 2014. LNCS (LNAI), vol. 8717, pp. 197–208. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10401-0_18
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61872378, and 91648204, in part by the National Defense Technology Innovation Special Zone Projects
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, M. et al. (2019). Cooperation-Based Gene Regulatory Network for Target Entrapment. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-26369-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26368-3
Online ISBN: 978-3-030-26369-0
eBook Packages: Computer ScienceComputer Science (R0)