single-rb.php

JRM Vol.35 No.4 pp. 1007-1015
doi: 10.20965/jrm.2023.p1007
(2023)

Paper:

Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm

Razzaq Asad* ORCID Icon, Tomohiro Hayakawa** ORCID Icon, and Toshiyuki Yasuda** ORCID Icon

*Graduate School of Science and Engineering, University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

**Faculty of Engineering, University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

Received:
March 6, 2023
Accepted:
June 19, 2023
Published:
August 20, 2023
Keywords:
swarm robotics, evolutionary robotics, artificial neural networks, heterogeneous, cooperative transport
Abstract

Swarm robotics system (SRS) is a type of artifact that employs multiple robots to work together in a coordinated way, inspired by the self-organizing behavior of social insects such as ants and bees. SRSs are known for their robustness, flexibility, and scalability. This study focuses on evolutionary robotics (ER) which uses artificial neural networks (ANNs) as controllers to operate autonomous robots. In traditional ER research, SRSs were often composed of teams of homogeneous robots, each of which is controlled by a single ANN. In contrast, this study focuses on the implementation of ER in a heterogeneous SRS. To evaluate our approach, we present the concept of employing multiple controllers for sub-teams in a swarm. Heterogeneity was achieved using different controllers for the same physical bodies. We simulated a cooperative transport task, in which the performance of heterogeneity was superior because the two ANN controllers were able to express a variety of behaviors as an entire swarm. Additionally, this study investigated how well the three types of parental selection methods of the heterogeneous approach, can help to optimize the performance of the swarm.

Small differences improve swarm performance

Small differences improve swarm performance

Cite this article as:
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.
Data files:
References
  1. [1] S. Roy, S. Biswas, and S. S. Chaudhuri, “Nature-inspired swarm intelligence and its applications,” Int. J. of Modern Education and Computer Science, Vol.6, No.12, pp. 55-65, 2014. http://doi.org/10.5815/ijmecs.2014.12.08
  2. [2] N. Bredeche and N. Fontbonne, “Social learning in swarm robotics,” Philosophical Trans. of the Royal Society B: Biological Sciences, Vol.377, Issue 1843, 2021. https://doi.org/10.1098/rstb.2020.0309
  3. [3] T. Yasuda and K. Ohkura, “Collective behavior acquisition of real robotic swarms using deep reinforcement learning,” 2018 2nd IEEE Int. Conf. on Robotic Computing (IRC), pp. 179-180, 2018. https://doi.org/10.1109/IRC.2018.00038
  4. [4] M. Dorigo, G. Theraulaz, and V. Trianni, “Swarm robotics: Past, present, and future [point of view],” Proc. of the IEEE, Vol.109, No.7, pp. 1152-1165, 2021. https://doi.org/10.1109/JPROC.2021.3072740
  5. [5] Y. Zhang, E. K. Antonsson, and A. Martinoli, “Evolving neural controllers for collective robotic inspection,” Applied Soft Computing Technologies: The Challenge of Complexity, Vol.34, pp. 717-729, 2006. https://doi.org/10.1007/3-540-31662-0_55
  6. [6] M. Hiraga and K. Ohkura, “Topology and weight evolving artificial neural networks in cooperative transport by a robotic swarm,” Artificial Life and Robotics, Vol.27, pp. 324-332, 2022. https://doi.org/10.1007/s10015-021-00716-9
  7. [7] G. Ananthakrishna, “Current theoretical approaches to collective behavior of dislocations,” Physics Reports, Vol.440, Issues 4-6, pp. 113-259, 2007. https://doi.org/10.1016/j.physrep.2006.10.003
  8. [8] M. Dorigo et al., “Swarmanoid: A Novel Concept for the Study of Heterogeneous Robotic Swarms,” IEEE Robotics and Automation Magazine, Vol.20, No.4, pp. 60-71, 2013. https://doi.org/10.1109/MRA.2013.2252996
  9. [9] R. Jeanson and A. Weidenmüller, “Interindividual variability in social insects–proximate causes and ultimate consequences,” Biological Reviews of the Cambridge Philosophical Society, Vol.89, No.3, pp. 671-687, 2014. https://doi.org/10.1111/brv.12074
  10. [10] G. Francesca and M. Birattari, “Automatic design of robot swarms: Achievements and challenges,” Frontiers in Robotics and AI, Vol.3, Article No.29, 2016. https://doi.org/10.3389/frobt.2016.00029
  11. [11] M. Knudson and K. Tumer, “Coevolution of heterogeneous multi-robot teams,” Proc. of the 12th Annual Conf. on Genetic and Evolutionary Computation, pp. 127-134, 2010. https://doi.org/10.1145/1830483.1830506
  12. [12] J. C. Brenes-Torres, F. Blanes, and J. Simo, “Magnetic Trails: A novel artificial pheromone for swarm robotics in outdoor environments,” Computation, Vol.10, No.6, Article No.98, 2022. https://doi.org/10.3390/computation10060098
  13. [13] M. H. M. Alkilabi, C. Lu, and E. Tuci, “Cooperative object transport using evolutionary swarm robotics methods,” Proc. of the 13th European Conf. on Artificial Life (ECAL 2015), Vol.6, pp. 464-471, 2015. https://doi.org/10.1162/978-0-262-33027-5-ch083
  14. [14] M. Hiraga and K. Ohkura, “Effects of congestion on swarm performance and autonomous specialization in robotic swarms,” J. Robot. Mechatron., Vol.31, No.4, pp. 526-534, 2019. https://doi.org/10.20965/jrm.2019.p0526
  15. [15] M. Duarte et al., “Evolution of collective behaviors for a real swarm of aquatic surface robots,” PLoS ONE, Vol.11, No.3, Article No.e0151834, 2016. https://doi.org/10.1371/journal.pone.0151834
  16. [16] K. Lerman, A. Martinoli, and A. Galstyan, “A review of probabilistic macroscopic models for swarm robotic systems,” Lecture Notes in Computer Science, Vol.3342, pp. 143-152, 2004. https://doi.org/10.1007/978-3-540-30552-1_12
  17. [17] H. Wang and M. Rubenstein, “Shape formation in homogeneous swarms using local task swapping,” IEEE Trans. on Robotics, Vol.36, No.3, pp. 597-612, 2020. https://doi.org/10.1109/TRO.2020.2967656
  18. [18] R. Groß and M. Dorigo, “Towards group transport by swarms of robot,” Int. J. of Bio-Inspired Computation, Vol.1, Nos.1-2, pp. 1-13, 2009.
  19. [19] Y. Wei et al., “Autonomous task allocation by artificial evolution for robotic swarms in complex tasks,” Artificial Life and Robotics, Vol.24, pp. 127-134, 2019. https://doi.org/10.1007/s10015-018-0466-6
  20. [20] C. Ampatzis et al., “Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots,” Artificial Life, Vol.15, No.4, pp. 465-484, 2009. https://doi.org/10.1162/artl.2009.Ampatzis.013
  21. [21] G. Shi et al., “Neural-swarm2: Planning and control of heterogeneous multirotor swarms using learned interactions,” IEEE Trans. on Robotics, Vol.38, No.2, pp. 1063-1079, 2022. https://doi.org/10.1109/TRO.2021.3098436
  22. [22] M. Wu et al., “Torch: Strategy evolution in swarm robots using heterogeneous-homogeneous co-evolution method,” J. of Industrial Information Integration, Vol.25, Article No.100239, 2022. https://doi.org/10.1016/j.jii.2021.100239
  23. [23] F. Ducatelle, G. A. Di Caro, and L. M. Gambardella, “Cooperative self-organization in a heterogeneous swarm robotic system,” Proc. of the 12th Annual Conf. on Genetic and Evolutionary Computation, pp. 87-94, 2010. https://doi.org/10.1145/1830483.1830501
  24. [24] Z. Ban et al., “Self-organised collision-free flocking mechanism in heterogeneous robot swarms,” Mobile Networks and Applications, Vol.26, pp. 2461-2471, 2021. https://doi.org/10.1007/s11036-021-01785-7
  25. [25] Y. Tan and Z.-Y. Zheng, “Research advance in swarm robotics,” Defence Technology, Vol.9, Issue 1, pp. 18-39, 2013. https://doi.org/10.1016/j.dt.2013.03.001
  26. [26] L. V. Valen, “A new evolutionary law,” Evolutionary Theory, Vol.1, pp. 1-30, 1973.
  27. [27] R. Dawkins and Krebs, “The Evolution of Adaptation by Natural Selection,” Proc. of the Royal Society of Biological, Vol.205, pp. 605-608, 1979.
  28. [28] S. Nolfi, “Co-evolving predator and prey robots,” Adaptive Behavior, Vol.20, Issue 1, pp. 10-15, 2012. https://doi.org/10.1177/1059712311426912

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

Last updated on Dec. 06, 2024