Skip to main content

Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems

Abstract

Recent trends like internet-of-things (IoT) and internet-of-everything (IoE) require new distributed computing and communication approaches as size of interconnected devices moves from a cm\(^{\text {3}}\)- to the sub-mm\(^{\text {3}}\)-scale. Technological advance behind size reduction will facilitate integration of networked computing on material rather than structural level, requiring algorithmic and architectural scaling towards distributed computing. Associated challenges are linked to use of low reliability, large scale computer networks operating on low to very low resources in robotic materials capable of performing cluster computing on micro-scale. Networks of this type need superior robustness to cope with harsh conditions of operation. These can be provided by self-organization and—adaptivity. On macro scale, robotic materials afford unified distributed data processing models to allow their connection to smart environments like IoT/IoE. The present study addresses these challenges by applying mobile Multi-agent systems (MAS) and an advanced JavaScript agent processing platform (JAM), realizing self-adaptivity as feature of both data processing and the mechanical system itself. The MAS’ task is to solve a distributed optimization problem using a mechanically adaptive robotic material in which stiffness is increased via minimization of elastic energy. A practical realization of this example necessitates environmental interaction and perception, demonstrated here via a reference architecture employing a decentralized approach to control local property change in service based on identification of the loading situation. In robotic materials, such capabilities can support actuation and/or lightweight design, and thus sustainability.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Choi, T.L.M., Sui, Y., Lee, I.H., Meredith, R., Ma, Y., Kim, G., Blaauw, D., Gianchandani, Y.B.: Autonomous microsystems for downhole applications: design challenges, current state, and initial test results. Sensors 17, 2190 (2017)

    Article  Google Scholar 

  2. 2.

    Di Lecce, V., Calabrese, M., Martines, C.: From sensors to applications: a proposal to fill the gap. Sens. Transd. 18, 5–13 (2013)

    Google Scholar 

  3. 3.

    Bosse, S., Lehmhus, D., Lang, W., Busse, M. (eds.): Material-Integrated Intelligent Systems: Technology and Applications. Wiley, New York (2018). 978-3-527-33606-7

    Google Scholar 

  4. 4.

    McEvoy, M.A., Correll, N.: Materials science. Materials that couple sensing, actuation, computation, and communication. Science 347(6228), 1261689 (2015)

    Article  Google Scholar 

  5. 5.

    McEvoy, M.A., Correll, N.: Materials science. Materials that couple sensing, actuation, computation, and communication. Science 347(6228), 1261689 (2015)

    Article  Google Scholar 

  6. 6.

    McEvoy, M.A., Correll, N.: Thermoplastic variable stiffness composites with embedded, networked sensing, actuation and control. J. Compos. Mater. https://doi.org/10.1177/0021998314525982, (2014)

  7. 7.

    Zhao, L., Ma, J., Wang, T., Xing, D.: Lightweight design of mechanical structures based on structural bionic methodology. J. Bionic Eng. 7, 224–231 (2010)

    Article  Google Scholar 

  8. 8.

    Hamm, C., Müller, S.: ELiSE—an integrated, holistic bionic approach to develop optimized lightweight solutions for engineering, architecture and design. In: Hamm, C. (ed.) Evolution of Lightweight Structures. Springer, Dordrecht (2015)

    Chapter  Google Scholar 

  9. 9.

    Hamm, C.: ELiSE: Bionic Lightweight Design, Project Flyer. Alfred-Wegener-Institut Helmholtz-Zentrum fü r Polar- und Meeresforschung, Bremerhaven (2013)

    Google Scholar 

  10. 10.

    Jog, C., Haber, R., Bendsoe, M.: Topology design with opti-mized, self-adaptive materials. Int. J. Numer. Methods Eng. 37(8), 1323–1350 (1994)

    Article  MATH  Google Scholar 

  11. 11.

    McEvoy, M.A., Correll, N.: Distributed inverse kinematics for shape-changing robotic materials. Procedia Technol. 26, 4–11 (2016)

    Article  Google Scholar 

  12. 12.

    Joo, J.J., Sanders, B., Washington, G.: Energy based efficiency of adaptive structure systems. Smart Mater. Struct. 15(1), 171 (2006)

    Article  Google Scholar 

  13. 13.

    Burblies, A., Busse, M.: Computer Based Porosity Design by Multi Phase Topology Optimization. In: Multiscale & Functionally Graded Materials Conference (FGM2006), Honolulu, October 15th–18th (2006)

  14. 14.

    Caridi, M., Sianesi, A.: Multi-agent systems in production planning and control: an application to the scheduling of mixed-model assembly lines. Int. J. Prod. Econ. 68, 29–42 (2000)

    Article  Google Scholar 

  15. 15.

    Leito, P., Karnouskos, S.: Industrial Agents Emerging Applications of Software Agents in Industry. Elsevier, Amsterdam (2015)

    Google Scholar 

  16. 16.

    Bosse, S., Lechleiter, A.: Structural health and load monitoring with material-embedded sensor networks and self-organizing multi-agent systems. Procedia Technol. https://doi.org/10.1016/j.protcy.2014.09.039, (2014)

  17. 17.

    Lehmhus, D., Wuest, T., Wellsandt, S., Bosse, S., Kaihara, T., Thoben, K.-D., Busse, M.: Cloud-based automated design and additive manufacturing: a usage data-enabled paradigm shift. Sens. MDPI 15(12), 32079–32122 (2015). https://doi.org/10.3390/s151229905

    Article  Google Scholar 

  18. 18.

    Bosse, S.: Mobile Multi-Agent Systems for the Internet-of-Things and Clouds using the JavaScript Agent Machine Platform and Machine Learning as a Service. In: The IEEE 4th International Conference on Future Internet of Things and Cloud, 22–24 August 2016, Vienna, Austria (2016)

  19. 19.

    Bosse, S.: Unified Distributed Computing and Co-ordination in Pervasive/Ubiquitous Networks with Mobile Multi-Agent Systems using a Modular and Portable Agent Code Processing Platform. In: The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2015), Procedia Computer Science (2015)

  20. 20.

    Milner, R.: The Space and Motion of Communicating Agents. Cambridge University Press, Cambridge (2009)

    Book  MATH  Google Scholar 

  21. 21.

    Bosse, S., Lehnhus, D.: Towards Large-Scale Material-integrated Computing: Self-Adaptive Materials and Agents. In: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W), IEEE, https://doi.org/10.1109/FAS-W.2017.123, (2017)

  22. 22.

    Janocha, H. (ed.): Adaptronics and Smart Structures, 2nd edn. Springer, Berlin (2007)

    Google Scholar 

  23. 23.

    Choi, M., Sui, Y., Lee, I.H., Meredith, R., Ma, Y., Kim, G., Blaauw, D., Gianchandani, Y.B., Li, T.: Autonomous microsystems for downhole applications: design challenges, current state, and initial test results. Sensors 17(10), 2190 (2017). https://doi.org/10.3390/s17102190

    Article  Google Scholar 

  24. 24.

    Poslad, S.: Ubiquitous Computing: Smart Devices, Environments and Interactions. Wiley, London (2009)

    Book  Google Scholar 

  25. 25.

    Haneef, F., Angalaeswari, S.: Self-healing framework for distribution systems. Int. J. Sci. Eng. Res. 4(7), 377–382 (2013)

    Google Scholar 

  26. 26.

    Pournaras, E., Moise, I., Helbing, D.: Privacy-preserving Ubiquitous Social Mining via Modular and Compositional Virtual Sensors. In: IEEE 29th International Conference on Advanced Information Networking and Applications, (2015)

  27. 27.

    Bosse, S., Pournaras, E.: An Ubiquitous Multi-Agent Mobile Platform for Distributed Crowd Sensing and Social Mining. In: FiCloud 2017: The 5th International Conference on Future Internet of Things and Cloud, Aug 21, 2017–Aug 23, Prague, Czech Republic, (2017)

  28. 28.

    Bosse, S.: Design of Material-integrated Distributed Data Processing Platforms with Mobile Multi-Agent Systems in Heterogeneous Networks. In: Proceedings of the 6’ th International Conference on Agents and Artificial Intelligence ICAART (2014) https://doi.org/10.5220/0004817500690080

  29. 29.

    Bosse, S.: Incremental distributed learning with JavaScript agents for earthquake and disaster monitoring. Int. J. Distrib. Syst. Technol. (IJDST) (2017) https://doi.org/10.4018/IJDST.2017100103

  30. 30.

    Bosse, S.: Distributed Machine Learning with Self-organizing Mobile Agents for Earthquake Monitoring. In: 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), SASO Conference, DSS, 12 September 2016. Augsburg, Germany (2016)

  31. 31.

    Chunlina, L., Zhengdinga, L., Layuanb, L., Shuzhia, Z.: A mobile agent platform based on tuple space coordination. Adv. Eng. Softw. 33(4), 215–225 (2002)

    Article  Google Scholar 

  32. 32.

    Qin, Z., Xing, J., Zhang, J.: A replication-based distribution approach for tuple space-based collaboration of heterogeneous agents. Res. J. Inf. Technol. 2(4), 201–214 (2010)

    Google Scholar 

  33. 33.

    Lehmhus, D., Bosse, S., Gemilang, A.: Multi-Agent System, A., based approach for Adaptive Property Control in Smart Load-Bearning Structures, European Congress and Exhibition on Advanced Materials and Processes, EUROMAT (2017), Symposium E6, Modeling, Simulation and Optimization, 17–22 September, 2017, Thessaloniki, Greek

  34. 34.

    Sigmund, O., Maute, K.: Topology optimization approaches—a comparative review. Struct. Multidiscip. Optim. 48, 1031–1055 (2013). https://doi.org/10.1007/s00158-013-0978-6

    MathSciNet  Article  Google Scholar 

  35. 35.

    Zhu, J.-H., Zhang, W.-H., Xia, L.: Topology optimization in aircraft and aerospace structures design. Arch. Comput. Methods Eng. 23, 595–622 (2016). https://doi.org/10.1007/s11831-015-9151-2

    MathSciNet  Article  MATH  Google Scholar 

  36. 36.

    Ivvan Valdez, S., Botello, S., Ochoa, M.A., Marroquin, J.L., Cardoso, V.: Topology optimization benchmarks in 2D: results for minimum compliance and minimum volume in planar stress problems. Arch. Comput. Methods Eng. 24, 803–839 (2017). https://doi.org/10.1007/s11831-016-9190-3

    MathSciNet  Article  MATH  Google Scholar 

  37. 37.

    Kang, J.: Technique of Tangible User Interfaces for Smartphone. In: 12 International Conference on Information and Computer Applications (ICICA 2012) IPCSIT, vol. 24 (2012)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Stefan Bosse.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bosse, S., Lehmhus, D. Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems. Cluster Comput 22, 1017–1037 (2019). https://doi.org/10.1007/s10586-018-02894-x

Download citation

Keywords

  • Pervasive computing
  • Ubiquitous computing
  • Agents
  • Optimization
  • Material informatics
  • Self-organizing systems
  • Self-adaptive systems