skip to main content
research-article

Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms

Published:04 March 2022Publication History
Skip Abstract Section

Abstract

Large pervasive systems, deployed in dynamic environments, require flexible control mechanisms to meet the demands of chaotic state changes while accomplishing system goals. As centralized control approaches may falter in environments where centralized communication and knowledge may be impossible to implement, researchers have proposed decentralized control methods that leverage agent-driven, self-organizing behaviors, to achieve reliable, flexible systems. This article presents and compares the performance of three decentralized control approaches in the online multi-object k-assignment problem. In this domain, a set of sensors is tasked to detect and track an unknown and changing set of targets. Results show that a proposed hybrid approach that incorporates supervisory devices within the population while allowing semi-autonomous operations in non-supervisory devices produces a flexible and reliable system capable of both high detection and coverage rates.

REFERENCES

  1. [1] Arnold R., Mezzacappa E., Jablonski J., and Abruzzo B.. 2020. Multi-role UAV swarm behaviors for wide area search using emergent intelligence. In Proceedings of the 4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). 255261. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Bajovic D., Bakhtiarnia A., Bravos G., Brutti A., Burkhardt F., Cauchi D., and Chazapis A.. 2021. MARVEL: Multimodal extreme scale data analytics for smart cities environments. In Proceedings of the Balkan Conference on Communications and Networking.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Bartolini N., Massini A., and Silvestri S.. 2009. P&P protocol: Local coordination of mobile sensors for self-deployment. In Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM’09). ACM, New York, NY, 305314. DOI:DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Cui Y., Voyles R. M., He M., Jiang G., and Mahoor M. H.. 2012. A self-adaptation framework for resource constrained miniature search and rescue robots. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). 16. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Doroodgar B., Liu Y., and Nejat G.. 2014. A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims. IEEE Trans. Cyber. 44, 12 (2014), 27192732. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Elhoseny M., Tharwat A., Yuan X., and Hassanien A. E.. 2018. Optimizing K-coverage of mobile WSNs. Exp. Syst. Applic. 92 (2018), 142153. DOI:DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Esterle L.. 2017. Centralised, decentralised, and self-organised coverage maximisation in smart camera networks. In Proceedings of the IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems. 110. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Esterle L.. 2018. Goal-aware team affiliation in collectives of autonomous robots. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems. 9099. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Esterle L. and Lewis P. R.. 2017. Online multi-object k-coverage with mobile smart cameras. In Proceedings of the International Conference on Distributed Smart Cameras. ACM, 16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Esterle L. and Lewis P. R.. 2019. Distributed autonomy and trade-offs in online multiobject k-coverage. Computat. Intell. (2019). DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Frasheri M., Esterle L., and Papadopoulos A. V.. 2020. Cooperative multi-agent systems for the multi-target \( \kappa \)-coverage problem. In Proceedings of the International Conference on Agents and Artificial Intelligence. Springer, 106131.Google ScholarGoogle Scholar
  12. [12] Fusco G. and Gupta H.. 2009. Selection and orientation of directional sensors for coverage maximization. In Proceedings of the International Conference on Sensor, Mesh and Ad Hoc Communications and Networks. 19.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Ghazali S. N. A. M., Anuar H. A., Zakaria S. N. A. S., and Yusoff Z.. 2016. Determining position of target subjects in Maritime Search and Rescue (MSAR) operations using rotary wing Unmanned Aerial Vehicles (UAVs). In Proceedings of the International Conference on Information and Communication Technology (ICICTM). 14. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Goodrich M. A., Lin L., and Morse B. S.. 2012. Using camera-equipped mini-UAVS to support collaborative wilderness search and rescue teams. In Proceedings of the International Conference on Collaboration Technologies and Systems (CTS). 638638. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Hamann H.. 2018. Swarm Robotics: A Formal Approach. Springer.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Hefeeda M. and Bagheri M.. 2007. Randomized k-coverage algorithms for dense sensor networks. In Proceedings of the International Conference on Computer Communications. 23762380. DOI:DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Heinrich M. K., Wahby M., Soorati M. D., Zahadat P. Hofstadler, D. N., Ayres P., Støy K., and Hamann H.. 2016. Self-organized construction with continuous building material: Higher flexibility based on braided structures. In Proceedings of the IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS* W). IEEE, 154159.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Huang Y.-C. Tseng and C.-F.. 2005. The coverage problem in a wireless sensor network. Mob. Netw. Applic. 10, 4 (2005), 519528.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Jennings J. S., Whelan G., and Evans W. F.. 1997. Cooperative search and rescue with a team of mobile robots. In Proceedings of the International Conference on Advanced Robotics. 193200. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Jennings Nicholas R., Sycara Katia, and Wooldridge Michael. 1998. A roadmap of agent research and development. Autonomous Agents and Multi-agent Systems 1, 1 (1998), 738.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Jung B. and Sukhatme G. S.. 2006. Cooperative multi-robot target tracking. In Distributed Autonomous Robotic Systems 7. Springer, 8190.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Khan A., Rinner B., and Cavallaro A.. 2018. Cooperative robots to observe moving targets: Review. IEEE Trans. Cyber. 48, 1 (2018), 187198. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] King D. W., Esterle L., and Peterson G. L.. 2019. Entropy-Based team self-organization with signal suppression. In Proceedings of the Conference on Artificial Life. 145152. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] King D. W. and Peterson G.. 2018. A macro-level order metric for self-organizing adaptive systems. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems. 6069.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Kitano H., Tadokoro S., Noda I., Matsubara H., Takahashi T., Shinjou A., and Shimada S.. 1999. RoboCup Rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 739743.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Kolling A. and Carpin S.. 2007. Cooperative observation of multiple moving targets: An algorithm and its formalization. International J. Robot. Res. 26, 9 (2007), 935953.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Liu B., Dousse O., Nain P., and Towsley D.. 2013. Dynamic coverage of mobile sensor networks. IEEE Trans. Parallel Distrib. Syst. 24, 2 (2013), 301311. DOI:DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Liu Y., Nejat G., and Vilela J.. 2013. Learning to cooperate together: A semi-autonomous control architecture for multi-robot teams in urban search and rescue. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). 16. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Mann H. B. and Whitney D. R.. 1947. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18, 1 (03 1947), 5060. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Maza I., Caballero F., Capitan J., Martinez-de-Dios J. R., and Ollero A.. 2010. Firemen monitoring with multiple UAVs for search and rescue missions. In Proceedings of the IEEE Safety Security and Rescue Robotics. 16. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] McCune R., Purta R., Dobski M., Jaworski A., Madey G., Madey A., Wei Y., and Blake M. B.. 2013. Investigations of DDDAS for command and control of UAV swarms with agent-based modeling. In Winter Simulations Conference (WSC). 14671478.Google ScholarGoogle Scholar
  32. [32] Nahavandi S.. 2019. Industry 5.0–A human-centric solution. Sustainability 11, 16 (2019), 4371.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Parker L. E. and Emmons B. A.. 1997. Cooperative multi-robot observation of multiple moving targets. In Proceedings of the International Conference on Robotics and Automation. 20822089.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Petersen K. H., Nagpal R., and Werfel J. K.. 2011. Termes: An autonomous robotic system for three-dimensional collective construction. Robot.: Sci. Syst. VII (2011).Google ScholarGoogle Scholar
  35. [35] Petrlík M., Báča T., Heřt D., Vrba M., Krajník T., and Saska M.. 2020. A robust UAV system for operations in a constrained environment. IEEE Robot. Autom. Lett. 5, 2 (2020), 21692176. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Queralta J. P., Taipalmaa J., Pullinen B. C., Sarker V. K., Gia T. N., Tenhunen H., Gabbouj M., Raitoharju J., and Westerlund T.. 2020. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8 (2020), 191617191643.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Queralta J. P., Taipalmaa J., Pullinen B. Can, Sarker V. K., Gia T. Nguyen, Tenhunen H., Gabbouj M., Raitoharju J., and Westerlund T.. 2020. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8 (2020), 191617191643. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Rajasekhar A., Lynn N., Das S., and Suganthan P. N.. 2017. Computing with the collective intelligence of honey bees—A survey. Swarm Evolut, Computat, 32 (2017), 2548.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Reynolds C. W.. 1987. Flocks, herds, and schools: A distributed model. Comput, Graph, 21, 4 (1987), 2534.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Schmeck H.. 2005. Organic computing-a new vision for distributed embedded systems. In Proceedings of the 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC’05). IEEE, 201203.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Shannon C. E.. 1948. A mathematical theory of communication. Bell Syst. Technic. J. 27 (July, Oct. 1948), 379–423, 623–656.Google ScholarGoogle Scholar
  42. [42] Shen H., Pan L., and Qian J.. 2019. Research on large-scale additive manufacturing based on multi-robot collaboration technology. Addit. Manufact. 30 (2019), 100906.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Steghöfer J.-P., Denzinger J., Kasinger H., and Bauer B.. 2010. Improving the efficiency of self-organizing emergent systems by an advisor. In Proceedings of the International Conference and Workshops on Engineering of Autonomic and Autonomous Systems.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Stroupe A., Huntsberger T., Okon A., Aghazarian H., and Robinson M.. 2005. Behavior-based multi-robot collaboration for autonomous construction tasks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 14951500.Google ScholarGoogle Scholar
  45. [45] Tolba S., Ammar R., and Rajasekaran S.. 2016. Taking swarms to the field: Constrained spiral flocking for underwater search. In Proceedings of the International Symposium on Computers and Communications.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Valentini G., Hamann H., Dorigo M. et al. 2014. Self-organized collective decision making: The weighted voter model.Google ScholarGoogle Scholar
  47. [47] Vivek V. Abhijith, B. Parvathy, G. H. Vismaya Dev, R. S. Unnikrishnan, P. K. Reddy, and A.. 2020. Unmanned aerial vehicle for search and rescue mission. In Proceedings of the 4th International Conference on Trends in Electronics and Informatics (ICOEI). 684687. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Vilela J., Liu Y., and Nejat G.. 2013. Semi-autonomous exploration with robot teams in urban search and rescue. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). 16. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Mammen S. von, Tomforde S., Höhner J., Lehner P., Förschner L., Hiemer A., Nicola M., and Blickling P.. 2014. Ocbotics: An organic computing approach to collaborative robotic swarms. In Proceedings of the IEEE Symposium on Swarm Intelligence. IEEE, 18.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Wang S., Han Y., Chen J., Zhang Z., Wang G., and Du N.. 2018. A deep-learning-based sea search and rescue algorithm by UAV remote sensing. In Proceedings of the IEEE CSAA Guidance, Navigation and Control Conference (CGNCC). 15. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Werger B. B. and Matarić M. J.. 2001. From insect to internet: Situated control for networked robot teams. Ann. Math. Artif. Intell. 31, 1 (2001), 173197. DOI:DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Yasunaga C. R., Rivera K. R. D., Harris J. D., Martinez M. A., Mau S. L. J., Mukai R. H., Sonoda K. Y., Shiroma W. A., and Trimble A. Z.. 2017. An autonomous, target-detecting, fixed-wing UAS for simulated search-and-rescue missions. In Proceedings of the IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). 864867. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Zuo J., Chen J., Li Z., Li Z., Liu Z., and Han Z.. 2020. Research on maritime rescue UAV based on Beidou CNSS and extended square search algorithm. In Proceedings of the International Conference on Communications, Information System and Computer Engineering (CISCE). 102106. DOI:DOI:Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Autonomous and Adaptive Systems
          ACM Transactions on Autonomous and Adaptive Systems  Volume 16, Issue 2
          June 2021
          83 pages
          ISSN:1556-4665
          EISSN:1556-4703
          DOI:10.1145/3514173
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 4 March 2022
          • Accepted: 1 November 2021
          • Revised: 1 October 2021
          • Received: 1 September 2020
          Published in taas Volume 16, Issue 2

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Refereed
        • Article Metrics

          • Downloads (Last 12 months)82
          • Downloads (Last 6 weeks)4

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!