research-article

Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms

Authors:

David W. KingAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 16, Issue 2

Article No.: 5, Pages 1 - 26

https://doi.org/10.1145/3502725

Published: 04 March 2022 Publication History

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]

R. Arnold, E. Mezzacappa, J. Jablonski, and B. Abruzzo. 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). 255–261. DOI:DOI:

[2]

D. Bajovic, A. Bakhtiarnia, G. Bravos, A. Brutti, F. Burkhardt, D. Cauchi, and A. Chazapis. 2021. MARVEL: Multimodal extreme scale data analytics for smart cities environments. In Proceedings of the Balkan Conference on Communications and Networking.

[3]

N. Bartolini, A. Massini, and S. Silvestri. 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, 305–314. DOI:DOI:

Digital Library

[4]

Y. Cui, R. M. Voyles, M. He, G. Jiang, and M. H. Mahoor. 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). 1–6. DOI:DOI:

[5]

B. Doroodgar, Y. Liu, and G. Nejat. 2014. A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims. IEEE Trans. Cyber. 44, 12 (2014), 2719–2732. DOI:DOI:

[6]

M. Elhoseny, A. Tharwat, X. Yuan, and A. E. Hassanien. 2018. Optimizing K-coverage of mobile WSNs. Exp. Syst. Applic. 92 (2018), 142–153. DOI:DOI:

Digital Library

[7]

L. Esterle. 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. 1–10. DOI:DOI:

[8]

L. Esterle. 2018. Goal-aware team affiliation in collectives of autonomous robots. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems. 90–99. DOI:DOI:

[9]

L. Esterle and P. R. Lewis. 2017. Online multi-object k-coverage with mobile smart cameras. In Proceedings of the International Conference on Distributed Smart Cameras. ACM, 1–6.

Digital Library

[10]

L. Esterle and P. R. Lewis. 2019. Distributed autonomy and trade-offs in online multiobject k-coverage. Computat. Intell. (2019). DOI:DOI:

[11]

M. Frasheri, L. Esterle, and A. V. Papadopoulos. 2020. Cooperative multi-agent systems for the multi-target \( \kappa \) -coverage problem. In Proceedings of the International Conference on Agents and Artificial Intelligence. Springer, 106–131.

[12]

G. Fusco and H. Gupta. 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. 1–9.

[13]

S. N. A. M. Ghazali, H. A. Anuar, S. N. A. S. Zakaria, and Z. Yusoff. 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). 1–4. DOI:DOI:

[14]

M. A. Goodrich, L. Lin, and B. S. Morse. 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). 638–638. DOI:DOI:

[15]

H. Hamann. 2018. Swarm Robotics: A Formal Approach. Springer.

[16]

M. Hefeeda and M. Bagheri. 2007. Randomized k-coverage algorithms for dense sensor networks. In Proceedings of the International Conference on Computer Communications. 2376–2380. DOI:DOI:

Digital Library

[17]

M. K. Heinrich, M. Wahby, M. D. Soorati, P. Hofstadler, D. N. Zahadat, P. Ayres, K. Støy, and H. Hamann. 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, 154–159.

[18]

Y.-C. Tseng and C.-F. Huang. 2005. The coverage problem in a wireless sensor network. Mob. Netw. Applic. 10, 4 (2005), 519–528.

Digital Library

[19]

J. S. Jennings, G. Whelan, and W. F. Evans. 1997. Cooperative search and rescue with a team of mobile robots. In Proceedings of the International Conference on Advanced Robotics. 193–200. DOI:DOI:

[20]

Nicholas R. Jennings, Katia Sycara, and Michael Wooldridge. 1998. A roadmap of agent research and development. Autonomous Agents and Multi-agent Systems 1, 1 (1998), 7–38.

Digital Library

[21]

B. Jung and G. S. Sukhatme. 2006. Cooperative multi-robot target tracking. In Distributed Autonomous Robotic Systems 7. Springer, 81–90.

[22]

A. Khan, B. Rinner, and A. Cavallaro. 2018. Cooperative robots to observe moving targets: Review. IEEE Trans. Cyber. 48, 1 (2018), 187–198. DOI:DOI:

[23]

D. W. King, L. Esterle, and G. L. Peterson. 2019. Entropy-Based team self-organization with signal suppression. In Proceedings of the Conference on Artificial Life. 145–152. DOI:DOI:

[24]

D. W. King and G. Peterson. 2018. A macro-level order metric for self-organizing adaptive systems. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems. 60–69.

[25]

H. Kitano, S. Tadokoro, I. Noda, H. Matsubara, T. Takahashi, A. Shinjou, and S. Shimada. 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, 739–743.

[26]

A. Kolling and S. Carpin. 2007. Cooperative observation of multiple moving targets: An algorithm and its formalization. International J. Robot. Res. 26, 9 (2007), 935–953.

Digital Library

[27]

B. Liu, O. Dousse, P. Nain, and D. Towsley. 2013. Dynamic coverage of mobile sensor networks. IEEE Trans. Parallel Distrib. Syst. 24, 2 (2013), 301–311. DOI:DOI:

Digital Library

[28]

Y. Liu, G. Nejat, and J. Vilela. 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). 1–6. DOI:DOI:

[29]

H. B. Mann and D. R. Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18, 1 (03 1947), 50–60. DOI:DOI:

[30]

I. Maza, F. Caballero, J. Capitan, J. R. Martinez-de-Dios, and A. Ollero. 2010. Firemen monitoring with multiple UAVs for search and rescue missions. In Proceedings of the IEEE Safety Security and Rescue Robotics. 1–6. DOI:DOI:

[31]

R. McCune, R. Purta, M. Dobski, A. Jaworski, G. Madey, A. Madey, Y. Wei, and M. B. Blake. 2013. Investigations of DDDAS for command and control of UAV swarms with agent-based modeling. In Winter Simulations Conference (WSC). 1467–1478.

[32]

S. Nahavandi. 2019. Industry 5.0–A human-centric solution. Sustainability 11, 16 (2019), 4371.

[33]

L. E. Parker and B. A. Emmons. 1997. Cooperative multi-robot observation of multiple moving targets. In Proceedings of the International Conference on Robotics and Automation. 2082–2089.

[34]

K. H. Petersen, R. Nagpal, and J. K. Werfel. 2011. Termes: An autonomous robotic system for three-dimensional collective construction. Robot.: Sci. Syst. VII (2011).

[35]

M. Petrlík, T. Báča, D. Heřt, M. Vrba, T. Krajník, and M. Saska. 2020. A robust UAV system for operations in a constrained environment. IEEE Robot. Autom. Lett. 5, 2 (2020), 2169–2176. DOI:DOI:

[36]

J. P. Queralta, J. Taipalmaa, B. C. Pullinen, V. K. Sarker, T. N. Gia, H. Tenhunen, M. Gabbouj, J. Raitoharju, and T. Westerlund. 2020. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8 (2020), 191617–191643.

[37]

J. P. Queralta, J. Taipalmaa, B. Can Pullinen, V. K. Sarker, T. Nguyen Gia, H. Tenhunen, M. Gabbouj, J. Raitoharju, and T. Westerlund. 2020. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8 (2020), 191617–191643. DOI:DOI:

[38]

A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan. 2017. Computing with the collective intelligence of honey bees—A survey. Swarm Evolut, Computat, 32 (2017), 25–48.

[39]

C. W. Reynolds. 1987. Flocks, herds, and schools: A distributed model. Comput, Graph, 21, 4 (1987), 25–34.

Digital Library

[40]

H. Schmeck. 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, 201–203.

Digital Library

[41]

C. E. Shannon. 1948. A mathematical theory of communication. Bell Syst. Technic. J. 27 (July, Oct. 1948), 379–423, 623–656.

[42]

H. Shen, L. Pan, and J. Qian. 2019. Research on large-scale additive manufacturing based on multi-robot collaboration technology. Addit. Manufact. 30 (2019), 100906.

[43]

J.-P. Steghöfer, J. Denzinger, H. Kasinger, and B. Bauer. 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.

Digital Library

[44]

A. Stroupe, T. Huntsberger, A. Okon, H. Aghazarian, and M. Robinson. 2005. Behavior-based multi-robot collaboration for autonomous construction tasks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1495–1500.

[45]

S. Tolba, R. Ammar, and S. Rajasekaran. 2016. Taking swarms to the field: Constrained spiral flocking for underwater search. In Proceedings of the International Symposium on Computers and Communications.

[46]

G. Valentini, H. Hamann, M. Dorigo et al. 2014. Self-organized collective decision making: The weighted voter model.

[47]

V. Abhijith, B. Parvathy, G. H. Vismaya Dev, R. S. Unnikrishnan, P. K. Reddy, and A. Vivek. 2020. Unmanned aerial vehicle for search and rescue mission. In Proceedings of the 4th International Conference on Trends in Electronics and Informatics (ICOEI). 684–687. DOI:DOI:

[48]

J. Vilela, Y. Liu, and G. Nejat. 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). 1–6. DOI:DOI:

[49]

S. von Mammen, S. Tomforde, J. Höhner, P. Lehner, L. Förschner, A. Hiemer, M. Nicola, and P. Blickling. 2014. Ocbotics: An organic computing approach to collaborative robotic swarms. In Proceedings of the IEEE Symposium on Swarm Intelligence. IEEE, 1–8.

[50]

S. Wang, Y. Han, J. Chen, Z. Zhang, G. Wang, and N. Du. 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). 1–5. DOI:DOI:

[51]

B. B. Werger and M. J. Matarić. 2001. From insect to internet: Situated control for networked robot teams. Ann. Math. Artif. Intell. 31, 1 (2001), 173–197. DOI:DOI:

Digital Library

[52]

C. R. Yasunaga, K. R. D. Rivera, J. D. Harris, M. A. Martinez, S. L. J. Mau, R. H. Mukai, K. Y. Sonoda, W. A. Shiroma, and A. Z. Trimble. 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). 864–867. DOI:DOI:

[53]

J. Zuo, J. Chen, Z. Li, Z. Li, Z. Liu, and Z. Han. 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). 102–106. DOI:DOI:

Cited By

Kwa HKit JHorsevad NPhilippot JSavari MBouffanais R(2023)Adaptivity: a path towards general swarm intelligence?Frontiers in Robotics and AI10.3389/frobt.2023.116318510Online publication date: 9-May-2023
https://doi.org/10.3389/frobt.2023.1163185
Frasheri MMiloradović BEsterle LPapadopoulos A(2023)GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371893(1696-1703)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371893

Index Terms

Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory
  2. Distributed computing methodologies
    1. Distributed algorithms
      1. Self-organization

Recommendations

Introduction of a hybrid control approach for automotive logistics
ROCOM'11/MUSP'11: Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing

Nowadays, processes of logistics are generally controlled by applying a centralized control approach. However, regarding an increasingly complex and dynamic environment the centralized control approach often turns out to be inflexible and not manageable ...
A survey on cooperative control strategies for DC microgrids
Abstract
Distributed generators have been widely used in modern power systems, and microgrids are becoming a key concept for the integration of multiple distributed generators and clusters of loads with great efficiency and high system ...
Hybrid control structure for multi-robot formation
ICANN'10: Proceedings of the 20th international conference on Artificial neural networks: Part II

In this paper a hybrid control structure to control a multi-robot formation is proposed. The hybrid control structure consists of two control levels: the discrete control level and the continuous control level. The discrete control level ensures the ...

Comments

Information & Contributors

Information

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

Editor:
Valérie Issarny
Inria, France

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: 04 March 2022

Accepted: 01 November 2021

Revised: 01 October 2021

Received: 01 September 2020

Published in TAAS Volume 16, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
298
Total Downloads

Downloads (Last 12 months)64
Downloads (Last 6 weeks)7

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kwa HKit JHorsevad NPhilippot JSavari MBouffanais R(2023)Adaptivity: a path towards general swarm intelligence?Frontiers in Robotics and AI10.3389/frobt.2023.116318510Online publication date: 9-May-2023
https://doi.org/10.3389/frobt.2023.1163185
Frasheri MMiloradović BEsterle LPapadopoulos A(2023)GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371893(1696-1703)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371893

View Options

Get Access

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents