Performance Evaluation of a Quorum Sensing based Scheme in Multi-Agent Task Development

Fredy Martinez, Edwar Jacinto, Holman Montiel

Abstract


Robotics is positioned today as a fundamental tool in industrial and commercial development, where machines interact directly with humans. There is a vast variety of tasks that require autonomous, robust, and high-performance systems. Among these tasks can benefit from the autonomous integration of multiple elements, known as multi-agent systems. These schemes have interesting advantages over the single robot solution centered on the high degree of robustness achieved and the lower cost. The control of these multi-agent systems turns out to be of great complexity and is an active field of robotics research. The motion coordination schemes are complex and require a certain level of processing and communication. In this paper, a decentralized coordination scheme for low-cost robot groups based on local interaction is evaluated. The algorithm uses bacterial Quorum Sensing (QS) as a behavioral model, a scheme under which certain actions are triggered by the agents conditioned to the population density in the region they cover. The algorithm is tested in navigation tasks for different conditions of the design parameters. Among the parameters evaluated are environment dependence, system size, and QS threshold. The development times of the tasks were statistically analyzed, and a strong dependence of the environment on the total time required was found (a well-structured and small environment concerning the system improves the performance considerably), as well as the design of the robot in terms of QS threshold and sensors.

Keywords


Autonomous systems; behavior-based control; local communication; mapping; motion; movement planning; quorum sensing; robotics; robustness; swarm.

Full Text:

PDF

References


H. ElGibreen and K. Youcef, “Dynamic task allocation in an uncertain environment with heterogeneous multi-agents,” Autonomous Robots, vol. 43, no. 7, pp. 1639–1664, 2019.

S. Krivic and J. Piater, “Pushing corridors for delivering unknown objects with a mobile robot,” Autonomous Robots, vol. 43, no. 6, pp. 1435–1452, 2019.

F. Berlinger, J. Dusek, M. Gauci, and R. Nagpal, “Robust maneuverability of a miniature, low-cost underwater robot using multiple fin actuation,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 140-147, 2018.

Z. Liu, C. West, B. Lennox, and F. Arvin, "Local bearing estimation for a swarm of low-cost miniature robots," Sensors (Switzerland), vol. 20, no. 11, pp. 1-23, 2020.

T. Xuehong, L. Huanlao, and L. Haitao, “Robust finite-time consensus control for multi-agent systems with disturbances and unknown velocities,” ISA Transactions, vol. 80, no. 1, pp. 73-80, 2018.

Z. Jing, Z. Xiaozhe, Z. Xiaopan, Z. Dongdong, and L. Huanhuan, “Task Allocation for Multi-Agent Systems Based on Distributed Many-Objective Evolutionary Algorithm and Greedy Algorithm,” IEEE Access, vol. 8, no. 1, pp. 19306-19318, 2020.

W. Guang, X. Ming, W. Yiming, Z. Ning, X. Jian, and Q. Tong, “Using Machine Learning for Determining Network Robustness of Multi-Agent Systems Under Attacks,” Lecture Notes in Computer Science, vol. 11013, no. 1, pp. 491-498, 2018.

A. Nazarova, and M. Zhai, “Distributed Solution of Problems in Multi Agent Robotic Systems,” Studies in Systems, Decision and Control, vol. 174, no. 1, pp. 107-124, 2019.

Y. Jiang, H. Yedidsion, S. Zhang, G. Sharon, and P. Stone, “Multi-robot planning with conflicts and synergies,” Autonomous Robots, vol. 43, no. 8, pp. 2011–2032, 2019.

Z. Hashemifar, C. Adhivarahan, A. Balakrishnan, and K. Dantu, “Augmenting visual slam with wi-fi sensing for indoor applications,” Autonomous Robots, vol. 43, no. 8, pp. 2245–2260, 2019.

O. Saha, P. Dasgupta, and B. Woosley, “Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options,” Autonomous Robots, vol. 43, no. 8, pp. 2071–2093, 2019.

G. Ferrer and A. Sanfeliu, “Anticipative kinodynamic planning: multi-objective robot navigation in urban and dynamic environments,” Autonomous Robots, vol. 43, no. 6, pp. 1473–1488, 2019.

A. Rendón, “Evaluation of autonomous navigation strategy based on reactive behavior for mobile robotic platforms,” Tekhnê, vol. 12, no. 5, pp. 75-82, 2015.

A. Hock and A. Schoelling, “Distributed iterative learning control for multi-agent systems,” Autonomous Robots, vol. 43, no. 8, pp. 1989–2010, 2019.

G. Li, D. Onge, C. Pinciroli, A. Gasparri, E. Garone, and G. Beltrame, “Decentralized progressive shape formation with robot swarms,” Autonomous Robots, vol. 43, no. 6, pp. 1505–1521, 2019.

T. Rijavec, J. Zrimec, R. Spanning, and A. Lapanje, “Natural microbial communities can be manipulated by artificially constructed biofilms,” Advanced Science, vol. 6, no. 22, pp. 1–12, 2019.

M. Schuster, D. Sexton, and B. Hense, “Why quorum sensing controls private goods,” Frontiers in Microbiology, vol. 8, no. 885, pp. 1–16, 2017.

S. Mukherjee, and B. Bassler, “Bacterial quorum sensing in complex and dynamically changing environments,” Nature Review Microbiology, vol. 17, no. 1, pp. 371–382, 2019.

S. McAnulty and S. Spencer, “The role of hemocytes in the hawaiian bobtail squid, euprymna scolopes: A model organism for studying beneficial host-microbe interactions,” Frontiers in Microbiology, vol. 7, no. 2013, pp. 1–8, 2017.

L. Tanet, C. Tamburini, C. Baumas, M. Garel, G. Simon, and L. Casalot, “Bacterial bioluminescence: Light emission in photobacterium phosphoreum is not under quorum-sensing control,” Frontiers in Microbiology, vol. 10, no. 365, pp. 1–9, 2019.

N. Tabassum, “Quorum Sensing-A Communication Pathway for Behavioural Synchronization in Bacteria,” International Journal of Medical Studies, vol. 4, no. 1, pp. 7-11, 2019.

A. Rasouli, P. Lanillos, G. Cheng, and J. Tsotsos, “Attention-based active visual search for mobile robots,” Autonomous Robots, vol. 44, no. 2, pp. 131–146, 2020.

J. Postat and P. Bousso, “Quorum sensing by monocyte-derived populations,” Frontiers in Immunology, vol. 10, no. 2140, pp. 1–7, 2019.

P. Cartledge, Cultures of Voting in Pre-modern Europe. Taylor & Francis Group, 1 ed., 2018.

B. Ichter, and M. Pavone, “Robot Motion Planning in Learned Latent Spaces,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2407-2414, 2019.

F. Martínez, A. Rendón, and M. Arbulú, “A data-driven path planner for small autonomous robots using deep regression models,” Lecture Notes in Computer Science, vol. 10943, no. 1, pp. 596–603, 2018.

F. Martínez, E. Jacinto, and H. Montiel, “Neuronal environmental pattern recognizer: Optical-by-distance LSTM model for recognition of navigation patterns in unknown environments,” Communications in Computer and Information Science, vol. 1071, no. 1, pp. 220–227, 2019.

J. Caley, N. Lawrance, and G. Hollinger, “Deep learning of structured environments for robot search,” Autonomous Robots, vol. 43, no. 7, pp. 1695–1714, 2019.

M. Castiblanco, and F. Martínez, “Exploración de un modelo comportamental basado en el Quorum Sensing bacterial para describir la interacción entre individuos,” Tekhnê, vol. 11, no. 1, pp. 21-26, 2014.

D. Ezzat, S. Amin, H. Shedeed, and M. Tolba, “A New Nano-robots Control Strategy for Killing Cancer Cells Using Quorum Sensing Technique and Directed Particle Swarm Optimization Algorithm,” Advances in Intelligent Systems and Computing, vol. 921, no. 1, pp. 218-226, 2019.

G. Cai, and D. Sofge, “An Urgency Dependent Quorum Sensing Algorithm for N-SiteSelection in Autonomous Swarms,” 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), pp. 1853-1855, 2019.




DOI: http://dx.doi.org/10.18517/ijaseit.12.1.11703

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development