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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Megan Emmons, Anthony A. Maciejewski, Charles Anderson and Edwin K. P. Chong, "Classifying Environmental Features From Local Observations of Emergent Swarm Behavior," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 674-682, May 2020. doi: 10.1109/JAS.2020.1003129
Citation: Megan Emmons, Anthony A. Maciejewski, Charles Anderson and Edwin K. P. Chong, "Classifying Environmental Features From Local Observations of Emergent Swarm Behavior," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 674-682, May 2020. doi: 10.1109/JAS.2020.1003129

Classifying Environmental Features From Local Observations of Emergent Swarm Behavior

doi: 10.1109/JAS.2020.1003129
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  • Robots in a swarm are programmed with individual behaviors but then interactions with the environment and other robots produce more complex, emergent swarm behaviors. One discriminating feature of the emergent behavior is the local distribution of robots in any given region. In this work, we show how local observations of the robot distribution can be correlated to the environment being explored and hence the location of openings or obstructions can be inferred. The correlation is achieved here with a simple, single-layer neural network that generates physically intuitive weights and provides a degree of robustness by allowing for variation in the environment and number of robots in the swarm. The robots are simulated assuming random motion with no communication, a minimalist model in robot sophistication, to explore the viability of cooperative sensing. We culminate our work with a demonstration of how the local distribution of robots in an unknown, office-like environment can be used to locate unobstructed exits.

     

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  • [1]
    I. Navarro and F. Matía, “An introduction to swarm robotics,” Int. Scholarly Research Notices Robotics, vol. 2013, 2012. doi: 10.5402/2013/608164
    [2]
    R. L. Sturdivant and E. K. P. Chong, “The necessary and sufficient conditions for emergence in systems applied to symbol emergence in robots,” IEEE Trans. Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1035–1042, Dec. 2018. doi: 10.1109/TCDS.2017.2731361
    [3]
    M. Emmons, A. A. Maciejewski, and E. K. P. Chong, “Modelling emergent swarm behavior using continuum limits for environmental mapping,” in Proc. IEEE Int. Conf. Control and Automation, Jun. 2018, pp. 86–93.
    [4]
    C. A. C. Parker and H. Zhang, “Cooperative decision-making in decentralized multiple-robot systems: the best-of-n problem,” IEEE/ASME Trans. Mechatronics, vol. 14, no. 2, pp. 240–251, Apr. 2009. doi: 10.1109/TMECH.2009.2014370
    [5]
    J. T. Ebert, M. Gauci, and R. Nagpal, “Multi feature collective decision making in robot swarms,” in Proc. 17th Int. Conf. Autonomous Agents and Multiagent Systems, Jul. 2018.
    [6]
    M. Schneider-Fontan and M. J. Mataric, “Territorial multi-robot task division,” IEEE Trans. Robotics and Automation, vol. 14, no. 5, pp. 815–822, Oct. 1998. doi: 10.1109/70.720357
    [7]
    B. P. Gerkey and M. J. Matarić, “A formal analysis and taxonomy of task allocation in multi-robot systems,” The Int. J. Robotics Research, vol. 23, no. 9, pp. 939–953, 2004. doi: 10.1177/0278364904045564
    [8]
    M. J. Matarić, G. S. Sukhatme, and E. H. Østergaard, “Multi-robot task allocation in uncertain environments,” Autonomous Robots, vol. 14, no. 2, pp. 255–263, Mar. 2003.
    [9]
    D. Fox, W. Burgard, H. Kruppa, and S. Thrun, “A probabilistic approach to collaborative multi-robot localization,” Autonomous Robots, vol. 8, no. 3, pp. 325–344, Jun. 2000. doi: 10.1023/A:1008937911390
    [10]
    A. Prorok, A. Bahr, and A. Martinoli, “Low-cost collaborative localization for large-scale multi-robot systems,” in Proc. IEEE Int. Conf. Robotics and Automation, May 2012, pp. 4236–4241.
    [11]
    J. Chen, K. H. Low, Y. Yao, and P. Jaillet, “Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems,” IEEE Trans. Automation Science and Engineering, vol. 12, no. 3, pp. 901–921, Jul. 2015. doi: 10.1109/TASE.2015.2422852
    [12]
    A. Giusti, J. Nagi, L. Gambardella, and G. A. D. Caro, “Cooperative sensing and recognition by a swarm of mobile robots,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Oct. 2012, pp. 551–558.
    [13]
    K. H. Low, W. K. Leow, and J. Marcelo H. Ang, “Autonomic mobile sensor network with self-coordinated task allocation and execution,” IEEE Trans. Systems,Man,and Cybernetics,Part C (Applications and Reviews), vol. 36, no. 3, pp. 315–327, May 2006. doi: 10.1109/TSMCC.2006.871590
    [14]
    S. Bandyopadhyay, S.-J. Chung, and F. Y. Hadaegh, “Probabilistic and distributed control of a large-scale swarm of autonomous agents,” IEEE Trans. Robotics, vol. 33, no. 5, pp. 1103–1123, Oct. 2017. doi: 10.1109/TRO.2017.2705044
    [15]
    A. Dirafzoon and E. Lobaton, “Topological mapping of unknown environments using an unlocalized robotic swarm,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 5545–5551, Nov. 2013.
    [16]
    S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and largescale data collection,” The Int. J. Robotics Research, vol. 34, no. 4–5, pp. 421–436, 2018.
    [17]
    J. Morelli, P. Zhu, B. Doerr, R. Linares, and S. Ferrari, “Integrated mapping and path planning for very large-scale robotic (vlsr) systems,” in Proc. Int. Conf. Robotics and Automation, May 2019, pp. 3356–3362.
    [18]
    L. Hou, F. Fan, J. Fu, and J. Wang, “Time-varying algorithm for swarm robotics,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 217–222, Jan. 2018. doi: 10.1109/JAS.2017.7510685
    [19]
    S. Berman, V. Kumar, and R. Nagpal, “Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination,” in Proc. IEEE Int. Conf. Robotics and Automation, May 2011, pp. 378–385.
    [20]
    K. Elamvazhuthi, H. Kuiper, and S. Berman, “PDE-based optimization for stochastic mapping and coverage strategies using robotic ensembles,” Automatica, vol. 95, pp. 356–367, Sept. 2018. doi: 10.1016/j.automatica.2018.06.007
    [21]
    L. E. Barnes, M. A. Fields, and K. P. Valavanis, “Swarm formation control utilizing elliptical surfaces and limiting functions,” IEEE Trans. Systems,Man,and Cybernetics,Part B, vol. 39, no. 6, pp. 1434–1445, Dec. 2009. doi: 10.1109/TSMCB.2009.2018139
    [22]
    F. Mondada, M. Bonani, X. Raemy, J. Pugh, C. Cianci, A. Klaptocz, S. Magnenat, J.-C. Zufferey, D. Floreano, and A. Martinoli, “The e-puck, a robot designed for education in engineering,” in Proc. 9th Conf. Autonomous Robot Systems and Competitions, vol. 1, no. 1, pp. 59–65, 2009. [Online]. Available: http://infoscience.epfl.ch/record/135236
    [23]
    F. Mondada, E. Franzi, and P. Ienne, “Mobile robot miniaturisation: a tool for investigation in control algorithms,” Experimental Robotics III, vol. 200, pp. 501–513, 2009.
    [24]
    M. Rubenstein, C. Ahler, and R. Nagpal, “Kilobot: a low cost scalable robot system for collective behaviors,” in Proc. IEEE Int. Conf. Robotics and Automation, May 2012, pp. 3293–3298.
    [25]
    C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2013.
    [26]
    B. Yamauchi, “A frontier-based approach for autonomous exploration,” in Proc. Int. Symp. Computational Intelligence in Robotics and Automation, Jul. 1997, pp. 146–151.

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    Highlights

    • Demonstrate the locally observed distribution of a robot swarm can be correlated to the location of openings in office-like environments
      a) Simulated robots are extremely simple - they are not equipped with any communication abilities and only use random motion to explore the environment
      b) Correlation between locally observed robot density and global environmental features can be achieved with a simple, single-layer neural network
      c) Only information required to predict environment class is a local observation of the robot density.
    • Can use the correlation to predict the location of exits in an office-like environment without requiring any communication or path-planning algorithms.
    • Approach is extremely robust: environments can still be classified at better than random accuracy following a 90% loss in the number of robots functioning
      a) Extending the classification process, after a 90% decrease in the number of robots, an office worker can still observe the number of robots around them and move away from the least likely exit location to further increase the classification accuracy of the system.

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