Information-based Optimal Deployment for a Group of Dynamic Unicycles

  • Maode Yan
  • Yaoren Guo
  • Lei ZuoEmail author
  • Panpan Yang
Regular Papers Control Theory and Applications


In this paper, we investigate the optimal deployment problems for a group of dynamic unicycles in a given region. The information of interests over this region is considered such that the region with higher interests can get more attentions. Firstly, we present a cost function to describe the performance of multi-unicycle systems and find out the optimal locations for unicycles. Then, we transform this information-based optimal deployment problem into a moving target tracking problem and propose a novel kinematic control protocol to drive unicycles to the optimal locations. On this basis, an algorithm is provided for the dynamic unicycles by using the back-stepping techniques. Moreover, we demonstrate the stability and convergence of the proposed multi-unicycle systems. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed approaches.


Back-stepping Techniques cost function dynamic unicycles information-based optimal deployment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Z. Li, W. Ren, X. Liu, and M. Fu, “Consensus of multiagent systems with general linear and lipschitz nonlinear dynamics using distributed adaptive protocols,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1786–1791, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  2. [2]
    H. Li and Y. Shi, “Robust distributed model predictive control of constrained continuous-time nonlinear systems: A robustness constraint approach,” IEEE Transactions on Automatic Control, vol. 59, no. 6, pp. 1673–1678, 2014.CrossRefGoogle Scholar
  3. [3]
    R. Cui, C. Yang, Y. Li, and S. Sharma, “Adaptive neural network control of auvs with control input nonlinearities using reinforcement learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 6, pp. 1019–1029, 2017.CrossRefGoogle Scholar
  4. [4]
    C. Hua, Y. Li, and X. Guan, “Leader-following consensus for high-order nonlinear stochastic multiagent systems,” IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 1882–1891, 2017.CrossRefGoogle Scholar
  5. [5]
    Y. Li, C. Hua, S. Wu, and X. Guan, “Output feedback distributed containment control for high-order nonlinear multiagent systems,” IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 2032–2043, 2017.CrossRefGoogle Scholar
  6. [6]
    C. Song, L. Liu, G. Feng, Y. Wang, and Q. Gao, “Persistent awareness coverage control for mobile sensor networks,” Automatica, vol. 49, no. 6, pp. 1867–1873, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    M. Schwager, D. Rus, and J.-J. Slotine, “Unifying geometric, probabilistic, and potential field approaches to multirobot deployment,” The International Journal of Robotics Research, vol. 30, no. 3, pp. 371–383, 2011.CrossRefzbMATHGoogle Scholar
  8. [8]
    F. Bullo, R. Carli, and P. Frasca, “Gossip coverage control for robotic networks: Dynamical systems on the space of partitions,” SIAM Journal on Control and Optimization, vol. 50, no. 1, pp. 419–447, 2012.MathSciNetCrossRefzbMATHGoogle Scholar
  9. [9]
    J. Le Ny and G. J. Pappas, “Adaptive deployment of mobile robotic networks,” IEEE Transactions on automatic control, vol. 58, no. 3, pp. 654–666, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    B. Mu, G. Chowdhary, and J. P. How, “Efficient distributed sensing using adaptive censoring-based inference,” Automatica, vol. 50, no. 6, pp. 1590–1602, 2014.MathSciNetCrossRefzbMATHGoogle Scholar
  11. [11]
    C. Rizzo, D. Tardioli, D. Sicignano, L. Riazuelo, J. L. Villarroel, and L. Montano, “Signal-based deployment planning for robot teams in tunnel-like fading environments,” The International Journal of Robotics Research, vol. 32, no. 12, pp. 1381–1397, 2013.CrossRefGoogle Scholar
  12. [12]
    J.-S. Marier, C. A. Rabbath, and N. Léchevin, “Healthaware coverage control with application to a team of small UAVs,” IEEE Transactions on Control Systems Technology, vol. 21, no. 5, pp. 1719–1730, 2013.CrossRefGoogle Scholar
  13. [13]
    J. Cortes, S. Martinez, T. Karatas, and F. Bullo, “Coverage control for mobile sensing networks,” IEEE Transactions on Robotics and Automation, vol. 20, no. 2, pp. 243–255, 2004.CrossRefGoogle Scholar
  14. [14]
    M. Schwager, D. Rus, and J.-J. Slotine, “Decentralized, adaptive coverage control for networked robots,” The International Journal of Robotics Research, vol. 28, no. 3, pp. 357–375, 2009.CrossRefGoogle Scholar
  15. [15]
    Y. Ru and S. Martinez, “Coverage control in constant flow environments based on a mixed energy-time metric,” Automatica, vol. 49, no. 9, pp. 2632–2640, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  16. [16]
    S. Alamdari, E. Fata, and S. L. Smith, “Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations,” The International Journal of Robotics Research, vol. 33, no. 1, pp. 138–154, 2014.CrossRefGoogle Scholar
  17. [17]
    T. M. Cheng and A. V. Savkin, “Self-deployment of mobile robotic sensor networks for multilevel barrier coverage,” Robotica, vol. 30, no. 4, pp. 661–669, 2012.CrossRefGoogle Scholar
  18. [18]
    J. Cortés, “Deployment of an unreliable robotic sensor network for spatial estimation,” Systems & Control Letters, vol. 61, no. 1, pp. 41–49, 2012.MathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    J. W. Durham, R. Carli, P. Frasca, and F. Bullo, “Discrete partitioning and coverage control for gossiping robots,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 364–378, 2012.CrossRefGoogle Scholar
  20. [20]
    K. Savla, E. Frazzoli, and F. Bullo, “Traveling salesperson problems for the Dubins vehicle,” IEEE Transactions on Automatic Control, vol. 53, no. 6, pp. 1378–1391, 2008.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    I. I. Hussein, D. M. Stipanovic, and Y. Wang, “Reliable coverage control using heterogeneous vehicles,” in Proceedings of the 46th IEEE Conference on Decision and Control, IEEE, pp. 6142–6147, 2007.Google Scholar
  22. [22]
    L. Zuo, W. Yan, R. Cui, and J. Gao, “A coverage algorithm for multiple autonomous surface vehicles in flowing environments,” International Journal of Control, Automation and Systems, vol. 14, no. 2, pp. 540–548, 2016.CrossRefGoogle Scholar
  23. [23]
    A. Kwok and S. Martìnez, “Unicycle coverage control via hybrid modeling,” IEEE Transactions on Automatic Control, vol. 55, no. 2, pp. 528–532, 2010.MathSciNetCrossRefzbMATHGoogle Scholar
  24. [24]
    L. Zuo, J. Chen, W. Yan, and Y. Shi, “Time-optimal coverage control for multiple unicycles in a drift field,” Information Sciences, vol. 373, pp. 571–580, 2016.CrossRefGoogle Scholar
  25. [25]
    Q. Du, V. Faber, and M. Gunzburger, “Centroidal Voronoi tessellations: Applications and algorithms,” SIAM review, vol. 41, no. 4, pp. 637–676, 1999.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic and Control EngineeringChang’an UniversityXi’anP. R. China

Personalised recommendations