Advertisement

Experimental Evaluation of Certain Pursuit and Evasion Schemes for Wheeled Mobile Robots

  • Amit KumarEmail author
  • Aparajita Ojha
Research Article

Abstract

Pursuit-evasion games involving mobile robots provide an excellent platform to analyze the performance of pursuit and evasion strategies. Pursuit-evasion has received considerable attention from researchers in the past few decades due to its application to a broad spectrum of problems that arise in various domains such as defense research, robotics, computer games, drug delivery, cell biology, etc. Several methods have been introduced in the literature to compute the winning chances of a single pursuer or single evader in a two-player game. Over the past few decades, proportional navigation guidance (PNG) based methods have proved to be quite effective for the purpose of pursuit especially for missile navigation and target tracking. However, a performance comparison of these pursuer-centric strategies against recent evader-centric schemes has not been found in the literature, for wheeled mobile robot applications. With a view to understanding the performance of each of the evasion strategies against various pursuit strategies and vice versa, four different proportional navigation-based pursuit schemes have been evaluated against five evader-centric schemes and vice-versa for non-holonomic wheeled mobile robots. The pursuer′s strategies include three well-known schemes namely, augmented ideal proportional navigation guidance (AIPNG), modified AIPNG, angular acceleration guidance (AAG), and a recently introduced pursuer-centric scheme called anticipated trajectory-based proportional navigation guidance (ATPNG). Evader-centric schemes are classic evasion, random motion, optical-flow based evasion, Apollonius circle based evasion and another recently introduced evasion strategy called anticipated velocity based evasion. The performance of each of the pursuit methods was evaluated against five different evasion methods through hardware implementation. The performance was analyzed in terms of time of interception and the distance traveled by players. The working environment was obstacle-free and the maximum velocity of the pursuer was taken to be greater than that of the evader to conclude the game in finite time. It is concluded that ATPNG performs better than other PNG-based schemes, and the anticipated velocity based evasion scheme performs better than the other evasion schemes.

Keywords

Pursuit-evasion wheeled mobile robot proportional navigation trajectory planning target interception 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    T. H. Chung, G. A. Hollinger, V. Isler. Search and pursuit-evasion in mobile robotics: A survey. Autonomous Robots, Springer vol. 31, no. 4, pp. 299–316, 2011. DOI: 10.1007/s10514–011–9241–4.CrossRefGoogle Scholar
  2. [2]
    Y. Song, S. X. Li, C. F. Zhu, H. X. Chang. Object tracking with dual field–of–view switching in aerial videos. International Journal of Automation and Computing, vol. 13, no. 6, pp. 565–573, 2016. DOI: 10.1007/s11633–016–0949–7.CrossRefGoogle Scholar
  3. [3]
    B. Das, B. Subudhi, B. B. Pati. Cooperative formation control of autonomous underwater vehicles: An overview. International Journal of Automation and Computing, vol. 13, no. 3, pp. 199–225, 2016. DOI: 10.1007/s11633–016–1004–4.CrossRefGoogle Scholar
  4. [4]
    R. Vidal, S. Rashid, C. Sharp, O. Shakernia, J. Kim, S. Sastry. Pursuit–evasion games with unmanned ground and aerial vehicles. In Proceedings of IEEE International Conference on Robotics and Automation, Seoul, South Korea, pp. 2948–2955, 2001. DOI: 10.1109/ROBOT.2001.933069.Google Scholar
  5. [5]
    J. R. Britnell, M. Wildon. Finding a princess in a palace: A pursuit–evasion problem. The Electronic Journal of Combinatorics, vol. 20, no. 1, Article number 25, 2013.Google Scholar
  6. [6]
    B. K. Sahu, B. Subudhi. Adaptive tracking control of an autonomous underwater vehicle. International Journal of Automation and Computing, vol. 11, no. 3, pp. 299–307, 2014. DOI: 10.1007/s11633–014–0792–7.CrossRefGoogle Scholar
  7. [7]
    X. X. Sun, W. Yeoh, S. Koenig. Moving target D* lite*. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, ACM, Toronto, Canada, pp. 67–74, 2010. DOI: 10.1145/1838206. 1838216.Google Scholar
  8. [8]
    N. Basilico, N. Gatti, F. Amigoni. Patrolling security games: Definition and algorithms for solving large instances with single patroller and single intruder. Artificial Intelligence, vol. 184–185, pp. 78–123, 2012. DOI: 10.1016/j. artint.2012.03.003.Google Scholar
  9. [9]
    L. Freda, G. Oriolo. Vision–based interception of a moving target with a nonholonomic mobile robot. Robotics and Autonomous Systems, vol. 55, no. 6, pp. 419–432, 2007. DOI: 10.1016/j.robot.2007.02.001.CrossRefGoogle Scholar
  10. [10]
    Y. Tian, Y. Li, Z. Ren. Vision–based adaptive guidance law for intercepting a manoeuvring target. IET Control Theory & Applications, vol. 5, no. 3, pp. 421–428, 2011. DOI: 10.1049/iet–cta.2010.0092.MathSciNetCrossRefGoogle Scholar
  11. [11]
    J. P. Hwang, J. Baek, B. Choi, E. Kim. A novel part–based approach to mean–shift algorithm for visual tracking. International Journal of Control, Automation and Systems, vol. 13, no. 2, pp. 443–453, 2015. DOI: 10.1007/s12555–013–0483–0.CrossRefGoogle Scholar
  12. [12]
    R. J. Wai, Y. W. Lin. Adaptive moving–target tracking control of a vision–based mobile robot via a dynamic petri recurrent fuzzy neural network. IEEE Transactions on Fuzzy Systems, vol. 21, no. 4, pp. 688–701, 2013. DOI: 10.1109/TFUZZ.2012.2227974.Google Scholar
  13. [13]
    A. M. Rao, K. Ramji, B. S. K. Sundara Siva Rao, V. Vasu, C. Puneeth. Navigation of non–holonomic mobile robot using neuro–fuzzy logic with integrated safe boundary algorithm. International Journal of Automation and Computing, vol. 14, no. 3, pp. 285–294, 2017. DOI: 10.1007/s11633–016–1042–y.CrossRefGoogle Scholar
  14. [14]
    Q. C. Li, W. S. Zhang, G. Han, Y. H. Zhang. Finite time convergent wavelet neural network sliding mode control guidance law with impact angle constraint. International Journal of Automation and Computing, vol. 12, no. 6, pp. 588–599, 2015. DOI: 10.1007/s11633–015–0927–5.CrossRefGoogle Scholar
  15. [15]
    L. Q. Li, W. X. Xie. Bearings–only maneuvering target tracking based on fuzzy clustering in a cluttered environment. AEU — International Journal of Electronics and Communications, vol. 68, no. 2, pp. 130–137, 2014. DOI: 10.1016/j.aeue.2013.07.013.CrossRefGoogle Scholar
  16. [16]
    M. H. Amoozgar, S. H. Sadati, K. Alipour. Trajectory tracking of wheeled mobile robots using a kinematical fuzzy controller. International Journal of Robotics and Automation, vol. 27, no. 6, pp. 49–59, 2012. DOI: 10.2316/Journal.206.2012.1.206–3476.Google Scholar
  17. [17]
    N. Duan, H. F. Min. NN–based output tracking for more general stochastic nonlinear systems with unknown control coefficients. International Journal of Automation and Computing, vol. 14, no. 3, pp. 350–359, 2017. DOI: 10.1007/s11633–015–0936–4.CrossRefGoogle Scholar
  18. [18]
    F. Belkhouche, B. Belkhouche, P. Rastgoufard. Line of sight robot navigation toward a moving goal. IEEE Transactions on Systems, Man, and Cybernetics, Part B ( Cybernetics), vol. 36, no. 2, pp. 255–267, 2006. DOI: 10.1109/TSMCB. 2005.856142.CrossRefGoogle Scholar
  19. [19]
    C. Z. Zhao, Y. Huang. A D R C based integrated guidance and control scheme for the interception of maneuvering targets with desired LOS angle. In Proceedings of the 29th Chinese Control Conference, Beijing, China, pp. 6192–6196, 2010.Google Scholar
  20. [20]
    H. M. Prasanna, D. Ghose. Retro–proportional–navigation: A new guidance law for interception of high speed targets. Journal of Guidance, Control, and Dynamics, vol. 35, no. 2, pp. 377–386, 2012. DOI: 10.2514/1.54892.CrossRefGoogle Scholar
  21. [21]
    L. Yan, J. G. Zhao, H. R. Shen, Y. Li. Biased retro–proportional navigation law for interception of high–speed targets with angular constraint. Defence Technology, vol. 10, no. 1, pp. 60–65, 2014. DOI: 10.1016/j.dt.2013.12.010.CrossRefGoogle Scholar
  22. [22]
    C. H. Lee, T. H. Kim, M. J. Tahk. Biased PNG for target observability enhancement against nonmaneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 1, pp. 2–17, 2015. DOI: 10.1109/TAES. 2014.120103.CrossRefGoogle Scholar
  23. [23]
    Y. Li, L. Yan, J. G. Zhao, F. Liu, T. Wang. Combined proportional navigation law for interception of high–speed targets. Defence Technology, vol. 10, no. 3, pp. 298–303, 2014. DOI: 10.1016/j.dt.2014.07.004.CrossRefGoogle Scholar
  24. [24]
    D. Ghose. True proportional navigation with maneuvering target. IEEE Transactions on Aerospace and Electronic Systems, vol. 30, no. 1, pp. 229–237, 1994. DOI: 10.1109/7.250423.MathSciNetCrossRefGoogle Scholar
  25. [25]
    C. D. Yang, C. C. Yang. Optimal pure proportional navigation for maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 3, pp. 949–957, 1997. DOI: 10.1109/7.599315.Google Scholar
  26. [26]
    M. Mehrandezh, M. N. Sela, R. G. Fenton, B. Benhabib. Robotic interception of moving objects using ideal proportional navigation guidance technique. Robotics and Autonomous Systems, vol. 28, no. 4, pp. 295–310, 1999. DOI: 10.1016/S0921–8890(99)00044–5.CrossRefzbMATHGoogle Scholar
  27. [27]
    M. Mehrandezh, M. N. Sela, R. G. Fenton, B. Benhabib. Robotic interception of moving objects using an augmented ideal proportional navigation guidance technique. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, vol. 30, no. 3, pp. 238–250, 2000. DOI: 10.1109/3468.844351.CrossRefzbMATHGoogle Scholar
  28. [28]
    J. L. Gu, W. C. Chen. Optimal proportional navigation guidance based on generalized predictive control. In Proceedings of the 16th International Conference on System Theory, Control and Computing, Sinaia, Romania, 2012.Google Scholar
  29. [29]
    M. Keshmiri, M. Keshmiri. Performance comparison of various navigation guidance methods in interception of a moving object by a serial manipulator considering its kinematic and dynamic limits. In Proceedings of the 15th International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, pp. 212–217, 2010. DOI: 10.1109/MMAR.2010.5587234.Google Scholar
  30. [30]
    Y. Y. Song, W. C. Chen, X. L. Yin. A new angular acceleration guidance law with estimation approach based on sliding mode observer against high maneuvering target. Applied Mechanics and Materials, vol. 110–116, pp. 5249–5256, 2012. DOI: 10.4028/www.scientific.net/AMM.110–116.5249.Google Scholar
  31. [31]
    A. Kumar, A. Ojha, P. K. Padhy. Anticipated trajectory based proportional navigation guidance scheme for intercepting high maneuvering targets. International Journal of Control, Automation and Systems, vol. 15, no. 3, pp. 1351–1361, 2017. DOI: 10.1007/s12555–015–0166–0.CrossRefGoogle Scholar
  32. [32]
    S. Kim, H. J. Kim. Robust proportional navigation guidance against highly maneuvering targets. In Proceedings of the 13th International Conference on Control, Automation and Systems, Gwangju, South Korea, pp. 61–65, 2013. DOI: 10.1109/ICCAS.2013.6703864.Google Scholar
  33. [33]
    S. Ghosh, D. Ghose, S. Raha. Capturability of augmented pure proportional navigation guidance against time–varying target maneuvers. Journal of Guidance, Control, and Dynamics, vol. 37, no. 5, pp. 1446–1461, 2014. DOI: 10.2514/1.G000561.CrossRefGoogle Scholar
  34. [34]
    R. Isaacs. Differential Games: A Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization, New York, USA: John Wiley and Sons, 1965.zbMATHGoogle Scholar
  35. [35]
    P. A. Meschler. On constructing efficient evasion strategies for a game with imperfect information. IEEE Transactions on Automatic Control, vol. 15, no. 5, pp. 576–580, 1970. DOI: 10.1109/TAC.1970.1099558.MathSciNetCrossRefGoogle Scholar
  36. [36]
    W. Rzymowski. Avoidance of one pursuer. Journal of Mathematical Analysis and Applications, vol. 120, no. 1, pp. 89–94, 1986. DOI: 10.1016/0022–247X(86)90206–4.MathSciNetCrossRefzbMATHGoogle Scholar
  37. [37]
    W. Chodun. Differential games of evasion with many pursuers. Journal of Mathematical Analysis and Applications, vol. 142, no. 2, pp. 370–389, 1989. DOI: 10.1016/0022–247X(89)90007–3.MathSciNetCrossRefzbMATHGoogle Scholar
  38. [38]
    G. I. Ibragimov, M. Salimi, M. Amini. Evasion from many pursuers in simple motion differential game with integral constraints. European Journal of Operational Research, vol. 218, no. 2, pp. 505–511, 2012. DOI: 10.1016/j.ejor.2011. 11.026.MathSciNetCrossRefzbMATHGoogle Scholar
  39. [39]
    S. Y. Liu, Z. Y. Zhou, C. Tomlin, K. Hedrick. Evasion as a team against a faster pursuer. In Proceedings of American Control Conference, Washington, USA, pp. 5368–5373, 2013. DOI: 10.1109/ACC.2013.6580676.Google Scholar
  40. [40]
    D. Pais, N. E. Leonard. Pursuit and evasion: Evolutionary dynamics and collective motion. In Proceedings of AIAA Guidance, Navigation, and Control Conference, Toronto, Canada, 2010. DOI: 10.2514/6.2010–7584.Google Scholar
  41. [41]
    M. V. Raman, M. Kothari. Pursuit–evasion games of high speed evader. Journal of Intelligent & Robotic Systems, vol. 85, no. 2, pp. 293–306, 2017. DOI: 10.1007/s10846–016–0379–3.CrossRefGoogle Scholar
  42. [42]
    A. Kumar, A. Ojha. Anticipated velocity based guidance strategy for wheeled mobile evader amidst stationary and moving obstacles in bounded environment. Computer Animation & Virtual Worlds, vol. 26, no. 5, pp. 495–507, 2015. DOI: 10.1002/cav.1609.CrossRefGoogle Scholar
  43. [43]
    G. Foderaro, A. Swingler, S. Ferrari. A model–based cell decomposition approach to on–line pursuit–evasion path planning and the video game Ms. Pac–Man. In Proceedings of IEEE Conference on Computational Intelligence and Games, Granada, Spain, pp. 281–287, 2012. DOI: 10.1109/CIG.2012.6374167.Google Scholar
  44. [44]
    E. Bakolas. Evasion from a group of pursuers with double integrator kinematics. In Proceedings of the 52nd IEEE Conference on Decision and Control, Florence, Italy, pp. 1472–1477, 2013. DOI: 10.1109/CDC.2013.6760090.CrossRefGoogle Scholar
  45. [45]
    B. Li, R. Chiong, L. G. Gong. Search–evasion path planning for submarines using the artificial bee colony algorithm. In Proceedings of IEEE Congress on Evolutionary Computation, Beijing, China, pp. 528–535, 2014. DOI: 10.1109/CEC.2014.6900224.Google Scholar
  46. [46]
    I. Exarchos, P. Tsiotras. An asymmetric version of the two car pursuit–evasion game. In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, USA, pp. 4272–4277, 2014. DOI: 10.1109/CDC.2014.7040055.CrossRefGoogle Scholar
  47. [47]
    J. C. Las Fargeas, P. T. Kabamba, A. R. Girard. Path planning for information acquisition and evasion using marsupial vehicles. In Proceedings of American Control Conference, Chicago, USA, pp. 3734–3739, 2015. DOI: 10.1109/ACC.2015.7171910.Google Scholar
  48. [48]
    W. Sun, P. Tsiotras. Pursuit evasion game of two players under an external flow field. In Proceedings of American Control Conference, Chicago, USA, pp. 5617–5622, 2015. DOI: 10.1109/ACC.2015.7172219.Google Scholar
  49. [49]
    P. J. Yuan, J. S. Chem. Ideal proportional navigation. Journal of Guidance, Control, and Dynamics, vol. 15, no. 5, pp. 1161–1165, 1992. DOI: 10.2514/3.20964.CrossRefGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and EngineeringIndian Institute of Information TechnologyKota, JaipurIndia
  2. 2.Computer Science and EngineeringIndian Institute of Information Technology Design and ManufacturingJabalpurIndia

Personalised recommendations