Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Equilibrium point smooth escape algorithm for local route of unmanned surface vehicles

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 18/07/2018, Accepted 02/08/2018, Revised 27/07/2018, Published 16/08/2018

For the problem of local equilibrium point in local path planning, a local equilibrium point smooth escape algorithm is proposed. In this method, a confirmation rule for obstructing obstacles is established to confirm obstacles obstructing routes of an unmanned surface vehicle and causing local equilibrium point from multiple detecting targets. A model for tracking point confirmation is constructed to guide the unmanned surface vehicle to bypass obstructing obstacles safely while tracking points. A tracking point smooth switching model is constructed to ensure smooth change of heading planned by the algorithm in tracking point switching. The idea of ‘wall-following’ algorithm and curve smoothing idea are integrated in the model and then a heading planning method escaping local equilibrium point is given. Simulation results show that the above algorithm can guide the unmanned surface vehicle out of the area where it is trapped when it falls into a local equilibrium point. The algorithm can improve the traceability of planned paths with a heading change rate much lower than that of conventional algorithms.

Inspec keywords: object detection; collision avoidance; path planning

Other keywords: local equilibrium point; unmanned surface vehicle; local path planning; local equilibrium point smooth escape algorithm; point switching; point confirmation; tracking point smooth switching model

Subjects: Mobile robots; Spatial variables control

References

    1. 1)
      • 14. Dash, T.: ‘Automatic navigation of wall following mobile robot using adaptive resonance theory of type-1’, Biol. Inspired Cog. Archit., 2015, 12, pp. 1–8.
    2. 2)
      • 13. Borenstein, J., Koren, Y.: ‘The vector field histogram-fast obstacle avoidance for mobile robots’, IEEE Trans. Robot. Autom., 1991, 7, (3), pp. 278–288.
    3. 3)
      • 18. Koyuncu, E., Inalhan, G.: ‘A probabilistic b-spline motion planning algorithm for unmanned helicopters flying in dense 3d environments’. IEEE/RSJ Int. Conf. Intelligent Robots and System, Nice, France, 2008, pp. 815–821.
    4. 4)
      • 6. Kim, H., Kim, S.H., Jeon, M., et al: ‘A study on path optimization method of an unmanned surface vehicle under environmental loads using genetic algorithm’, Ocean Eng., 2017, 142, pp. 616–624.
    5. 5)
      • 11. Borenstein, J., Koren, Y.: ‘Real-time obstacle avoidance for fast mobile robots’, IEEE Trans. Syst. Man Cybern., 1989, 1, (5), pp. 1179–1187.
    6. 6)
      • 4. Maki, T., Ura, T., Sakamaki, T., et al: ‘Application of A* algorithm for real-time path re-planning of an unmanned surface vehicle avoiding underwater obstacles’, J. Mar. Sci. Appl., 2014, 13, (1), pp. 105–116.
    7. 7)
      • 17. Sahingoz, O.K.: ‘Generation of Bezier curve-based flyable trajectories for multi-UAV systems with parallel genetic algorithm’, J. Intell. Robot. Syst., 2014, 74, (1–2), pp. 499–511.
    8. 8)
      • 8. Liu, Y.C., Bucknall, R.: ‘Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment’, Ocean Eng., 2015, 97, pp. 126–144.
    9. 9)
      • 5. Naeem, W.W., Irwin, G., Yang, A.: ‘COLREGs-based collision avoidance strategies for unmanned surface vehicles’, Mechatronics (Oxf), 2012, 22, (6), pp. 669–678.
    10. 10)
      • 1. Peng, Y., Yang, Y., Cui, J.X., et al: ‘Development of the USV ‘JingHai-I’ and sea trials in the Southern Yellow Sea’, Ocean Eng., 2017, 131, pp. 186–196.
    11. 11)
      • 7. Wang, X.W., Shi, Y.P., Ding, D.Y., et al: ‘Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning’, Eng. Optim., 2016, 48, (2), pp. 299–316.
    12. 12)
      • 2. Howie, C., Sean, W., Kunnayut, E.A, et al: ‘Sensor-based exploration: incremental construction of the hierarchical generalized voronoi graph’, Int. J. Robotic Res., 2000, 19, pp. 126–148.
    13. 13)
      • 16. Yang, K., Sukkarieh, S.: ‘An analytical continuous-curvature path-smoothing algorithm’, IEEE Trans. Robot., 2010, 26, (3), pp. 561–568.
    14. 14)
      • 19. Piazzi, A., Bianco, C.G.L, Romano, M.: ‘B-Splines for the smooth path generation of wheeled mobile robots’, IEEE Trans. Robot., 2007, 23, (5), pp. 1089–1095.
    15. 15)
      • 20. Sun, T.Y., Huo, C.L., Tsai, S.J., et al: ‘Intelligent flight task algorithm for unmanned aerial vehicle’, Expert Syst. Appl., 2011, 38, (8), pp. 10036–10048.
    16. 16)
      • 15. Bibuli, M., Gasparri, A., Priolo, A., et al: ‘Virtual target based path-following guidance system for cooperative USV swarms’, IFAC Proc. Vol., 2012, 45, (27), pp. 362–367.
    17. 17)
      • 12. Ma, Y., Hu, M.Q., Yan, X.P.: ‘Multi-objective path planning for unmanned surface vehicle with currents effects’, ISA, 2018, 75, pp. 137–156.
    18. 18)
      • 9. Hetmaniok, E.: ‘Artificial bee colony algorithm used for solving some inverse problem in solidification of the binary alloy’, Key Eng. Mater., 2014, 662, pp. 749–755.
    19. 19)
      • 3. Yang, J.M., Tseng, C.M., Tseng, P.S.: ‘Path planning on satellite images for unmanned surface vehicles’, Int. J. Naval Archit. Ocean Eng., 2015, 7, (1), pp. 87–99.
    20. 20)
      • 10. Min, H.S., Lin, Y.H., Wang, S.J., et al: ‘Path planning of mobile robot by mixing experience with modified artificial potential field method’, Adv. Mech. Eng., 2015, 7, (12), pp. 90–98.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8313
Loading

Related content

content/journals/10.1049/joe.2018.8313
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address