Advertisement

Optimized NURBS Curves Modelling Using Genetic Algorithm for Mobile Robot Navigation

  • Sawssen JalelEmail author
  • Philippe Marthon
  • Atef Hamouda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

This paper presents a new approach for solving one of the crucial robotic tasks: the global path planning problem. It consists in calculating the existing optimal path, for a non-point, non-holonomic robot, from start to goal position in terms of Non Uniform Rational B-Spline (NURBS) curve. With a priori knowledge of the environment and the robot characteristics (size and radius of curvature), the algorithm begins by selecting a set of control points derived from the shortest, collision-free polyline path. Then, an optimized NURBS curve modelling using Genetic Algorithm (GA) is introduced to replace that polyline path by a smooth curvature-constrained curve which avoids obstacles. Computer simulation studies demonstrate the effectiveness of the proposed method.

Keywords

NURBS curves parameterization Robot path planning Path smoothing Curvature constraint Genetic algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Khatib, O.: Real time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research 5, 90–98 (1986)CrossRefGoogle Scholar
  2. 2.
    Bigaj, P., Kacprzyk, J.: A memetic algorithm based procedure for a global path planning of a movement constrained mobile robot. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, pp. 135–141 (2013)Google Scholar
  3. 3.
    Tu, J., Yang, S.X.: Genetic algorithm based path planning for a mobile robot. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1221–1226 (2003)Google Scholar
  4. 4.
    Gemeinder, M., Gerke, M.: GA-based path planning for mobile robot systems employing an active search algorithm. Appl. Soft Comput. 3, 149–158 (2003)CrossRefGoogle Scholar
  5. 5.
    Sedighi, K.H., Ashenayi, K., Manikas, T.W., Wainwright, R.L., Tai, H.M.: Autonomous local path planning for a mobile robot using a genetic algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1338–1345 (2004)Google Scholar
  6. 6.
    Yun, S.C., Ganapathy, V., Chong, L.O.: Improved genetic algorithms based optimum path planning for mobile robot. In: 11th International Conference on Control, Automation, Robotics and Vision(ICARCV), pp. 1565–1570 (2010)Google Scholar
  7. 7.
    Tamilselvi, D., Shalinie, S.M., Thasneem, A.F., Sundari, S.G.: Optimal path selection for mobile robot navigation using genetic algorithm in an indoor environment. In: Thilagam, P.S., Pais, A.R., Chandrasekaran, K., Balakrishnan, N. (eds.) ADCONS 2011. LNCS, vol. 7135, pp. 263–269. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Tuncer, A., Yildirim, A.: Dynamic path planning of mobile robots with improved genetic algorithm. Computers & Electrical Engineering 38, 1564–1572 (2012)CrossRefGoogle Scholar
  9. 9.
    Qu, H., Xing, K., Alexander, T.: An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing. 120, 509–517 (2013)CrossRefGoogle Scholar
  10. 10.
    Ahmed, F., Deb, K.: Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput. 17, 1283–1299 (2013)CrossRefGoogle Scholar
  11. 11.
    Jalel, S., Marthon, P., Hamouda, A.: Optimum path planning for mobile robots in static environments using graph modelling and NURBS curves. In:12th WSEAS International Conference on Signal Processing, Robotics and Automation(ISPRA), pp. 216–221 (2013)Google Scholar
  12. 12.
    Piegl, L., Tiller, W.: The NURBS book, 2nd edn. Springer, Heidelberg (1997) CrossRefGoogle Scholar
  13. 13.
    Adi, D.I.S., Shamsuddin, S.M., Ali, A.: Particle swarm optimization for NURBS curve fitting. In: Sixth International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends(CGIV), pp. 259–263 (2009)Google Scholar
  14. 14.
    Jing, Z., Shaowei, F., Hanguo, C., Optimized NURBS curve and surface modelling using simulated evolution algorithm. In: Second International Workshop on Computer Science and Engineering (WCSE), pp. 435–439 (2009)Google Scholar
  15. 15.
    Singh, A.K., Aggarwal, A., Vashisht, M., Siddavatam, R.: Robot motion planning in a dynamic environment using offset non-uniform rational B-splines (NURBS). In: ICIT, pp. 312–317. IEEE (2011)Google Scholar
  16. 16.
    Xidias, E.K., Aspragathos, N.A.: Continuous curvature constrained shortest path for a car-like robot using S-Roadmaps. In: MED, pp. 13–18. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sawssen Jalel
    • 1
    • 2
    Email author
  • Philippe Marthon
    • 2
  • Atef Hamouda
    • 1
  1. 1.LIPAH Research Laboratory, Faculty of Sciences of TunisTunis El Manar UniversityTunisTunisia
  2. 2.Site ENSEEIHT de l’Institut de Recherche en Informatique de Toulouse (IRIT)University of ToulouseToulouseFrance

Personalised recommendations