Obstacles Avoidance Based on Switching Potential Functions

  • Giuseppe Fedele
  • Luigi D’Alfonso
  • Francesco Chiaravalloti
  • Gaetano D’Aquila


In this paper, a novel path planning and obstacles avoidance method for a mobile robot is proposed. This method makes use of a switching strategy between the attractive potential of the target and a new helicoidal potential field which allows to bypass an obstacle by driving the robot around it. The new technique aims at overcoming the local minima problems of the well-known artificial potentials method, caused by the summation of two (or more) potential fields. In fact, in the proposed approach, only a single potential is used at a time. The resulting proposed technique uses only local information and ensures high robustness, in terms of achieved performance and computational complexity, w.r.t. the number of obstacles. Numerical simulations, together with comparisons with existing methods, confirm a very robust behavior of the method, also in the case of a framework with multiple obstacles.


Path planning Obstacles avoidance Artificial potentials method Finite-time control 


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  1. 1.
    Mac, T.T., Copot, C., Tran, D.T., De Keyser, R.: Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, Available on-line 26 August 2016, ISSN 0921-8890.
  2. 2.
    Chu, K., Lee, M., Sunwoo, M.: Local path planning for Off-Road autonomous driving with avoidance of static obstacles. IEEE Trans. Intell. Transp. Syst. 13(4), 1599–1616 (2012)CrossRefGoogle Scholar
  3. 3.
    Zhang, H., Butzke, J., Likhachev, M.: Combining global and local planning with guarantees on completeness. In: IEEE International Conference on Robotics and Automation (ICRA), St. Paul, Minnesota, USA, pp 4500–4506 (2012)Google Scholar
  4. 4.
    Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. Springer Publishing Company Incorporated, Berlin (2008)Google Scholar
  5. 5.
    Dang, A.D., Horn, J.: Formation control of autonomous robots following desired formation during tracking a moving target. In: IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, pp 160–165 (2015)Google Scholar
  6. 6.
    Dang, A.D., La, H.M., Horn, J.: Distributed formation control for autonomous robots following desired shapes in noisy environment. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Baden-Baden, Germany, pp 285–290 (2016)Google Scholar
  7. 7.
    Al-Sultan, K.S., Aliyu, M.D.S.: A new potential field-based algorithm for path planning. J. Intell. Robot. Syst. 17(3), 265–282 (1996)CrossRefGoogle Scholar
  8. 8.
    Rosell, J., Iniguez, P.: Path planning using harmonic functions and probabilistic cell decomposition. In: International Conference on Robotics and Automation (ICRA), Barcelona, Spain, pp 1803–1808 (2005)Google Scholar
  9. 9.
    Seda, M.: Roadmap method vs. cell decomposition in robot motion planning. In: International Conference on Signal Processing, Robotics and Automation (WSEAS), Corfu Island, Greece, pp 127–132 (2007)Google Scholar
  10. 10.
    Choset, H., Lynch, K., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L., Thrun, S.: Principles of robot Motion-Theory, algorithms, and implementation. The MIT press, Cambridge (2005)zbMATHGoogle Scholar
  11. 11.
    Bopardikar, S.D., Englot, B., Speranzon, A.: Multiobjective path planning: localization constraints and collision probability. IEEE Trans. Robot. 31(3), 562–577 (2015)CrossRefGoogle Scholar
  12. 12.
    Franzé, G., Lucia, W.: The obstacle avoidance motion planning problem for autonomous vehicles: a low-demanding receding horizon control scheme. Syst. Control Lett. 77, 1–10 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Lau, B., Sprunk, C., Burgard, W.: Efficient grid-based spatial representations for robot navigation in dynamic environments. Robot. Auton. Syst. 61(10), 1116–1130 (2013)CrossRefGoogle Scholar
  14. 14.
    Chamberland, S., Beaudry, E., Clavien, L., Kabanza, F., Michaud, F., Lauriay, M.: Motion planning for an omnidirectional robot with steering constraints. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), Taipei, Taiwan, pp 4305–4310 (2010)Google Scholar
  15. 15.
    Kowalczyk, W., Przybyla, M., Kozlowski, K.: Set-point control of mobile robot with obstacle detection and avoidance using navigation function - experimental verification. J. Intell. Robot. Syst. 85(3–4), 1–14 (2016)Google Scholar
  16. 16.
    Yang, S.X., Meng, M.: An efficient neural network approach to dynamic robot motion planning. Neural Netw. 13(2), 143–148 (2000)CrossRefGoogle Scholar
  17. 17.
    Chen, X., Li, Y.: Smooth formation navigation of multiple mobile robots for avoiding moving obstacles. Int. J. Control. Autom. Syst. 4(4), 466–479 (2006)Google Scholar
  18. 18.
    Arajo, R.: Prune-Able Fuzzy ART neural architecture for robot map learning and navigation in dynamic environments. IEEE Trans. Neural Netw. 17(5), 1235–1249 (2006)CrossRefGoogle Scholar
  19. 19.
    Hui, N.B., Mahendar, V., Pratihar, D.K.: Time-optimal, collision-free navigation of a car-like mobile robot using neuron-fuzzy approaches. Fuzzy Set. Syst. 157(16), 2171–2204 (2006)CrossRefzbMATHGoogle Scholar
  20. 20.
    Alajlan, M., Koubaa, A., Chaari, I., Bennaceur, H., Ammar, A.: Global path planning for mobile robots in large-scale grid environments using genetic algorithms. In: IEEE International Conference on Individual and Collective Behaviors in Robotics (ICBR), Sousse, Tunisia, pp 1–8 (2013)Google Scholar
  21. 21.
    Karami, A.H., Hasanzadeh, M.: An adaptive genetic algorithm for robot motion planning in 2D complex environments. Comput. Electr. Eng. 43, 317–329 (2015)CrossRefGoogle Scholar
  22. 22.
    Yang, C., Simon, D.: A new particle swarm optimization technique. In: IEEE International Conference on Systems Engineering (ICSEng), Las Vegas, NV, USA, pp 164–169 (2005)Google Scholar
  23. 23.
    Couceiro, M.S., Machado, J.A.T., Rocha, R.P., Ferreira, N.M.F.: A fuzzified systematic adjustment of the robotic Darwinian PSO. Robot. Auton. Syst. 60(12), 1625–1639 (2012)CrossRefGoogle Scholar
  24. 24.
    Franzé, G., Lucia, W.: A receding horizon control strategy for autonomous vehicles in dynamic environments. IEEE Trans. Control Syst. Technol. 24(2), 695–702 (2016)CrossRefGoogle Scholar
  25. 25.
    Benzerrouk, A., Adouane, L., Martinet, P.: Dynamic obstacle avoidance strategies using limit cycle for the navigation of multi-robot system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4th Workshop on Planning Perception and Navigation for Intelligent Vehicles, Vilamoura, Algarve, Portugal (2012)Google Scholar
  26. 26.
    Kim, D., Kim, J.: A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer. Robot. Auton. Syst. 42(1), 17–30 (2003)CrossRefzbMATHGoogle Scholar
  27. 27.
    Tsoularis, A., Kambhampati, C.: On-line planning for collision avoidance on the nominal path. J. Intell. Robot. Syst. 21(4), 327–371 (1998)CrossRefzbMATHGoogle Scholar
  28. 28.
    Belkhous, S., Azzouz, A., Saad, M., Nerguizian, C., Nerguizian, V.: A novel approach for mobile robot navigation with dynamic obstacles avoidance. J. Intell. Robot. Syst. 44(3), 187–201 (2005)CrossRefGoogle Scholar
  29. 29.
    Park, M.G., Lee, M.C.: A new technique to escape local minimum in artificial potential field based path planning. KSME Int. J. 17(12), 1876–1885 (2003)CrossRefGoogle Scholar
  30. 30.
    Struik, D.J.: Lectures on classical differential geometry. Courier Corporation, North Chelmsford (2012)zbMATHGoogle Scholar
  31. 31.
    Stifter, S., Lenarcic, J.: Advances in robot kinematics, pp 227–235. Springer, Wien (1991)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Giuseppe Fedele
    • 1
  • Luigi D’Alfonso
    • 2
  • Francesco Chiaravalloti
    • 3
  • Gaetano D’Aquila
    • 2
  1. 1.Department of Informatics, Modeling, Electronics and System EngineeringUniversity of CalabriaRendeItaly
  2. 2.GiPStech s.r.lRendeItaly
  3. 3.IRPI - CNRRendeItaly

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