Navigation of Mobile Robot with Polygon Obstacles Avoidance Based on Quadratic Bezier Curves

  • Ammar A. AldairEmail author
  • Mofeed T. Rashid
  • Abdulmuttalib T. Rashid
Research Paper


Navigation and obstacle avoidance problems are introduced and solved in this paper. A new algorithm called quadratic Bezier curves is used to navigate a mobile robot in an unknown environment. The quadratic Bezier curve can be improved through a division and conquer technique whose basic operation is the generation of multiple midpoints on a specific curve. The platform of the mobile robot is constructed with three ultrasonic sensors placed around the front of the platform with 45° between them. These sensors are utilized to sense the locations of obstacles in any environment surrounding the mobile robot. Based on sensor(s) detection for obstacles in the environment of the mobile robot, five different scenarios are introduced and studied. To navigate the robot in the unknown environment, two main points are taken into consideration: Firstly, the smooth rotation of the mobile robot around the obstacles should be performed to avoid a collision; and secondly, the shortest path should be followed by the robot to reach a target with minimum time. Visual basic language is used to simulate the navigation of mobile robot in different environments. The quadratic Bezier curves algorithm is investigated real-time experiment, and the obtained results have proved the efficiency and evaluation of the proposed algorithm.


Navigation and obstacle avoidance problems Path planning Mobile robot Quadratic Bezier curves Visual basic language 


  1. Abadi D, Khooban M (2015) Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots. J King Saud Univ Eng Sci 27(1):92–100CrossRefGoogle Scholar
  2. Ahmadzadeh S, Ghanavati M (2012) Navigation of mobile robot using the PSO particle swarm optimization. J Acad Appl Stud: JAAS 2(1):32–38Google Scholar
  3. Askari A, Mortazavi M, Talebi HA, Motamedi A (2016) A new approach in UAV path planning using Bezier–Dubins continuous curvature path. Proc IMechE Part G J Aerosp Eng 230(6):1103–1113CrossRefGoogle Scholar
  4. Biagiotti L, Melchiorri C (2013) Online trajectory planning and filtering for robotic applications via B-spline smoothing filters. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 5668–5673Google Scholar
  5. Cederberg TW (2007) Computer aided geometric design. CAGD Course Notes, Brigham Young University, Provo, UT, 84602Google Scholar
  6. David M, Joseph T (2008) An optimal and opportunistic path planner (with obstacle avoidance) using Voronoi polygons. 2008 10th IEEE international workshop on advanced motion control, Trento, Italy, pp 371–376. 26–28 Mar 2008Google Scholar
  7. Dong H, Li W, Zhu J, Duan S (2010) The path planning for mobile robot based on Voronoi diagram. In: 2010 third international conference on intelligent networks and intelligent systems Shenyang, China, pp 446–449. 1–3 Nov 2010.
  8. Engedy I, Horvath G (2010) Artificial neural network based local motion planning of a wheeled mobile robot. In: IEEE international symposium on computational intelligence and informatics (CINTI), Hungary, pp 213–218Google Scholar
  9. Gue J, Gao Y, Cui G (2013) Path planning of mobile robot base on improved potential field. Inf Technol J 12(11):15–29Google Scholar
  10. Hoc T, Hai X (2016) Path planning and obstacle avoidance approaches for mobile robot. Int J Comput Sci Issues 13(4):1–10CrossRefGoogle Scholar
  11. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5(1):90–98CrossRefGoogle Scholar
  12. Louis A (2009) Orbital obstacle avoidance algorithm for reliable and on-line mobile robot navigation. In: 9th conference on autonomous robot systems and competitions, Castelo Branco, 7 May 2009Google Scholar
  13. Masehian E, Amin-Naseri M (2004) A Voronoi diagram-visibility graph—potential field compound algorithm for robot path planning. J Robot Syst 21(6):275–300CrossRefGoogle Scholar
  14. Miao H, Tian Y (2013) Dynamic robot path planning using an enhanced simulated annealing approach. Appl Math Comput 222:420–437zbMATHGoogle Scholar
  15. Mohamed EK (2014) Visibility graph for path planning in the presence of moving obstacles. Eng Sci Technol Int J: ESTIJ 4(4):118–123MathSciNetGoogle Scholar
  16. Montaner M, Ramirez-Serrano A (1998) Fuzzy knowledge-based controller design for autonomous robot navigation. Expert Syst Appl 14(1):179–186CrossRefGoogle Scholar
  17. Montiel O, Orozco-Rosas U, Path RS (2015) Planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 42(12):5177–5191CrossRefGoogle Scholar
  18. Songqiao T, Juan T (2018) Path planning with obstacle avoidance based on normalized R-function. J Robot 2018:1–10Google Scholar
  19. Wang X, Yang S (2003) Neuro-fuzzy approach to obstacle avoidance of a nonholonomic mobile robot. In: IEEE/ASME international conference on advanced intelligent mechatronic, Japan, pp 29–34Google Scholar
  20. Zhou F, Song B, Tian G (2011) Bezier curve based smooth path planning for mobile robot. J Inf Comput Sci 8(12):2441–2450Google Scholar
  21. Zhou Z, Wanga J, Zhub Z, Yang D, Wua J (2018) Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik 158:639–651CrossRefGoogle Scholar
  22. Zhu A, Yang S (2007) Neurofuzzy-based approach to mobile robot navigation in unknown environments. IEEE Trans Syst 37(4):610–621Google Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentUniversity of BasrahBasrahIraq

Personalised recommendations