Abstract
A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.
Similar content being viewed by others
References
Agirrebeitia J, Avile R, de Bustos IF, Ajuria G (2005) A new APF strategy for path planning in environments with obstacles. Mech Mach Theory 40:645–658
Baran Hui N, Kumar Pratihar D (2009) A comparative study on some navigation schemes of a real robot tackling moving obstacles. Robotics Comput Integrated Manuf 25:810–828
Castillo O, Trujillo L, Melin P (2007) Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots. Soft Comput Fusion Found Methodol Appl 11:269–279
Cosio F, Castaneda M (2004) Autonomous robot navigation using adaptive potential fields. Math Comput Model J 40:1141–1156
Filliat D, Meyer J (2003) Map-based navigation in mobile robots: I. A review of localization strategies. Cogn Syst Res 4:243–282
Ge SS, Cui YJ (2000) New potential functions for mobile robot path planning. IEEE Trans Robotics Autom 16:615–620
Ge SE, Cui YJ (2002) Dynamic motion planning for mobile robots using potential field method. Autonomous Robots 13:207–222
Hart E, Timmis J (2008) Application areas of AIS: the past, the present and the future. Appl Soft Comput 8:191–201
Huang L (2009) Velocity planning for a mobile robot to track a moving target—a potential field approach. Robotics Auton Syst 57:55–63
Huanga WH, Fajenb BR, Finka JR, Warrenc WH (2006) Visual navigation and obstacle avoidance using a steering potential function. Robotics Auton Syst 54:288–299
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685
Jaradat MA, Al-Rousan M, Quadan L (2011) Reinforcement based mobile robot navigation in dynamic environment. Robotics Comput Integrated Manuf 27:135–149
Jianping T, Yang S (2003) Genetic algorithm based path planning for a mobile robot. In: Proceedings of IEEE international conference on robotics and automation, pp 1221–1226
Jing X (2005) Behavior dynamics based motion planning of mobile robots in uncertain dynamic environments. Robotics Auton Syst 53:99–123
Khated O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robotics Res 5:90–98
Koren Y, Borenstein J (1991) Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings of the IEEE conference on robotics and automation, pp 1398–1404
Li Z, Chen X, Xiao W (2004) A new motion planning approach based on artificial potential field in unknown environment. LNCS, pp 376–382
Luh G, Liu W (2008) An immunological approach to mobile robot reactive navigation. Appl Soft Comput J 8:30–45
Masoud SA, Masoud AA (2000) Constrained motion control using vector potential fields. IEEE Trans Syst Man Cybern Part A Syst Hum 30:251–272
McFetridge L, Ibrahim MY (2009) A new methodology of mobile robot navigation: the agoraphilic algorithm. Robotics Comput Integrated Manuf 25:545–551
Matlab Inc. (2008) Matlab fuzzy logic toolbox. http://www.Mathwork.com
Meyer J, Filliat D (2003) Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies. Cogn Syst Res 4:283–317
Tsourveloudis NC, Valavanis KP, Hebert T (2001) Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic. IEEE Trans Robotics Autom 17:490–497
Wang Y, Han L, Li M, Wang Q, Zhou J, Cartmel M (2004) A real time path planning approach without the computation of Cspace obstacles. Robetica 22:173–187
Zou A, Hou Z, Fu S, Tan M (2006) Neural networks for mobile robot navigation: a survey. Lecture Notes in Computer Science. Springer, Berlin, vol 3972, pp 1218–1226
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jaradat, M.A., Garibeh, M.H. & Feilat, E.A. Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Comput 16, 153–164 (2012). https://doi.org/10.1007/s00500-011-0742-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-011-0742-z