Abstract
In this paper, a new hybrid intelligent motion planning approach to mobile robot navigation is presented. In this new hybrid methodology, the invasive weed optimization (IWO) algorithm is used for training the premise parameters, and the least square estimation (LSE) method is used for training the consequent part of the adaptive neuro-fuzzy inference system (ANFIS). In this proposed navigational model, different kinds of sensor-extracted information, such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD), heading angle (HA), left wheel velocity (LWV), and right wheel velocity (RWV), are given input to the hybrid controller, in order to calculate the suitable steering angle (SA) for the robot. Using the IWO algorithm, the obtained root mean of squared error (RMSE) for the training data set in the ANFIS is 0.0013. The simulation results are verified by the real-time experimental results, using the Khepera III mobile robot to show the versatility and effectiveness of the proposed hybrid navigational algorithm. The results obtained using the proposed hybrid algorithm are validated by comparison with the results from other intelligent algorithms. Finally, it is proved that the proposed hybrid navigational controller can be implemented in the robot for navigation in any complex environments.
Similar content being viewed by others
References
Latombe JC (1991) Robot motion planning. Kluwer, Boston, MA
Masehian E, Amin-Naseri MR (2004) A Voronoi diagram–visibility graph–potential field compound algorithm for robot path planning. J Robot Syst 21:275–300
Weigl M, Siemiaatkkowska B, Sikorski KA, Borkowski A (1993) Grid-based mapping for autonomous mobile robot. Robot Auton Syst 11(1):13–21
Lingelbach F, (2004) Path planning using probabilistic cell decomposition. In Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans, LA, USA, pp 467–472
Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12:566–580
Khatib O, (1985) Real time obstacle avoidance for manipulators and mobile robots. In Proceedings IEEE International Conference on Robotics and Automation, March 25-28, Missouri, pp 500-505
Saffiotti A (1997) Fuzzy logic in autonomous robotics: behavior coordination. In Proceedings of the 6th IEEE International Conference on Fuzzy Systems. pp. 573–578
Selekwa MF, Dunlap DD, Shi D, CollinsJr EG (2008) Robot navigation in very cluttered environment by preference based fuzzy behaviors. Robot Auton Syst 56(3):231–246
Abdessemed F, Benmahammed K, Monacelli E (2004) A fuzzy based reactive controller for a non-holonomic mobile robot. Robot Auton Syst 47(1):31–46
Wang M, Liu JNK (2008) Fuzzy logic-based real-time robot navigation in unknown environment with dead ends. Robot Auton Syst 56(7):625–643
Motlagh ORE, Hong TS, Ismail N (2009) Development of a new minimum avoidance system for a behavior-based mobile robot. Fuzzy Sets Syst 160(13):1929–1946
Zou AM, Hou ZG, Fu SY, Tan M (2006) Neural networks for mobile robot navigation: a survey. In: Advances in neural networks. Springer Berlin Heidelberg, pp. 1218-1226
Fujii T, Arai Y, Asama H, Endo I (1998) Multilayered reinforcement learning for complicated collision avoidance problems. Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3, pp 2186-2191
Castro V, Neira JP, Rueda CL, Villamizar JC, Angel L (2007) Autonomous navigation strategies for mobile robots using a probabilistic neural network (PNN). 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON), 5-8 November, Taipei, Taiwan, pp 2795-2800
Choi JM, Lee SJ, Won M (2011) Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms. J Mech Sci Technol 25(1):247–254
Yang SX, Meng M (2000) An efficient neural network method for real-time motion planning with safety consideration. Robot Auton Syst 32(2-3):115–128
Nefti S, Oussalah M, Djouani K, Pontnau J (2001) Intelligent adaptive mobile robot navigation. J Intell Robot Syst 30:311–329
Hui NB, Mahendar V, Pratihar DK (2006) Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches. Fuzzy Sets Syst 157(16):2171–2204
Godjevac J, Steele N (1999) Neuro-fuzzy control of a mobile robot. Neural Comput 28(1-3):127–143
Rusu P, Petriu EM, Whalen TE, Cornell A, Spoelder HJW (2003) Behavior-based neuro-fuzzy controller for mobile robot navigation. IEEE Trans Instrum Meas 52(4):1335–1340
Kim CN, Trivedi MM (1998) A neuro-fuzzy controller for mobile robot navigation and multi robot convoying. IEEE Trans Syst Man Cybern B Cybern 28(6):829–840
Jang JSR (1996) Input selection for ANFIS learning. In Proceedings of the fifth IEEE international conference on fuzzy systems, Vol. 2, pp 1493–1499
Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybern B 23(3):665–685
Mohanty PK, Parhi DR (2014) Navigation of autonomous mobile robot using adaptive network based fuzzy inference system. J Mech Sci Technol 28(7):2861–2868
Mohanty PK, Parhi DR (2012) Path generation and obstacle avoidance of an autonomous mobile robot using intelligent hybrid controller. 3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO) & Fuzzy and Neural Computing (FANCCO), 20-22 December, Bhubaneswar, pp 240–247
Mohanty PK, Parhi DR (2014) Path planning strategy for mobile robot navigation using MANFIS controller. International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), 14-16 November, Bhubaneswar, pp 353-361
Mohanty PK, Parhi DR (2013) A new intelligent approach for mobile robot navigation. 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI), 10-14 December, Kolkata, pp 243-249
Mehrabiana AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1:355–366
Jiang HM, Kwong CK, Ip WH, Wong TC (2012) Modeling customer satisfaction for new product development using a PSO-based ANFIS approach. Applied Soft Computing 12(2):726–734
Kundu D, Suresh K, Ghosh S, Das S, Pangrahi BK, Das S (2011) Multi-objective optimization with artificial weed colonies. Inf Sci 181:2441–2454
Basak A, Maity D, Das S (2013) A differential invasive weed optimization algorithm for improved global numerical optimization. Appl Math Comput 219:6645–6668
Nikoofard AH, Hajimirsadeghi H, Kian AR, Lucas C (2012) Multi-objective invasive weed optimization: application to analysis of Pareto improvement models in electricity markets. Appl Soft Comput 12:100–112
Ramezani Ghalenoei M, Hajimirsadeghi H, Lucas C (2009) Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R. China, December 16-18, 2009
Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269–1278
Rad HS, Lucas C (2007) A recommender system based on invasive weed optimization algorithm. IEEE Congress on Evolutionary Computation, 25-28 September, pp 4297-4304
Mo H, Tang Q, Meng L, (2013) Behavior-based fuzzy control for mobile robot navigation. Mathematical problems in engineering, vol. 2013. Article ID 561451, 10 pages
He Kunpeng, Sun Hua, Cheng Wanjuan (2008) Application of fuzzy neural network based on T-S model for mobile robot to avoid obstacles. Proceedings of the 7th World Congress on Intelligent Control and Automation. June 25–27, 2008, Chongqing, China
Cherroun L, Boumehraz M (2013) Fuzzy behavior based navigation approach for mobile robot in unknown environment. J Electr Eng 13:1–8
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Parhi, D.R., Mohanty, P.K. IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments. Int J Adv Manuf Technol 83, 1607–1625 (2016). https://doi.org/10.1007/s00170-015-7512-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-7512-5