Document Type : Research Paper
Authors
1 Control and Systems Department, University of Technology-Iraq, Baghdad, Iraq.
2 Control and Systems Department, University of Technology-Iraq, Baghdad, Iraq
Abstract
A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.
Keywords
[2] Nizar Hadi Abbas and Farah Mahdi Ali, “Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm”, International Journal of Computer Applications (0975 – 8887) Vol. 96, No. 11, June 2014 [3] K. Heero, “Path Planning and Learning Strategies for Mobile Robots in Dynamic Partially Unknown Environments,” Ph. D. Thesis, University of Tartu, Estonia, ., 2006. [4] H. Chen, Y. Zhu, and K. Hu, “Adaptive Bacterial Foraging Optimization,” Abstract and Applied Analysis., Vol. 2011, pp. 1–27, 2011. [5] D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Comput.,V. 22, no. 2, pp. 387–408, 2018. [6] A. M. Husain, S. M. Sohail, and V. S. Narwane, “Path planning of material handling robot using Ant Colony Optimization ( ACO ) technique,” International Journal of Engineering Research and Applications (IJERA) ,Vol. 2, no. 5, pp. 1698–1701, 2012. [7] X. Yang, “Optimization techniques and applications with examples”, JohnWiley & Sons, Inc, USA, pp 1-350, 2018. [8] V. Maheshwari and U. Datta, “Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation,” Int. J. Comput. Appl., Vol. 91, no. 13, pp. 37–40, 2014. [9] M. J. Mohamed, “Enhanced Genetic Algorithm Based on Node Codes for Mobile Robot Path Planning” IJCCCE, Vol. 12, no. 2, pp. 69–80, 2012. [10] I. K. Ibraheem and F. H. Ajeil “Multi-Objective Path Planning of an Autonomous Mobile Robot in Static and Dynamic Environments using a Hybrid PSO-MFB Optimization Algorithm,”Electrical Engineering Department, College of Engineering, University of Baghdad,arXiv:1805.00224v2, no. May, 2018.