Expert system for Robotic Path Planning
Baidaa M. Madlol1, Ahmad T. Abdulsadda2, Ali A. Al Bakry3
1Baidaa M. Madlol*, Communication Technical Engineering Department ,Al Najaf Technical Engineering College, Al Furat Al Awast Technical University (ATU), Al Najaf, Iraq.
2Ahmad T. Abdulsadda, Communication Technical Engineering Department , Al Najaf Technical Engineering College, Al Furat Al Awast Technical University (ATU), Al Najaf, Iraq.
3Ali A. Abdullah Albakry, Communication Technical Engineering Department ,Al Najaf Technical Engineering College, Al Furat Al Awast Technical University (ATU), Al Najaf, Iraq.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 592-596 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5150029320/2020©BEIESP | DOI: 10.35940/ijeat.C5150.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Robotic planning to find the target our goal point/s is most important subject with the minimum distance and the fastest speed with obstacle avoidance expert system has been proposed. In this paper we try to compare and consider different scenario by taking two or more moving robot figure out the short path from the initial and the final point automatically through the map of many regular and irregular obstacles. Firstly, the adaptive fuzzy expert system is present where the fuzzy rule has been adaptive recursively through the robot moving, and then the potential field algorithm has been compared with the adaptive fuzzy system, the results demonstrated that the adaptive fuzzy is faster than the potential field but the accuracy moving of the potential field robotic path planning is much better. All the algorithms were failed when two robots moving from two different initial points to one final target point the why we have proposed particle swarm optimization (PSO) algorithm to solve such problem.
Keywords: Robot, path planning, optimization, fuzzy, PSO.