PSO Based Path Planning Strategy in Manufacturing Plants with Unknown Environmental Criteria
Keywords:
Mobile robot, motion planning, unknown environment, PSO, Artificial IntelligenceAbstract
In the present era, mobile robots have been widely used in industrial sectors for transferring goods/tools from one place to another with in the manufacturing plant. It is a challenging task to develop an efficient motion planner of a mobile robot, if the robot is moving in unknown environments. Hence, the current research work aims at developing an intelligent motion planning strategy using particle swarm optimization in order to transport the goods within the manufacturing plant. Finally, simulation results have been presented to validate the efficiency of the proposed algorithm.
Downloads
References
. Gong, H., Wang, P., Ni, C., & Cheng, N. (2022). Efficient path planning for mobile robot based on deep deterministic policy gradient. Sensors, 22(9), 3579.
. Hichri, B., Gallala, A., Giovannini, F., &Kedziora, S. (2022). Mobile robots path planning and mobile multirobots control: A review. Robotica, 1-14.
. Deepak, B. B. V. L., Parhi, D. R., &Kundu, S. (2012). Innate immune based path planner of an autonomous mobile robot. Procedia Engineering, 38, 2663-2671.
. Jogeshwar, B. K., &Lochan, K. (2022). Algorithms for Path Planning on Mobile Robots. IFAC-PapersOnLine, 55(1), 94-100.
. Shi, P., & Cui, Y. (2010, May). Dynamic path planning for mobile robot based on genetic algorithm in unknown environment. In 2010 Chinese control and decision conference (pp. 4325-4329). IEEE.
. Hao, K., Zhao, J., Yu, K., Li, C., & Wang, C. (2020). Path planning of mobile robots based on a multi-population migration genetic algorithm. Sensors, 20(20), 5873.
. Luh, G. C., & Liu, W. W. (2008). An immunological approach to mobile robot reactive navigation. Applied Soft Computing, 8(1), 30-45.
. Deepak, B. B. V. L., &Parhi, D. (2013). Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intelligent Service Robotics, 6, 155-162.
. Chia, S. H., Su, K. L., Guo, J. H., & Chung, C. Y. (2010, December). Ant colony system based mobile robot path planning. In 2010 fourth international conference on genetic and evolutionary computing (pp. 210-213). IEEE.
. Lu, L., & Gong, D. (2008, October). Robot path planning in unknown environments using particle swarm optimization. In 2008 Fourth International Conference on Natural Computation (Vol. 4, pp. 422-426). IEEE.
. Deepak, B. B. V. L., Parhi, D. R., &Raju, B. M. V. A. (2014). Advance particle swarm optimization-based navigational controller for mobile robot. Arabian Journal for Science and Engineering, 39, 6477-6487.

Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.