Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity
Classical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajector...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-03-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098615001548 |
| _version_ | 1818900766290608128 |
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| author | P.K. Das H.S. Behera B.K. Panigrahi |
| author_facet | P.K. Das H.S. Behera B.K. Panigrahi |
| author_sort | P.K. Das |
| collection | DOAJ |
| description | Classical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajectory of the path for multi-robots in clutter environment using hybridization of improving classical Q-learning based on four fundamental principles with improved particle swarm optimization (IPSO) by modifying parameters and differentially perturbed velocity (DV) algorithm for improving the convergence. The algorithms are used to minimize path length and arrival time of all the robots to their respective destination in the environment and reducing the turning angle of each robot to reduce the energy consumption of each robot. In this proposed scheme, the improve classical Q-learning stores the Q-value of the best action of the state and thus save the storage space, which is used to decide the Pbest and gbest of the improved PSO in each iteration, and the velocity of the IPSO is adjusted by the vector differential operator inherited from differential evolution (DE). The validation of the algorithm is studied in simulated and Khepera-II robot. |
| first_indexed | 2024-12-19T20:09:04Z |
| format | Article |
| id | doaj.art-438bea4747ff44588fbfba70007d2936 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-12-19T20:09:04Z |
| publishDate | 2016-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-438bea4747ff44588fbfba70007d29362022-12-21T20:07:22ZengElsevierEngineering Science and Technology, an International Journal2215-09862016-03-0119165166910.1016/j.jestch.2015.09.009Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocityP.K. Das0H.S. Behera1B.K. Panigrahi2Department of Computer Science & Engineering and Information Technology, VSSUT, Burla, Odisha, IndiaDepartment of Computer Science & Engineering and Information Technology, VSSUT, Burla, Odisha, IndiaDepartment of Electrical Engineering, IIT, Delhi, IndiaClassical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajectory of the path for multi-robots in clutter environment using hybridization of improving classical Q-learning based on four fundamental principles with improved particle swarm optimization (IPSO) by modifying parameters and differentially perturbed velocity (DV) algorithm for improving the convergence. The algorithms are used to minimize path length and arrival time of all the robots to their respective destination in the environment and reducing the turning angle of each robot to reduce the energy consumption of each robot. In this proposed scheme, the improve classical Q-learning stores the Q-value of the best action of the state and thus save the storage space, which is used to decide the Pbest and gbest of the improved PSO in each iteration, and the velocity of the IPSO is adjusted by the vector differential operator inherited from differential evolution (DE). The validation of the algorithm is studied in simulated and Khepera-II robot.http://www.sciencedirect.com/science/article/pii/S2215098615001548Q-learningPath planningMobile robotsEnergyIPSO-DVKhepera II |
| spellingShingle | P.K. Das H.S. Behera B.K. Panigrahi Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity Engineering Science and Technology, an International Journal Q-learning Path planning Mobile robots Energy IPSO-DV Khepera II |
| title | Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity |
| title_full | Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity |
| title_fullStr | Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity |
| title_full_unstemmed | Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity |
| title_short | Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity |
| title_sort | intelligent based multi robot path planning inspired by improved classical q learning and improved particle swarm optimization with perturbed velocity |
| topic | Q-learning Path planning Mobile robots Energy IPSO-DV Khepera II |
| url | http://www.sciencedirect.com/science/article/pii/S2215098615001548 |
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