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• W4384200876 abstract "The increasing demand for automation and material transportation has shown an incline toward optimal path navigation. The present work implements an intelligent Memory-based gravitational search algorithm (MGSA) with an evolutionary learning strategy to achieve a globally optimal collision-free path. The Evolutionary learning strategy helps improve the diversity among the Gravitational masses/agents, hence improving the overall exploration capability of the model. While the other approaches focus more on an evolutionary strategy based on mutation and cross-overs, the present technique implements the evolutionary strategy based on the position of the fit agents to improve the position of the unfit agents in the population. It ensures a fast-converging path planning result with an improved trajectory. Further, adding a memory-based approach helps the model remember the location of the best agent within the population. The controller is tested with multiple Humanoids on even and uneven terrains and showed a minimal improvement of more than 4% in path length with a minimum 5% deviation in the simulation and experimental results. The proposed approach showed a further improvement of more than 6% compared to the different intelligent path-planning approaches in a similar environment." @default.
• W4384200876 created "2023-07-14" @default.
• W4384200876 creator A5068789867 @default.
• W4384200876 creator A5069237189 @default.
• W4384200876 date "2023-07-13" @default.
• W4384200876 modified "2023-09-25" @default.
• W4384200876 title "Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy" @default.
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• W4384200876 doi "https://doi.org/10.1177/09544062231185511" @default.
• W4384200876 hasPublicationYear "2023" @default.
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