Academic Journal
Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy
| العنوان: | Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy |
|---|---|
| المؤلفون: | Vikas, Parhi, Dayal R |
| المصدر: | Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science ; volume 238, issue 5, page 1542-1560 ; ISSN 0954-4062 2041-2983 |
| بيانات النشر: | SAGE Publications |
| سنة النشر: | 2023 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1177/09544062231185511 |
| الاتاحة: | https://doi.org/10.1177/09544062231185511 https://journals.sagepub.com/doi/pdf/10.1177/09544062231185511 https://journals.sagepub.com/doi/full-xml/10.1177/09544062231185511 |
| Rights: | https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| رقم الانضمام: | edsbas.8F0BF64 |
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