Abstract
Navigation for a humanoid robot in inclined terrain is a challenging activity in robotics. The goal of current research is to explore possible paths and optimize the footstep and identify routes that are optimum in reference to path length covered by the robot. A hybrid approach of Probabilistic Roadmap (PRM) and Grey wolf optimization (GWO) is proposed for humanoid NAO in terrain with an inclined plane and static obstacles. The sensory data such as obstacle distance in the right direction (RD), left direction (LD), and front direction (FD) are fed to the PRM approach, which provides stable walking for a humanoid robot with an interim driving angle (IDA). For optimum navigation and footstep adjustment for the inclined plane, the GWO approach is utilized. The proposed hybrid approach offers optimal driving angles (ODA) to navigate an inclined plane and guarantees the shortest distance. Simulation in flat terrain using the proposed approach and standalone approaches has been performed in a 3D simulator. The obtained convergence curve, travel distance, and time spent show that the NAO meets the objective in all situations, but that GWO tuned PRM approach is preferable to this objective. Further, the proposed approach has been analyzed in inclined terrain. Based on these results, the designed approach guarantees robustness and effectiveness.
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Kashyap, A.K., Parhi, D.R.K., Kumar, S., Pandey, A. (2022). A GWO Tuned Probabilistic Roadmap Approach for Coarse Mapping of Humanoid Robot in Inclined Terrain. In: Deepak, B.B.V.L., Parhi, D., Biswal, B., Jena, P.C. (eds) Applications of Computational Methods in Manufacturing and Product Design. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-0296-3_11
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DOI: https://doi.org/10.1007/978-981-19-0296-3_11
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