Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning

This paper introduces a neural network model designed for autonomous navigation in complex environments. It combines DRL methodologies to capture critical environmental features in the neural network. These features encompass data about the robot, humans, static obstacles, and path constraints. The...

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Váldodahkkit:	Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	Elsevier 2025-04-01
Ráidu:	Engineering Science and Technology, an International Journal
Fáttát:	Autonomous robot navigation Reinforcement learning Path constraint Motion and path planning
Liŋkkat:	http://www.sciencedirect.com/science/article/pii/S2215098625000485
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