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...

Whakaahuatanga katoa

Ngā taipitopito rārangi puna kōrero

Ngā kaituhi matua:	Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Elsevier 2025-04-01
Rangatū:	Engineering Science and Technology, an International Journal
Ngā marau:	Autonomous robot navigation Reinforcement learning Path constraint Motion and path planning
Urunga tuihono:	http://www.sciencedirect.com/science/article/pii/S2215098625000485
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!