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

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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Bibliographic Details
Main Authors: Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon
Format: Article
Language:English
Published: Elsevier 2025-04-01
Series:Engineering Science and Technology, an International Journal
Subjects:
Autonomous robot navigation
Reinforcement learning
Path constraint
Motion and path planning
Online Access:http://www.sciencedirect.com/science/article/pii/S2215098625000485
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