Deep Reinforcement Learning-Based Navigation with RGB-D Input for Autonomous Systems

This paper presents a novel navigation framework based on deep reinforcement learning (DRL) and dual-source network architecture, it utilizes RGB-D input for autonomous mobile robots in complex environments. The integration of RGB-D data into the dual-source Actor-Critic architecture allows enhanced...

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Bibliographic Details
Published inData Driven Control and Learning Systems Conference (Online) pp. 584 - 590
Main Authors Zhou, Xianhan, Xu, Degang
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.05.2025
Subjects
Accuracy
Autonomous robots
Collision avoidance
Deep reinforcement learning
dual-source network architecture
Navigation
Network architecture
obstacle avoidance
Path planning
Robot sensing systems
Robustness
Safety
Online AccessGet full text
ISSN2767-9861
DOI10.1109/DDCLS66240.2025.11065124

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Summary:This paper presents a novel navigation framework based on deep reinforcement learning (DRL) and dual-source network architecture, it utilizes RGB-D input for autonomous mobile robots in complex environments. The integration of RGB-D data into the dual-source Actor-Critic architecture allows enhanced spatial awareness, obstacle avoidance, and path planning, addressing limitations in RGB-only and monocular vision systems. Experimental results demonstrate significant improvements in navigation accuracy, robustness, and safety in both simulated and real-world scenarios. Our approach advances state-of-the-art in DRL-based autonomous navigation by fully leveraging RGB-D data for complex navigation tasks.
ISSN:2767-9861
DOI:10.1109/DDCLS66240.2025.11065124