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Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review.

Yingjie Zhu¹, Wan Zuha Wan Hasan¹, Hafiz Rashidi Harun Ramli¹

¹Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

Deep reinforcement learning (DRL) shows promise for mobile robot navigation in dynamic settings. This study analyzes DRL algorithms, identifies trends, and proposes future directions for improved real-world adaptability and performance.

Keywords:

deep reinforcement learning dynamic environments mobile robot navigation

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Area of Science:

Artificial Intelligence
Robotics
Machine Learning

Background:

Deep reinforcement learning (DRL) is crucial for mobile robot navigation in dynamic environments.
Current DRL models struggle with generalization and adaptability in real-world scenarios due to simplified training.
Challenges include complex tasks, dynamic obstacles, and multimodal data fusion.

Purpose of the Study:

To comparatively analyze classical DRL algorithms for mobile robot navigation in dynamic environments.
To identify key trends and challenges in DRL-based navigation from recent literature (2021-2024).
To outline future research directions for enhancing DRL in dynamic environments.

Main Methods:

Comparative analysis of value-based, policy-based, and hybrid DRL algorithms.
Systematic review of recent studies (2021-2024) on DRL for robot navigation.
Identification of trends, limitations, and future research opportunities.

Main Results:

Classical DRL algorithms have distinct advantages and limitations for real-time navigation.
Recent research predominantly focuses on indoor DRL navigation, with outdoor and multi-robot systems underexplored.
Key challenges include sim-to-real transfer and effective sensor fusion.

Conclusions:

DRL algorithms require further development for robust real-world mobile robot navigation.
Future research should prioritize enhancing real-time adaptability, multimodal perception, and collaborative learning.
Addressing sim-to-real transfer and sensor fusion is critical for practical deployment.