A Comprehensive Review of Deep Learning Techniques in Mobile Robot Path Planning: Categorization and Analysis

摘要

Deep Reinforcement Learning (DRL) has emerged as a transformative approach in mobile robot path planning, addressing challenges associated with dynamic and uncertain environments. This comprehensive review categorizes and analyzes DRL methodologies, highlighting their effectiveness in navigating high-dimensional state–action spaces and adapting to complex real-world scenarios. The paper explores value-based methods like Deep Q-Networks (DQNs) and policy-based strategies such as Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), emphasizing their contributions to efficient and robust navigation. Hybrid approaches combining these methodologies are also discussed for their adaptability and enhanced performance. Additionally, the review identifies critical gaps in current research, including limitations in scalability, safety, and generalization, proposing future directions to advance the field. This work underscores the transformative potential of DRL in revolutionizing mobile robot navigation across diverse applications, from search-and-rescue missions to autonomous urban delivery systems.