Deep Reinforcement Learning for Dynamic Obstacle Avoidance of Mobile Robots in Indoor Environments: A Review

The ability of mobile robots to avoid obstacles dynamically in indoor environments is a necessary condition for achieving autonomous planning and navigation. When dealing with unstructured and randomly dynamic indoor scenes, traditional obstacle avoidance algorithms have poor adaptability and low flexibility, making it difficult to handle environmental uncertainties. Deep Reinforcement Learning (DRL), with its efficient end-to-end decision-making, autonomous interactive learning capabilities, and proficiency in modeling complex dynamic systems, has emerged as a focal point of research in dynamic obstacle avoidance. This paper first presents the theoretical foundation of DRL, then categorizes the fundamental DRL algorithms for indoor dynamic obstacle avoidance into three main types: Value function-based, Policy-based, and Actor-Critic-based algorithms, while also introducing relevant algorithms. Furthermore, it addresses the core challenges encountered in indoor dynamic obstacle avoidance and summarizes various improvement strategies for the different basic algorithms, detailing their starting points and performance impacts. Finally, the paper outlines the development trends and future research directions in this domain. This review aims to serve as a systematic reference for the design and engineering application of DRL algorithms in dynamic obstacle avoidance for indoor mobile robots.