ABSTRACT Mobile robots are increasingly deployed across industrial and service sectors, where autonomous navigation is required in both time‐invariant, static environments and time‐variant, dynamic environments. During navigation, robots must handle diverse obstacles, including fixed and moving objects, while minimizing travel distance, execution time, and collision risk. Although various machine‐learning‐based path planning approaches have been proposed to address these challenges, many depend on pre‐collected data sets, and obtaining such data in real‐time, unpredictable environments is difficult and often impractical. This review focuses on reinforcement‐learning‐based path planning, wherein mobile robots learn obstacle characteristics, path structure, and optimal policies directly from the environment through trial‐and‐error interaction, largely without relying on external training data. The study examines key challenges associated with autonomous navigation and analyzes reinforcement learning techniques in terms of their advantages, limitations, applications, performance metrics, obstacle categories, and obstacle avoidance mechanisms. A quantitative assessment of 58 selected papers reveals that 51 percent of the studies concentrate on local path planning, 32 percent on global planning, and 17 percent on hybrid approaches that integrate both planning strategies. These findings highlight a growing research shift towards data‐efficient reinforcement learning approaches for dynamic and uncertain environments, while global planners remain prevalent in static settings. The insights provided in this review support researchers and practitioners in selecting suitable reinforcement‐learning‐based path planning algorithms aligned with specific environmental conditions and navigation requirements.
Ramya et al. (Tue,) studied this question.