The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.

Recent interest in unmanned aerial vehicles (UAVs) has grown due to the wide range of possible civilian uses for these aircraft. However, present robot navigation technologies still need to be improved in various situations. Researchers are particularly interested in the 'Sense and Avoid' capacity as a critical issue. UAVs operating in civilian areas must have this functionality to do so safely. Numerous path planning and navigation algorithms have been developed for autonomous decision-making and control of UAVs. These path-planning algorithms are divided into either heuristic and non-heuristic or accurate methods. Both existing UAV route planning algorithms for the first and second techniques will be thoroughly compared in this work. Each algorithm is put through its paces in three diverse obstacle scenarios. Each method has been evaluated under various global and local obstacle information availability conditions while comparing the computational time and solution optimality.