Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

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 RL this growing technological era, few studies have systematically reviewed research concept. Therefore, study provides a comprehensive review literature on learning-based avoidance. Furthermore, reviews publications from last 5 years (2018–2022) to include 34 evaluate latest trends autonomous mobile robot development with RL. In end, shed light learning. Likewise, propagation model performance evaluation metrics approaches that been employed previous were synthesized by study. Ultimately, article’s major objective is aid scholars their understanding present future applications deep