In recent years, there has been a notable increase in usage of autonomous mobile robots capable of independently executing a variety of tasks in real-world environments. Effective path planning is essential for these robots to successfully fulfill their objectives. However, a significant challenge in robotic path planning is avoiding the pitfalls of local minima. Over time, several optimization methods have been developed to tackle local minima traps. This paper presents a review of studies conducted over the past four years focused on resolving local minima challenges in robotics path planning. The review evaluates various optimization techniques, considering factors such as the type of robot, the year of publication, the nature of the environment (static or dynamic), and the evaluation method (whether through simulations or in real-world scenarios). Key challenges in these approaches, such as managing the complexity of hybrid algorithms and uncertainty in dynamic environments, are discussed in this review. In addition, a quantitative comparison between path planning methods is provided in terms of path length and quality.