The field of motion planning is witnessing significant advancements with the development of innovative optimization techniques. Researchers are focusing on improving the efficiency and effectiveness of sampling-based planners, which are crucial in robotics and other areas. The introduction of new heuristics, such as direction-informed and force-directed approaches, is enhancing the convergence rate and solution quality of motion planning algorithms. Furthermore, the integration of machine learning and fuzzy logic is enabling adaptive and environment-aware planning. Noteworthy papers include: Direction Informed Trees (DIT*) which proposes a novel sampling-based planner that focuses on optimizing the search direction for each edge. APT* which introduces adaptively prolated elliptical r-nearest neighbors to dynamically modulate the path searching process based on environmental feedback. Genetic Informed Trees (GIT*) which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data to refine heuristic functions for better guidance. Deep Fuzzy Optimization for Batch-Size and Nearest Neighbors which introduces Learning-based Informed Trees (LIT*), a sampling-based deep fuzzy learning-based planner that dynamically adjusts batch size and nearest neighbor parameters to obstacle distributions in the configuration spaces.