The field of robotics is witnessing significant advancements in path planning, with a focus on developing more efficient and versatile algorithms. Researchers are exploring new paradigms, such as passage-traversing optimal path planning, which optimizes paths based on accessible free space. Additionally, there is a growing interest in using Riemannian metric models to transform complex path planning problems into geometric problems on lower-dimensional planes. These innovative approaches are addressing prior limitations and incapabilities in traditional path planning methods, enabling robots to effectively navigate complex scenarios. Notably, some papers have proposed novel distance metrics and collision detection algorithms that enhance computational efficiency and accuracy. Overall, the field is moving towards more sophisticated and adaptive path planning techniques. Noteworthy papers include:

• RM-Dijkstra, which introduces a surface optimal path planning algorithm based on Riemannian metric, demonstrating improved path accuracy and smoothness.
• Passage-traversing optimal path planning, which provides a versatile solution to accessible free space optimization, outperforming conventional approaches.
• An RRT* algorithm based on Riemannian metric model, which shows better smoothness and optimization properties compared to traditional RRT* algorithms.