FE-RRT*: Forward Expansion RRT* Enhanced Space Exploitation and Informed Subset Convergence

Path planning is an important part of decision making, and high-quality planning results will dramatically improve work efficiency. This study delves into the realm of motion planning for intelligent mobile robots. The research addresses the challenges of reverse growth branches and redundant nodes in sampling algorithms, proposing the Forward Expansion RRT* (FE-RRT*) algorithm as a solution. By integrating strategies to enhance space exploitation efficiency and optimize heuristic, the FE-RRT* algorithm outperforms RRT*-Connect and Informed RRT*-Connect. Experimental results show the algorithms efficiency through metrics such as final path length reduction and decreased iteration time. This manuscript contributes novel strategies and evaluation metrics for motion planning, offering valuable insights for enhancing decision-making processes in intelligent mobile robotics.