FF-RRT*: a sampling-improved path planning algorithm for mobile robots against concave cavity obstacle

Abstract The slow convergence rate and large cost of the initial solution limit performance rapidly exploring random tree star (RRT*). To address this issue, paper proposes a modified RRT* algorithm (defined as FF-RRT*) that creates an optimal with fast rate. An improved hybrid sampling method is proposed to speed up by decreasing iterations overcoming application limitation original towards concave cavity obstacle. combines goal bias strategy strategy, which requires few searching time, resulting in faster than existing method. Then, parent node created for optimize path. Finally, FF-RRT* validated four simulation environments compared other algorithms. shortens 32% time complex maze environment 25% simple F-RRT*. And obstacle, average Fast-RRT* 134% more 12% F-RRT* 34% FF-RRT*. results show possesses superior algorithms, also fits much environment.