Ipif-Rrt* : An Improved Potential-Informed Fast Path-Planning Method for Mobile Robots

Rapidly-exploring random tree (RRT), a sampling-based path-planning method, has become a hot research topic in mobile robot path planning due to its ability to find feasible solutions quickly in a given environment. RRT* is a variant of RRT that extended to the problem of finding optimal paths; but its convergence is slow. To improve the quality of the initial solution and the convergence time to the optimal solution, an improved potential-informed fast RRT* path-planning method, (IPIF-RRT*), is presented in this study. In the sampling phase, the method samples and modifies random nodes on the map using an improved potential-function-based informed sampling method. During tree expansion, it attempts to create one or more new nodes in the direction near the obstacle, to further shorten the path. Based on simulations and comparisons with the RRT*, Informed-RRT* and F-RRT* algorithms, as well as separate experiments on some important methods, the algorithm presented in this study shows advantages in terms of quality of initial solutions and convergence time to optimal solutions