Aiming at the problems of the Informed-RRT（rapidly-exploring random tree）* algorithm， such as slow convergence speed， low optimization efficiency and inability to execute the generated path， path planning research based on MI-RRT* （modified informed-RRT*） algorithm is carried out. The method of greedy sampling and adaptive step size is introduced to improve the convergence rate of the algorithm， reduce the path generation time and the memory usage. Minimum Snap curve is used to make the path smooth and the power change smoothly， so as to achieve the effect of saving energy and the path to generate the executable. Several sets of different experimental environments are run to show that the MI-RRT* algorithm is more advantageous than the original algorithm， the resulting planned path is smooth executable and it can reduce the number of iterations by 20% and the search time by 25%. Compared with the open and dense environment， the MI-RRT* algorithm has obvious advantages over the Informed-RRT* ，RRT* algorithms.