Although the Basic RRT algorithm is considered a traditional search method, it has been widely used in the field of robot path planning (manipulator and mobile robot), especially in the past decade. This algorithm has many features that give it superiority over other methods. On the other hand, the Basic RRT suffers from a bad convergence rate (it takes a long time until finding the goal point), especially in environments with cluttered obstacles, or whose targets are located in narrow passages. Many studies have discussed this problem in recent years. This paper introduces an improved method called (Hybrid RRT-A*) to overcome the shortcomings of the original RRT, specifically slow convergence and cost rate. The heuristic function of A-star algorithm is combined with RRT to decrease tree expansion and guide it towards the goal with less nodes and time. Various experiments have been conducted with different environment scenarios to compare the proposed method with the Basic RRT and A-star under the same conditions, which have shown remarkable performance. The time consumed to find the path of the worst one of these scenarios is about 4.9 seconds, whereas it is 18.3 and 34 for A-star and RRT, respectively.