Path planning is a major challenge surrounding the development of autonomous drones. For a practical solution, a computationally inexpensive and efficient path planning algorithm needs to be utilized to ensure the smooth operation of drones during long distance missions. Randomly Exploring Random Trees (RRT) and RRT* are sampling based path planning algorithms that have been widely used to solve high dimensional complex problems. RRT* ensures asymptotic optimality; however, it requires a long time to converge to a near optimum solution. RRT* variants have been proposed to improve the rate of convergence. Although many RRT* variants have been proposed, to the best of our knowledge, there has not been a comprehensive analysis comparing the performance of these algorithm. In this study, we perform a detailed comparison of a select group of RRT* variants with RRT and RRT* to determine its potential to be used as a path planner for autonomous drones. We review each algorithm and evaluate its performance by investigating the path cost, execution time and the number of nodes required to generate a path. Experimental results suggest that the performance of the RRT* variants is generally dependent on the type of the environment.