Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories

Abstract

Sampling-based methods such as Rapidly-exploring Random Tree (RRT) have been successfully used in solving motion planning problems in high-dimensional and complex environments. The RRT algorithm is the most popular and has the ability to find a feasible solution faster than other algorithms. The drawback of using RRT is that, as the number of samples increases, the probability that the algorithm converges to a sub-optimal solution increases. Furthermore, the path generated by this algorithm is not smooth (tree form). The RRT-based methods will be discussed and simulations are given to evaluate the performance of the methods.