Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories
Belinda Matebese, MK Banda, S Utete · 2012
Sampling-based methods such as Rapidly-exploring Random Tree (RRT) have been successfully used in solving motion planning problems in highdimensional 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.