Random-walk planner

A surprisingly simple and efficient algorithm can be made entirely from random walks [179]. To avoid parameter tuning, the algorithm adjusts its distribution of directions and magnitude in each iteration, based on the success of the past $k$ iterations (perhaps $k$ is the only parameter). In each iteration, the VSM just selects the vertex that was most recently added to ${\cal G}$. The LPM generates a direction and magnitude by generating samples from a multivariate Gaussian distribution whose covariance parameters are adaptively tuned. The main drawback of the method is similar to that of the previous method. Both have difficulty traveling through long, winding corridors. It is possible to combine adaptive random walks with other search algorithms, such as the potential field planner [178].