Probabilistic roadmaps for efficient path planning

The problem of finding a collision-free path between points in space has applications across many different fields. In robotics, path planning prevents articulated arms from colliding with other machinery and allows a car to drive autonomously to a destination. In computer animation and gaming, characters can be given destinations to reach without colliding with anything in their environment. For lower dimensions, such as planar robots which may only translate through space, theoretical methods have been developed which are guaranteed to find a path if it exists. However, as the dimension of the object increases, the work required to find a solution has been shown to increase exponentially. Because of this, newer developments in path planning have revolved around randomized algorithms. In this paper, I discuss a class of methods called probabilistic roadmaps, which have been shown to have a high success rate and a fast running time. However, the randomness of PRMs introduces its own set of problems, most often resulting in failures to find legal pathways through space. I will give a brief overview of the theoretical underpinnings of path planning in section 2, followed by a description of the basic PRM algorithm in section 3, then the shortcomings of the original PRM algorithm are introduced in 4, along with papers that address these shortcomings.