State Lattice with Controllers: Augmenting Lattice-Based Path Planning with Controller-Based Motion Primitives

Abstract

State lattice-based planning has been used in navigation for ground, water, aerial and space robots. State lattices are typically constructed of simple motion primitives connecting one state to another. There are situations where these metric motions may not be available, such as in GPS-denied areas. In many of these cases, however, the robot may have some additional sensing capability that is not being fully utilized by the planner. For example, if the robot has a camera it may be able to use simple visual servoing techniques to navigate through a GPS-denied region. Likewise, a LIDAR may allow the robot to skirt along an environmental feature even if there is not enough information to generate an accurate pose estimate. In this paper we present an expansion of the state lattice framework that allows us to incorporate controller-based motion primitives and external perceptual triggers directly into the planning process. We provide a formal description of our method of constructing the search graph in these cases as well as presenting real-world and simulated testing data showing the practical application of this approach.