Iterative path optimization for practical robot planning

We present a hybrid path planner that combines two common methods for robotic planning: a Dijkstra graph search for the minimum distance path through the configuration space and an optimization scheme to iteratively improve grid-based paths. Our formulation is novel because we first commit to the minimum distance path, then explicitly relax the path to maximize the clearance up to a user-specified bound. Notably, this formulation yields more predictable paths than potential field methods which try to trade increases in path length for greater clearance around obstacles. These potential field costs infer a trade off that can yield poor paths when the obstacle map is partially observable and has a finite history. Some approximations are used to ensure efficient planning, but only a small set of additional behaviors were required to ensure safe operation. Our method has been field tested extensively, as it is the main on-robot path planner for our large team of 14 medium-scale autonomous ground robots and entry to the 2010 Multi Autonomous Ground-robotic International Challenge, MAGIC 2010.