##### Autonomy Artificial Intelligence Robotics (AAIR)

##### 2022

# PATH-FREE ESTIMATION OF NAVIGATION DISTANCE USING OBSTACLE SHAPE STATISTICS AND DENSITY

### by Stephen J. Harnett; Sean Brennan; Karl Reichard; Jesse Pentzer; David Gorsich

#### Abstract

In the field of ground robotics, the problems of global path planning and
local obstacle avoidance are often treated separately but both are assessed in
terms of a cost related to navigating through a given environment. Traversal
cost is typically defined in terms of the required fuel [1], required travel time
[2], and imparted mechanical wear [3] to guide route selection. Prior work [4]
has shown that obstacle field complexity and navigation cost can be abstracted
into quantitative dimensionless parameters. But determining the cost parameters
and their relationship to field complexity requires running repeated path planning
simulations [4]. This work presents a method for estimating navigation cost
solely from geometric obstacle field complexity measures, namely the statistical
properties of an obstacleâ€™s shape and the density of obstacles within an environment,
eliminating the requirement to run a path planner in a simulation environment.