Optimal obstacle placement with disambiguations

TL;DR

This paper studies how to optimally place true obstacles among false ones to maximize a navigating agent's traversal length, using game theory and statistical analysis, with applications to maritime minefields.

Contribution

It introduces the optimal obstacle placement with disambiguations problem and analyzes it using variance analysis for different clutter and obstacle configurations.

Findings

More regular clutter leads to longer traversal lengths.

Traversal length varies with obstacle number, showing a concave-down trend.

Results are supported by a real-world maritime minefield case study.

Abstract

We introduce the optimal obstacle placement with disambiguations problem wherein the goal is to place true obstacles in an environment cluttered with false obstacles so as to maximize the total traversal length of a navigating agent (NAVA). Prior to the traversal, the NAVA is given location information and probabilistic estimates of each disk-shaped hindrance (hereinafter referred to as disk) being a true obstacle. The NAVA can disambiguate a disk's status only when situated on its boundary. There exists an obstacle placing agent (OPA) that locates obstacles prior to the NAVA's traversal. The goal of the OPA is to place true obstacles in between the clutter in such a way that the NAVA's traversal length is maximized in a game-theoretic sense. We assume the OPA knows the clutter spatial distribution type, but not the exact locations of clutter disks. We analyze the traversal length using repeated measures analysis of variance for various obstacle number, obstacle placing scheme and clutter spatial distribution type combinations in order to identify the optimal combination. Our results indicate that as the clutter becomes more regular (clustered), the NAVA's traversal length gets longer (shorter). On the other hand, the traversal length tends to follow a concave-down trend as the number of obstacles increases. We also provide a case study on a real-world maritime minefield data set.