Generalized k-Cell Decomposition for Visibility Planning in Polygons

TL;DR

This paper presents a new $k$-cell decomposition method for pursuit-evasion in polygons, enabling robust visibility-based path planning with $k$-modems that see through up to $k$ walls, ensuring stable shadow regions.

Contribution

It introduces a generalized $k$-cell decomposition method that extends existing visibility models, ensuring stable shadow regions and enabling reliable pursuit strategies in polygonal environments.

Findings

Decomposition ensures unchanged shadow regions during movement.

Method extends $0$- and $2$-visibility to all even $m \, (0 \, ext{to} \, k)$.

Validated through proofs and simulated pursuit scenarios.

Abstract

This paper introduces a novel $k$-cell decomposition method for pursuit-evasion problems in polygonal environments, where a searcher is equipped with a $k$-modem: a device capable of seeing through up to $k$ walls. The proposed decomposition ensures that as the searcher moves within a cell, the structure of unseen regions (shadows) remains unchanged, thereby preventing any geometric events between or on invisible regions, that is, preventing the appearance, disappearance, merge, or split of shadow regions. The method extends existing work on $0$- and $2$-visibility by incorporating m-visibility polygons for all even $0 \le m \le k$, constructing partition lines that enable robust environment division. The correctness of the decomposition is proved via three theorems. The decomposition enables reliable path planning for intruder detection in simulated environments and opens new avenues for visibility-based robotic surveillance. The difficulty in constructing the cells of the decomposition consists in computing the $k$-visibility polygon from each vertex and finding the intersection points of the partition lines to create the cells.