Generalizable Context-Aware Deep Assignment Network for the Perimeter Defense Problem

This paper presents a generalizable decentralized Context-aware Deep Assignment Network (CDAN) tailored for addressing Perimeter Defense Problems (PDP). In PDP scenarios, a group of defenders operates along a segmented convex closed perimeter, aiming to intercept intruders attempting to breach it. The PDP is framed as an assignment learning problem for the defenders to sequentially capture intruders. A context map is generated to depict the intruders’ trajectories, serving as input for the CDAN.