Learning-Based Perimeter Invasion Design with Partial Information of Defense Model

Abstract

This paper focuses on the learning-based perimeter-defense problem. Specifically, we consider a scenario where an attacker invades an area protected by a defender with only partial information about the target area and defense strategy. The attack design is challenging since the flexible and unknown defense strategy results in the highly uncertain feasible invasion space. To address the problem, we propose a learning-based method by using patrol and defense trajectories. First, we apply an ellipse fitting method to regress the perimeter of the protected area with piecewise elliptic segments. Then, we characterize the defense behaviors into different patterns and learn the conditions to activate different strategies by tentative invasions. Finally, we design a model predictive controller to solve the optimal invasion trajectory planning. Simulations are provided to illustrate the feasibility and effectiveness of the proposed method.