Search

This article proposes a coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of the following two steps: first, impact point optimization and, second, model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). It allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To avoid the conservativeness, we present a state probability distribution function (pdf) prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor controlled by a human operator in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent the human operators’ evasive maneuver. Then, we can retrieve a human control input pdf from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the multi-rotor's future state pdf by propagating the pdf of the retrieved human control input according to the given dynamics, which yields closed-loop analysis of the HiLCPS. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm.