Real-Time Trajectory Planning and Effectiveness Analysis of Intercepting Large-Scale Invading UAV Swarms Based on Motion Primitives

Abstract

This paper introduces a swift method for intercepting the state trajectory of large-scale invading drone swarms using quadrotor drones. The research primarily concentrates on the design and computation of multi-target interception trajectories, with an analysis of the trajectory state constraints inherent to multi-target interception tasks. Utilizing Pontryagin’s principle of motion, we have designed computationally efficient motion primitives for multi-target interception scenarios. These motion primitives’ durations have informed the design of cost matrices for multi-target interception tasks. In contrast to static planar scenarios, the cost matrix in dynamic scenarios displays significant asymmetry, correlating with the speed and spatial distribution of the targets. We have proposed an algorithmic framework based on three genetic operators for solving multi-target interception trajectories, offering certain advantages in terms of solution accuracy and speed compared to other optimization algorithms. Simulation results from large-scale dynamic target interception scenarios indicate that for an interception task involving 50 targets, the average solution time for trajectories is a mere 3.7 s. Using the methods proposed in this paper, we conducted a comparative analysis of factors affecting the performance of interception trajectories in various target interception scenarios. This study represents the first instance in existing public research where precise evaluations have been made on the trajectories of drone interceptions against large-scale flying targets. This research lays the groundwork for further exploration into game-theoretic adversarial cluster interception methods.