Towards Safe Mid-Air Drone Interception: Strategies for Tracking & Capture

TL;DR

The paper addresses safe mid-air interception of non-cooperative UAVs using a net-equipped interceptor. It introduces two guidance strategies: an MPC-based trajectory planner and an PN-derived Enhanced Proportional Navigation (EPN), and couples them with an IMM-based state estimator augmented by a LiDAR measurement uncertainty model. Extensive simulations on 100 target trajectories and 14 hours of data, plus SITL validation and real-world experiments, show that EPN achieves faster time to first contact and higher interception rates, with MPC offering complementary trajectory optimization. A publicly available dataset of testing trajectories and robust real-world demonstrations establish the practicality of a complete autonomous interception system.

Abstract

A unique approach for the mid-air autonomous aerial interception of non-cooperating UAV by a flying robot equipped with a net is presented in this paper. A novel interception guidance method dubbed EPN is proposed, designed to catch agile maneuvering targets while relying on onboard state estimation and tracking. The proposed method is compared with state-of-the-art approaches in simulations using 100 different trajectories of the target with varying complexity comprising almost 14 hours of flight data, and EPN demonstrates the shortest response time and the highest number of interceptions, which are key parameters of agile interception. To enable robust transfer from theory and simulation to a real-world implementation, we aim to avoid overfitting to specific assumptions about the target, and to tackle interception of a target following an unknown general trajectory. Furthermore, we identify several often overlooked problems related to tracking and estimation of the target's state that can have a significant influence on the overall performance of the system. We propose the use of a novel state estimation filter based on the IMM filter and a new measurement model. Simulated experiments show that the proposed solution provides significant improvements in estimation accuracy over the commonly employed KF approaches when considering general trajectories. Based on these results, we employ the proposed filtering and guidance methods to implement a complete autonomous interception system, which is thoroughly evaluated in realistic simulations and tested in real-world experiments with a maneuvering target going far beyond the performance of any state-of-the-art solution.

Figures (4)

• Figure 1: Collage of a successful autonomous interception of a moving target using the proposed system. The maneuver took approx. 2s from $t_1$ to $t_4$.
• Figure 2: Illustration of the fundamental concepts of PN, highlighting the LOS. $\bm{v}_{\text{int}}$ denotes the velocity of the interceptor, and $\bm{v}_{\text{tg}}$ denotes the velocity of the target.
• Figure 3: Comparison between the commanded acceleration of LPN and EPN. The white arrows and indicate the acceleration and the color its magnitude for different positions of the interceptor. The target is positioned in the center and is flying at 1ms along the $x$-axis (red arrow). The interceptor is flying at 2ms in the same direction.
• Figure 4: Example trajectories of the target from the evaluation dataset (left column), the trajectory used in the full-system simulation (top right) and the fast trajectory used in the real-world experiments (bottom right). Color encodes velocity, dimensions are in meters.