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High-Speed Interception Multicopter Control by Image-based Visual Servoing

Kun Yang, Chenggang Bai, Zhikun She, Quan Quan

TL;DR

This article proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder unmanned aerial vehicle (UAV) and an image-based visual servoing (IBVS) controller is proposed to make the interception fast and accurate.

Abstract

In recent years, reports of illegal drones threatening public safety have increased. For the invasion of fully autonomous drones, traditional methods such as radio frequency interference and GPS shielding may fail. This paper proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder UAV. The interceptor multicopter can autonomously detect and intercept intruders moving at high speed in the air. The strapdown camera avoids the complex mechanical structure of the electro-optical pod, making the interceptor multicopter compact. However, the coupling of the camera and multicopter motion makes interception tasks difficult. To solve this problem, an Image-Based Visual Servoing (IBVS) controller is proposed to make the interception fast and accurate. Then, in response to the time delay of sensor imaging and image processing relative to attitude changes in high-speed scenarios, a Delayed Kalman Filter (DKF) observer is generalized to predict the current image position and increase the update frequency. Finally, Hardware-in-the-Loop (HITL) simulations and outdoor flight experiments verify that this method has a high interception accuracy and success rate. In the flight experiments, a high-speed interception is achieved with a terminal speed of 20 m/s.

High-Speed Interception Multicopter Control by Image-based Visual Servoing

TL;DR

This article proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder unmanned aerial vehicle (UAV) and an image-based visual servoing (IBVS) controller is proposed to make the interception fast and accurate.

Abstract

In recent years, reports of illegal drones threatening public safety have increased. For the invasion of fully autonomous drones, traditional methods such as radio frequency interference and GPS shielding may fail. This paper proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder UAV. The interceptor multicopter can autonomously detect and intercept intruders moving at high speed in the air. The strapdown camera avoids the complex mechanical structure of the electro-optical pod, making the interceptor multicopter compact. However, the coupling of the camera and multicopter motion makes interception tasks difficult. To solve this problem, an Image-Based Visual Servoing (IBVS) controller is proposed to make the interception fast and accurate. Then, in response to the time delay of sensor imaging and image processing relative to attitude changes in high-speed scenarios, a Delayed Kalman Filter (DKF) observer is generalized to predict the current image position and increase the update frequency. Finally, Hardware-in-the-Loop (HITL) simulations and outdoor flight experiments verify that this method has a high interception accuracy and success rate. In the flight experiments, a high-speed interception is achieved with a terminal speed of 20 m/s.
Paper Structure (56 sections, 1 theorem, 67 equations, 13 figures, 1 table, 2 algorithms)

This paper contains 56 sections, 1 theorem, 67 equations, 13 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement and Challenges
  3. Related Work
  4. Review on Autonomous Interception and Racing
  5. Review on Delayed State Estimation
  6. Proposed Methods and Main Contributions
  7. Outline
  8. Model and Problem Formulation
  9. Coordinate Systems and Models
  10. Coordinate Systems
  11. Multicopter Model
  12. Target Model
  13. Camera Imaging Model
  14. IBVS Model
  15. State Equation and Measurement Equations
  16. ...and 41 more sections

Key Result

Theorem 1

Under Assumption assumption:origin-coincides, for a multicopter with strapdown camera model eq:rigid-body-model--eq:measurement model, the attitude controller is designed by eq:controller-summarized. If the initial state satisfies ${\bf {n}}_{{\rm {t}}}\left(0\right) \in \mathcal{S} = \left\{ {\bf

Figures (13)

  • Figure 1: Interception with drones.
  • Figure 2: A classic architecture for an autonomous interception and racing system.
  • Figure 3: Relationship among visual servo model, controller, and state observer.
  • Figure 4: Description of the interception problem. ${^{{\rm {e}}}{\bf {p}}}$ and ${^{{\rm {e}}}{{\bf {p}}_{{\rm {t}}}}}$ respectively represent the position of the intercepting multicopter and the target in EFCS; $^{{\rm {i}}}{\bf {p}}={[{{p_{{x_{{\rm {i}}}}}} \enspace {p_{{y_{{\rm {i}}}}}}}]^{{\rm {T}}}}$ is the coordinate of the target in ICS; ${{{\bf {n}}_{{\rm {t}}}}}$ represents the target unit vector along the Line of Sight (LOS) in EFCS; ${{{\bf {n}}_{{\rm {c}}}}}$ represents the optical axis unit vector in EFCS; ${\bf{n}}_{{\rm{td}}}$ is defined as the unit vector of the designed LOS in EFCS, called the designed LOS vector; ${^{{\rm {i}}}{\bf {p}}}_{{\rm {td}}}$ is the intersection of ${\bf{n}}_{{\rm{td}}}$ with the image plane in ICS, called the intercept point.
  • Figure 5: High-speed interception control process. The process for intercepting a lower target is (a)--(b), and the process for intercepting a higher target is (c)--(d)--(b).
  • ...and 8 more figures

Theorems & Definitions (3)

  • Remark 1
  • Theorem 1
  • proof