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Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

Valerio Brunacci, Davide Plozza, Alessio De Angelis, Michele Magno, Tommaso Polonelli

TL;DR

This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs) by extending the sensing horizon and providing complementary viewpoints to enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments.

Abstract

We present a complete infrastructure-less magneto-inductive (MI) localization system enabling a lightweight UAV to autonomously hover, track, and land with centimeter precision on a mobile quadruped robot acting as a dynamic docking pad. This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs). By extending the sensing horizon and providing complementary viewpoints, the UAVs enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments. The proposed system aims to complements traditional localization modalities with a compact, embedded, and infrastructure-less magnetic sensing approach, providing accurate short-range relative positioning to bridge the gap between coarse navigation and precise UAV docking. A single lightweight receive coil and a fully embedded estimation pipeline on the UAV deliver 20 Hz relative pose estimates in the UGV's frame, achieving a 3D position root-mean-square error (RMSE) of 5 cm. The system uses real-time estimation and a warm-started solver to estimate the 3D position, which is then fused with inertial and optical-flow measurements in the onboard extended Kalman filter. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in UAV--UGV teaming in infrastructure-less scenarios compared to state-of-the-art methods, requiring no external anchors or global positioning. In dynamic scenarios, the UAV tracks and docks with a moving UGV while maintaining a 7.2 cm RMSE and achieving successful autonomous landings.

Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

TL;DR

This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs) by extending the sensing horizon and providing complementary viewpoints to enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments.

Abstract

We present a complete infrastructure-less magneto-inductive (MI) localization system enabling a lightweight UAV to autonomously hover, track, and land with centimeter precision on a mobile quadruped robot acting as a dynamic docking pad. This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs). By extending the sensing horizon and providing complementary viewpoints, the UAVs enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments. The proposed system aims to complements traditional localization modalities with a compact, embedded, and infrastructure-less magnetic sensing approach, providing accurate short-range relative positioning to bridge the gap between coarse navigation and precise UAV docking. A single lightweight receive coil and a fully embedded estimation pipeline on the UAV deliver 20 Hz relative pose estimates in the UGV's frame, achieving a 3D position root-mean-square error (RMSE) of 5 cm. The system uses real-time estimation and a warm-started solver to estimate the 3D position, which is then fused with inertial and optical-flow measurements in the onboard extended Kalman filter. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in UAV--UGV teaming in infrastructure-less scenarios compared to state-of-the-art methods, requiring no external anchors or global positioning. In dynamic scenarios, the UAV tracks and docks with a moving UGV while maintaining a 7.2 cm RMSE and achieving successful autonomous landings.
Paper Structure (20 sections, 5 equations, 7 figures, 3 tables, 2 algorithms)

This paper contains 20 sections, 5 equations, 7 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Relative Localization for Teaming Heterogeneous Robots and Docking
  4. Magneto-Inductive and Near-Field Magnetic Localization
  5. Magnetic Model & Estimator
  6. Measurement Model
  7. Localization Method
  8. Sensor Fusion with Onboard State Estimation
  9. System Architecture
  10. Magnetic Deck on the nano-UAV
  11. Experimental Evaluation
  12. Field Experimental Protocol
  13. Evaluation Metrics
  14. Experimental Results
  15. Static Hovering and Landing (S1)
  16. ...and 5 more sections

Figures (7)

  • Figure 1: Representative picture of a nano-size UAV autonomously landing on a moving legged robot and GNSS-denied environment. The infrastructure-free localization systems relies on onboard sensors, combining IMUs, optical-flow camera, UWB, and the MI system. The four reference coils are represented by the red lines with a magnetic dipole shape. The landing deck can be used to recharge the UAV's batteries.
  • Figure 2: Custom Magnetic Hardware. (a) The Unitree AI legged robot UGV. (b) One of the four lightweight MI coils used for precise localization. (c) The Crazyflie 2.1 nano-UAV. (d) The landing pad with a diameter of 22 mounted on UGV. (e) The wiring for the AnchorDeck and the four coils, plus the connections for the wireless battery charger. (f) The ultra-lightweight MI coil mounted on the Crazyflie 2.1 nano-UAV and the MagneticDeck. (g) Motion caption system markers used to assess the system performances.
  • Figure 3: System Overview Block Diagram. The AnchorDeck (orange box) houses the signal generation and driving logic, powered by the UGV battery. It directly drives four independent anchor coils. The magnetic field is received by the UAV, processed alongside UWB and IMU data within the EKF for state estimation.
  • Figure 4: Visual overview of the experimental validation scenarios using the proposed magnetic localization system. (a)-(c) S1 (Static Hovering & Landing): The nano-UAV performs an autonomous sequence of takeoff, hovering, and precision landing on the stationary UGV. (d)-(f) S2 (Linear Tracking & Landing): The nano-UAV tracks the UGV moving along a linear trajectory. Visualization includes (d) 3D perspective, (e) lateral view showing the relative distance maintenance ($T_0$ to $T_1$), and (f) top-down view of the alignment. (g)-(h) S3 (Composite Motion Tracking): The UAV tracks and follows the UGV performing complex planar maneuvers with varying velocity and direction, maintaining the relative position within the magnetic workspace.
  • Figure 5: Touchdown accuracy analysis for scenario S1-Hovering. The plot shows the planar 2D position of the UAV relative to the docking pad center at the moment of landing for the proposed Mag+Flow method (circles).
  • ...and 2 more figures