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Control of Unknown Quadrotors from a Single Throw

Till M. Blaha, Ewoud J. J. Smeur, Bart D. W. Remes

TL;DR

The proposed algorithm runs efficiently on microcontrollers found in common UAV flight controllers, and was shown to recover an agile quadrotor every time in live experiments with as low as 3.5m throw height, demonstrating robustness against initial rotations and noise.

Abstract

This paper presents a method to recover quadrotor UAV from a throw, when no control parameters are known before the throw. We leverage the availability of high-frequency rotor speed feedback available in racing drone hardware and software to find control effectiveness values and fit a motor model using recursive least squares (RLS) estimation. Furthermore, we propose an excitation sequence that provides large actuation commands while guaranteeing to stay within gyroscope sensing limits. After 450ms of excitation, an INDI attitude controller uses the 52 fitted parameters to arrest rotational motion and recover an upright attitude. Finally, a NDI position controller drives the craft to a position setpoint. The proposed algorithm runs efficiently on microcontrollers found in common UAV flight controllers, and was shown to recover an agile quadrotor every time in 57 live experiments with as low as 3.5m throw height, demonstrating robustness against initial rotations and noise. We also demonstrate control of randomized quadrotors in simulated throws, where the parameter fitting RMS error is typically within 10% of the true value. This work has been submitted to IROS 2024 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Control of Unknown Quadrotors from a Single Throw

TL;DR

The proposed algorithm runs efficiently on microcontrollers found in common UAV flight controllers, and was shown to recover an agile quadrotor every time in live experiments with as low as 3.5m throw height, demonstrating robustness against initial rotations and noise.

Abstract

This paper presents a method to recover quadrotor UAV from a throw, when no control parameters are known before the throw. We leverage the availability of high-frequency rotor speed feedback available in racing drone hardware and software to find control effectiveness values and fit a motor model using recursive least squares (RLS) estimation. Furthermore, we propose an excitation sequence that provides large actuation commands while guaranteeing to stay within gyroscope sensing limits. After 450ms of excitation, an INDI attitude controller uses the 52 fitted parameters to arrest rotational motion and recover an upright attitude. Finally, a NDI position controller drives the craft to a position setpoint. The proposed algorithm runs efficiently on microcontrollers found in common UAV flight controllers, and was shown to recover an agile quadrotor every time in 57 live experiments with as low as 3.5m throw height, demonstrating robustness against initial rotations and noise. We also demonstrate control of randomized quadrotors in simulated throws, where the parameter fitting RMS error is typically within 10% of the true value. This work has been submitted to IROS 2024 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Paper Structure (19 sections, 11 equations, 5 figures, 2 tables)

This paper contains 19 sections, 11 equations, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. METHODOLOGY
  3. Controller
  4. Inner Loop
  5. Attitude control
  6. Position control
  7. Gain tuning
  8. Model Identification
  9. RLS
  10. Motor model
  11. Effectiveness $B_1$ and $B_2$
  12. Excitation
  13. Experimental Setup
  14. Conditions
  15. Simulation Setup
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Composite image: The quadrotor learns to fly before it would hit the ground. Moving picture at https://youtu.be/CLFPXcpzA14
  • Figure 2: INDI inner loop similar to smeur_adaptive_2016 with linearized motor acceleration dynamics
  • Figure 3: Excitation after detection of throwing. In this trial, the commands for motors 1 and 2 are truncated to avoid sensor saturation in the roll axis.
  • Figure 4: Time evolution of the control effectiveness around the yaw axis.
  • Figure 5: Angular rate tracking and motor commands during recovery