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Efficient Control Allocation and 3D Trajectory Tracking of a Highly Manoeuvrable Under-actuated Bio-inspired AUV

Walid Remmas, Christian Meurer, Yuya Hamamatsu, Ahmed Chemori, Maarja Kruusmaa

Abstract

Fin actuators can be used for for both thrust generation and vectoring. Therefore, fin-driven autonomous underwater vehicles (AUVs) can achieve high maneuverability with a smaller number of actuators, but their control is challenging. This study proposes an analytic control allocation method for underactuated Autonomous Underwater Vehicles (AUVs). By integrating an adaptive hybrid feedback controller, we enable an AUV with 4 actuators to move in 6 degrees of freedom (DOF) in simulation and up to 5-DOF in real-world experiments. The proposed method outperformed state-of-the-art control allocation techniques in 6-DOF trajectory tracking simulations, exhibiting centimeter-scale accuracy and higher energy and computational efficiency. Real-world pool experiments confirmed the method's robustness and efficacy in tracking complex 3D trajectories, with significant computational efficiency gains 0.007 (ms) vs. 22.28 (ms). Our method offers a balance between performance, energy efficiency, and computational efficiency, showcasing a potential avenue for more effective tracking of a large number of DOF for under-actuated underwater robots.

Efficient Control Allocation and 3D Trajectory Tracking of a Highly Manoeuvrable Under-actuated Bio-inspired AUV

Abstract

Fin actuators can be used for for both thrust generation and vectoring. Therefore, fin-driven autonomous underwater vehicles (AUVs) can achieve high maneuverability with a smaller number of actuators, but their control is challenging. This study proposes an analytic control allocation method for underactuated Autonomous Underwater Vehicles (AUVs). By integrating an adaptive hybrid feedback controller, we enable an AUV with 4 actuators to move in 6 degrees of freedom (DOF) in simulation and up to 5-DOF in real-world experiments. The proposed method outperformed state-of-the-art control allocation techniques in 6-DOF trajectory tracking simulations, exhibiting centimeter-scale accuracy and higher energy and computational efficiency. Real-world pool experiments confirmed the method's robustness and efficacy in tracking complex 3D trajectories, with significant computational efficiency gains 0.007 (ms) vs. 22.28 (ms). Our method offers a balance between performance, energy efficiency, and computational efficiency, showcasing a potential avenue for more effective tracking of a large number of DOF for under-actuated underwater robots.
Paper Structure (31 sections, 60 equations, 13 figures, 9 tables)

This paper contains 31 sections, 60 equations, 13 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Problem formulation and Related Work
  3. Control framework
  4. Brief introduction of U-CAT
  5. Hybrid System
  6. Trajectory tracking controller
  7. Error System and Control Problem
  8. Hybrid Adaptive Controller
  9. Control allocation
  10. Direct solution
  11. Optimization based solution
  12. Proposed analytic solution
  13. Force to amplitude
  14. CPG algorithm
  15. Numerical simulations
  16. ...and 16 more sections

Figures (13)

  • Figure 1: Illustration of the earth fixed frame $R_n$ (NED convention), the body fixed frame $R_b$ and the fin rest frame $R^{fin}$ defined for the U-CAT AUV.
  • Figure 2: Illustration of the different fin configurations for controlling the U-CAT robot in each degree of freedom, using either two or four fins. The fins responsible for actuation in each configuration are marked with a red dot.
  • Figure 3: Proposed autonomy architecture consisting of: 1) trajectory generation module in blue, 2) control module including hybrid adaptive 6-DOF controller and control allocation in red, 3) state estimation module with sensors and EKF in green.
  • Figure 4: Illustration of the horizontal force compensation principle to minimize the fins' zero-direction change when controlling vertical forces.
  • Figure 5: Visualization of forces on $i^{th}$ fin-based on a simple lift and drag model (model and figure adopted from georgiades2009simulation).
  • ...and 8 more figures