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A CBF-Adaptive Control Architecture for Visual Navigation for UAV in the Presence of Uncertainties

Viswa Narayanan Sankaranarayanan, Akshit Saradagi, Sumeet Satpute, George Nikolakopoulos

Abstract

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which is tracked by an adaptive controller to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

A CBF-Adaptive Control Architecture for Visual Navigation for UAV in the Presence of Uncertainties

Abstract

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which is tracked by an adaptive controller to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.
Paper Structure (12 sections, 25 equations, 7 figures)

This paper contains 12 sections, 25 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Dynamics of quadrotor UAV
  4. Visual-locking constraint for ascending
  5. Descending constraint
  6. Control Architecture
  7. Control Barrier Function Design
  8. Visual locking CBF
  9. Descending CBF
  10. Experimental Validation
  11. Results and Analysis
  12. Conclusions

Figures (7)

  • Figure 2: A representation of the reference frames with $\mathbf{O}_D, \mathbf{O}_C, \mathbf{O}_W, \mathbf{O}_T$ as the origins of the UAV's body frame, camera frame, base frame, and target frame respectively. Their respective axes are subscripted with D, C, W, and T.
  • Figure 3: A schematic representation of the constraints with respect to the UAV and UGVs: (i) the visual locking constraint between the camera frame and the base frame used in the ascending phase, where the green area is the safe region; (ii) the descending constraint used in the approaching phase between the UAV and the target, where the red area is an unsafe region, and region $A$ is the focus region; (iii) the descending constraint used in the landing phase, where region $B$ is the landing region. It is to be noted that constraints in (ii) and (iii) are formed with a single function with different parameters.
  • Figure 4: Block diagram of the proposed control architecture.
  • Figure 5: The boundary layer of the VCBF used in the ascending phase with the relative trajectory of the base frame represented in the camera frame for both settings.
  • Figure 6: The values of the CBF $h_v, h_d$ during (a) ascending, (b) approaching, and (c) descending phases for both the runs and (d) the plot between the vertical and horizontal distance of the UAV in the target frame with the boundaries.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Remark 1
  • Remark 2: Proximity
  • Definition 1