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Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control

Hiu Ching Cheung, Bailun Jiang, Yang Hu, Henry K. Chu, Chih-Yung Wen, Ching-Wei Chang

TL;DR

This work tackles aerial grasping with a Soft Aerial Vehicle by addressing control challenges introduced by payload variations and disturbances. It introduces a disturbance observer-based nonlinear model predictive control (DOMPC) that uses an EKF-based disturbance observer to adapt to dynamic payload changes and environmental disturbances, integrated into a real-time NMPC framework solved with acados/qpOASES. The approach yields accurate 3D tracking and robust grasping performance, achieving a maximum payload of $337/1002 \approx 0.337$ and effective handling of static and non-static payloads across multiple target geometries. The experimental results demonstrate superior translational tracking, stable hovering under grasping, and reliable mid-air manipulation, highlighting the practical potential for payload-rich soft-gripper drones in delivery and harvesting tasks.

Abstract

Aerial grasping, particularly soft aerial grasping, holds significant promise for drone delivery and harvesting tasks. However, controlling UAV dynamics during aerial grasping presents considerable challenges. The increased mass during payload grasping adversely affects thrust prediction, while unpredictable environmental disturbances further complicate control efforts. In this study, our objective aims to enhance the control of the Soft Aerial Vehicle (SAV) during aerial grasping by incorporating a disturbance observer into a Nonlinear Model Predictive Control (NMPC) SAV controller. By integrating the disturbance observer into the NMPC SAV controller, we aim to compensate for dynamic model idealization and uncertainties arising from additional payloads and unpredictable disturbances. Our approach combines a disturbance observer-based NMPC with the SAV controller, effectively minimizing tracking errors and enabling precise aerial grasping along all three axes. The proposed SAV equipped with Disturbance Observer-based Nonlinear Model Predictive Control (DOMPC) demonstrates remarkable capabilities in handling both static and non-static payloads, leading to the successful grasping of various objects. Notably, our SAV achieves an impressive payload-to-weight ratio, surpassing previous investigations in the domain of soft grasping. Using the proposed soft aerial vehicle weighing 1.002 kg, we achieve a maximum payload of 337 g by grasping.

Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control

TL;DR

This work tackles aerial grasping with a Soft Aerial Vehicle by addressing control challenges introduced by payload variations and disturbances. It introduces a disturbance observer-based nonlinear model predictive control (DOMPC) that uses an EKF-based disturbance observer to adapt to dynamic payload changes and environmental disturbances, integrated into a real-time NMPC framework solved with acados/qpOASES. The approach yields accurate 3D tracking and robust grasping performance, achieving a maximum payload of and effective handling of static and non-static payloads across multiple target geometries. The experimental results demonstrate superior translational tracking, stable hovering under grasping, and reliable mid-air manipulation, highlighting the practical potential for payload-rich soft-gripper drones in delivery and harvesting tasks.

Abstract

Aerial grasping, particularly soft aerial grasping, holds significant promise for drone delivery and harvesting tasks. However, controlling UAV dynamics during aerial grasping presents considerable challenges. The increased mass during payload grasping adversely affects thrust prediction, while unpredictable environmental disturbances further complicate control efforts. In this study, our objective aims to enhance the control of the Soft Aerial Vehicle (SAV) during aerial grasping by incorporating a disturbance observer into a Nonlinear Model Predictive Control (NMPC) SAV controller. By integrating the disturbance observer into the NMPC SAV controller, we aim to compensate for dynamic model idealization and uncertainties arising from additional payloads and unpredictable disturbances. Our approach combines a disturbance observer-based NMPC with the SAV controller, effectively minimizing tracking errors and enabling precise aerial grasping along all three axes. The proposed SAV equipped with Disturbance Observer-based Nonlinear Model Predictive Control (DOMPC) demonstrates remarkable capabilities in handling both static and non-static payloads, leading to the successful grasping of various objects. Notably, our SAV achieves an impressive payload-to-weight ratio, surpassing previous investigations in the domain of soft grasping. Using the proposed soft aerial vehicle weighing 1.002 kg, we achieve a maximum payload of 337 g by grasping.
Paper Structure (20 sections, 17 equations, 10 figures, 2 tables)

This paper contains 20 sections, 17 equations, 10 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. Motivation
  3. Related Work
  4. Contribution
  5. Soft Aerial Vehicle Design
  6. Overall System Design
  7. Modular Soft Gripper Design
  8. Aerial Grasping Control
  9. System Dynamics
  10. Disturbance Observer Design
  11. Disturbance Observer-Based Nonlinear Model Predictive Control
  12. EXPERIMENTAL RESULTS AND DISCUSSION
  13. Experiment Implementation
  14. Observer Performance Test
  15. SAV Payload Test
  16. ...and 5 more sections

Figures (10)

  • Figure 1: (a) Our SAV hovers in mid-air. (b) The SAV grasps its target object under its center of gravity by inflating its modular pneumatic soft gripper. (c) Takeoff and landing pose of the SAV with its deflated soft gripper. (d) Demonstrating the high grasping tolerance of the pneumatic soft gripper.
  • Figure 2: (a) Exploded CAD view of the SAV. (b) Side view of a modular pneumatic soft finger.
  • Figure 3: (a) Assembly of H-base (cylindrical) soft gripper. (b) Assembly of X-base (spherical) soft gripper. (c) SAV sketch with its inertial frame $\Gamma_I$ and body frame $\Gamma_B$.
  • Figure 4: Cascaded loop control structure of disturbance observer-based NMPC (DOMPC).
  • Figure 5: Finite state machine diagram for the SAV aerial grasping task.
  • ...and 5 more figures