Adaptive Gain Nonlinear Observer for External Wrench Estimation in Human-UAV Physical Interaction

TL;DR

This paper introduces an Adaptive Gain Nonlinear Observer (AGNO) for accurately estimating external forces and torques in human-UAV interactions, improving robustness and eliminating the need for dedicated sensors.

Contribution

The paper develops a novel AGNO that explicitly considers nonlinear dynamics and variable inertia, providing more accurate and robust wrench estimation without extra hardware.

Findings

AGNO outperforms EKF in estimation accuracy

Effective in nonlinear human-UAV interaction scenarios

Reduces system weight and cost by eliminating sensors

Abstract

This paper presents an Adaptive Gain Nonlinear Observer (AGNO) for estimating the external interaction wrench (forces and torques) in human-UAV physical interaction for assistive payload transportation. The proposed AGNO uses the full nonlinear dynamic model to achieve an accurate and robust wrench estimation without relying on dedicated force-torque sensors. A key feature of this approach is the explicit consideration of the non-constant inertia matrix, which is essential for aerial systems with asymmetric mass distribution or shifting payloads. A comprehensive dynamic model of a cooperative transportation system composed of two quadrotors and a shared payload is derived, and the stability of the observer is rigorously established using Lyapunov-based analysis. Simulation results validate the effectiveness of the proposed observer in enabling intuitive and safe human-UAV interaction. Comparative evaluations demonstrate that the proposed AGNO outperforms an Extended Kalman Filter (EKF) in terms of estimation root mean square errors (RMSE), particularly for torque estimation under nonlinear interaction conditions. This approach reduces system weight and cost by eliminating additional sensing hardware, enhancing practical feasibility.