Adaptive Gain Nonlinear Observer for External Wrench Estimation in Human-UAV Physical Interaction
Hussein N. Naser, Hashim A. Hashim, Mojtaba Ahmadi
AI summary
Problem
Dedicated force-torque sensors add prohibitive weight, cost, and complexity to UAVs, limiting payload capacity and flight endurance. Accurately estimating human-applied interaction forces without direct sensing remains challenging due to system nonlinearities and time-varying inertial properties.
Approach
The authors derive a full nonlinear dynamic model for a two-quadrotor payload system and design an acceleration-free adaptive gain nonlinear observer that explicitly accounts for time-varying inertia to estimate external wrenches using only standard navigation data.
Key results
- Comprehensive dynamic model for a cooperative two-quadrotor payload system
- Acceleration-free adaptive gain nonlinear observer for wrench estimation
- Lyapunov-based stability proof guaranteeing convergence under time-varying inertia
- Simulation validation showing lower RMSE than an Extended Kalman Filter, particularly for torque estimation
Why it matters
Eliminates the need for heavy, expensive force sensors while enabling safe, intuitive human-guided aerial payload transportation.
Abstract
This paper presents an Adaptive Gain Nonlin- ear 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 ob- server is rigorously established using Lyapunov-based analysis. Simulation results validate the effectiveness of the proposed observer in enabling intuitive and safe human-UAV interac- tion. 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.