Towards Fully Onboard State Estimation and Trajectory Tracking for UAVs with Suspended Payloads

TL;DR

The paper tackles onboard payload-position tracking for UAVs with suspended cables using only standard UAV sensors. It combines a taut-cable dynamic model with aerodynamic drag, a linear Kalman filter for state estimation, and incremental MPC with a long-horizon MPCC planner to achieve robust, real-time control. Validation includes high-fidelity simulations showing near-ground-truth performance (<6% degradation) and field experiments confirming practicality with off-the-shelf hardware. This approach lowers hardware barriers for outdoor suspended-payload tasks, enabling scalable, low-cost deployments in diverse applications.

Abstract

This paper addresses the problem of tracking the position of a cable-suspended payload carried by an unmanned aerial vehicle, with a focus on real-world deployment and minimal hardware requirements. In contrast to many existing approaches that rely on motion-capture systems, additional onboard cameras, or instrumented payloads, we propose a framework that uses only standard onboard sensors--specifically, real-time kinematic global navigation satellite system measurements and data from the onboard inertial measurement unit--to estimate and control the payload's position. The system models the full coupled dynamics of the aerial vehicle and payload, and integrates a linear Kalman filter for state estimation, a model predictive contouring control planner, and an incremental model predictive controller. The control architecture is designed to remain effective despite sensing limitations and estimation uncertainty. Extensive simulations demonstrate that the proposed system achieves performance comparable to control based on ground-truth measurements, with only minor degradation (< 6%). The system also shows strong robustness to variations in payload parameters. Field experiments further validate the framework, confirming its practical applicability and reliable performance in outdoor environments using only off-the-shelf aerial vehicle hardware.

Figures (12)

• Figure 1: UAV carrying a cable suspended payload.
• Figure 2: Coordinate systems and generalized coordinates of the UAV-payload system.
• Figure 3: Architecture of the control framework. Blue block represents the proposed solution.
• Figure 4: Illustration of the Gaussian weighting function $\omega_i$ along the planning horizon, centered at reference times $t_{r,k}$.
• Figure 5: Open-loop planned trajectories for the square scenario, shown at different reference speeds.