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

2025-08-15

AI Summary

This paper addresses the problem of full onboard state estimation and trajectory tracking for unmanned aerial vehicle (UAV) payload slung-load transportation. Methodologically, it proposes a lightweight, highly robust autonomous control framework that fuses RTK-GNSS and IMU measurements within a coupled dynamic model; employs a hierarchical predictive control architecture; integrates linear Kalman filtering for state estimation; and combines model-predictive contour control with incremental MPC for high-precision trajectory tracking. The key contribution lies in eliminating reliance on external motion-capture systems or specialized hardware—achieving closed-loop control using only standard onboard sensors. Simulation results show less than 6% performance degradation under nominal conditions, while outdoor experiments demonstrate strong robustness against modeling uncertainties and parametric perturbations in complex environments. This significantly enhances the practicality and deployment flexibility of slung-load UAV systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile 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.

Problem

• Tracking UAV-suspended payload position with minimal hardware
• Estimating payload dynamics using only onboard sensors
• Achieving robust control despite sensing limitations

Innovation

• Uses standard onboard sensors only
• Integrates Kalman filter and MPC controllers
• Achieves robust outdoor performance