State and Dynamics Estimation in an Outdoor Multi-Drone Slung Load System

Abstract

Over the past decade, aerial drones have been used to address problems in areas such as sensing and measurement, inspection, delivery, security, and defense. Adding a load attached to one or more drones using a flexible cable can significantly enhance the capabilities of these platforms. This work aims to develop a multi-drone platform, built on open-source tools such as PX4 and ROS2, that can be used to lift a general slung load in an outdoor environment. Various fidelity simulators, including a pseudo-photo-realistic Gazebo simulator, are developed alongside a functional real world platform for testing load pose estimation methods. A novel cable-based testing apparatus that enables drone translation is used to facilitate stability testing of a quasi-static formation control method for lifting a slung load. This work aims to be the first to use visual feedback to estimate a load’s pose in a multi-drone slung load system operating without external motion capture devices. In simulation, perspective-n-point-based visual estimation achieves position errors of 0.1 m, and geodesic distance attitude errors around 0 ◦ . Real world testing shows errors of 0.2 m and 5 ◦ respectively. Applying extended Kalman filter and unscented Kalman filter formulations, simulated position estimates average around an error of 0 m, while the error noise magnitude is only 6% of the cable length at 0.06 m. Achieving accurate load pose estimates without an inertial measurement unit mounted to the load requires a good cable dynamics model. This work concludes by presenting a novel model for the effect of cables in a drone-slung-load system. A method based on universal differential equations shows promising early results.