State and Dynamics Estimation in an Outdoor Multi-Drone Slung Load System
Name
merton-hmer101-smme-meche-2024-thesis.pdf
Description
Thesis PDF
Size
50.83 MB
Format
Adobe PDF
Checksum (MD5)
e767ca0ead7df0952a9bb18f7748f7e7
Author(s)
Merton, Harvey
Advisor(s)
Hunter, Ian W.
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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