Author ORCID Identifier

https://orcid.org/0000-0001-5624-0116

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Guilherme Pereira

Committee Member

Adrian Tudorascu

Committee Member

Ali Baheri

Committee Member

Jason Gross

Committee Member

Yu Gu

Abstract

This dissertation proposes solutions for motion planning, localization, and landing of tethered drones using only tether variables. A tether-based multi-model localization framework for tethered drones is proposed. This framework comprises three independent localization strategies based on a different model. The first strategy uses simple trigonometric relations assuming that the tether is taut; the second method relies on a set of catenary equations for the slack tether case; the third estimator is a neural network-based predictor that can cover different tether shapes. Multi-layer perceptron networks previously trained with a dataset comprised of the tether variables (i.e., length, tether angles on the drone, and the platform) as input are used to select which model provides the best results. Those networks can also identify situations where tether localization is impossible, thus rejecting all estimates. The experimental results have shown that the proposed localization framework consistently selects reasonable solutions from the three estimators and rejects them when the input tether variables suggest bad estimation results. In addition, a precise tether-guided landing method based on a vector field using only tether variables is also proposed. This method assumes that the vehicle is attached to a landing platform through a tensioned and free-of-collision cable whose length and angles are measured by a tether management system. The method has proven convergence by Lyapunov stability and is robust to external disturbances since vector fields can be considered a closed-loop problem. The vector field can be conveniently shaped, i.e., to guide the UAV to a safer region (higher altitude) when the drone starts the landing phase at low elevation angles. This can avoid unwanted situations, such as saturation of the tether angle sensors or even collision with obstacles around the landing pad. Experiments with a quad-rotor vehicle landing on static and moving platforms illustrate the method's precision, robustness, and efficacy. Finally, it is proposed a path planning for tethered robots that minimizes tension due to tether-obstacle interaction. This method assumes that the tether is managed externally by a tether management system and pulled by the robot. This method is initially formulated for ground robots in a 2D environment and then extended for a 3D environment for tethered drones. It assumes a taut tether between two consecutive contact points and knowledge about the coefficient of friction of the obstacles present in the environment. The method first computes the visibility graph of the environment, where each node represents a vertex of an obstacle. Then, a second graph is built so that the tether-obstacle friction is computed using the capstan equation and used as a cost function to the edges. Therefore, a graph search algorithm, i.e., Dijkstra, can lead to a path with minimum tension, which can help the tethered robot reach longer distances by minimizing the tension required to drag the tether along the way.

Comments

This dissertation addresses motion planning, localization, and landing challenges of tethered drones, introducing a tether-based multi-model localization framework, a tether-guided landing method based on an artificial vector field, and a path planning algorithm that aims to minimize cable tension for tethered robots in both 2D and 3D environments.

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