Date of Award
Santa Clara : Santa Clara University, 2021.
Doctor of Philosophy (PhD)
Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of its environment. Compared to conventional navigation methods, AN techniques can potentially reduce the time and energy needed to explore scalar characteristics of unknown and dynamic regions of interest (e.g., temperature, concentration level). Multiple Uncrewed Aerial Vehicle (UAV) approaches to AN can improve performance by exploiting synchronized spatially-dispersed measurements to generate realtime information regarding the structure of the local scalar field for use in navigation decisions. This dissertation presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using Santa Clara University’s (SCU) unique multilayer control architecture for groups of vehicles. Using SCU’s flexible formation control system, this work builds upon prior 2D AN research and provides new contributions to 3D scalar field AN by a) demonstrating a wide range of 3D AN capabilities using a unified, multilayer control architecture, b) extending multivehicle 2D AN control primitives to navigation in 3D scalar fields, and c) introducing state-based sequencing of these primitive AN functions to execute 3D mission-level capabilities such as isosurface mapping and plume following. Functionality is verified using high-fidelity simulations of multivehicle drone clusters which account for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise. This dissertation presents the multilayer architecture for multivehicle formation control, the 3D AN control primitives, the sequencing approaches for specific mission-level capabilities, and simulation results that demonstrate these functions.
Lee, Robert Ka-Hing, "Adaptive Navigation of Three-Dimensional Scalar Fields with Multiple UAVs" (2021). Engineering Ph.D. Theses. 37.