Author ORCID Identifier
Date of Award
2024
Document Type
Thesis (Ph.D.)
Department or Program
Computer Science
First Advisor
Alberto Quattrini Li
Abstract
As the global community confronts the pressing issues of climate change, the importance of environment monitoring cannot be overstated. This process is essential in identifying and tracking critical environmental trends to facilitate efficient and effective conservation efforts. Traditionally, scientists perform this task using hand-held, in-situ instruments. With the recent robot advancements, this task can be fully automated by applying multi-robot systems to a task referred to as multi-robot adaptive sampling. However, the cost of purchasing and operating robot systems for this application are still prohibitive due to various environmental, technological, and logistical constraints. Some of the outstanding constraints to the automation of these efforts are associated with communication, environment size, time, and energy limitations.
This thesis investigates the development of solutions that explicitly consider these constraints. Regarding communication constraints, the thesis addresses limitations encountered in environments with poor to no communication connectivity, such as underwater, caves, or extraterrestrial regions. Poor communication is characterized by short-range communication, low bandwidth, and throughput, which restricts the reliance of robots on communication for coordination. In terms of environment size limitations, the thesis tackles inefficiencies in environment modeling of large environments to ensure accuracy in estimates. Despite several previous attempts to enhance environment modeling methods, such as Gaussian Processes, such methods are still computationally prohibitive for use in large dynamic environments. The thesis addresses time constraints, particularly challenges associated with sampling varying space-time phenomena. Novel methods are proposed to reduce the logistical demands of deploying and operating robots in persistent monitoring tasks. Finally, energy constraints are addressed with a focus on studying the effects of dynamic environments on energy consumption. Adaptive methods are proposed to minimize energy usage by accounting for these dynamics, as energy consumption is a significant factor limiting the duration of monitoring missions.
The envisioned broader outcome of these efforts includes the enabling of more detailed studies of natural environmental processes. Ultimately, this deeper understanding will contribute to better addressing complex challenges such as climate change and its impacts.
Recommended Citation
Masaba, Kizito, "Scalable Methods for Resource-Constrained Adaptive Sampling" (2024). Dartmouth College Ph.D Dissertations. 321.
https://digitalcommons.dartmouth.edu/dissertations/321
