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Description
Robotic technology is advancing to the point where it will soon be feasible to deploy massive populations, or swarms, of low-cost autonomous robots to collectively perform tasks over large domains and time scales. Many of these tasks will require the robots to allocate themselves around the boundaries of regions or features of interest and achieve target objectives that derive from their resulting spatial configurations, such as forming a connected communication network or acquiring sensor data around the entire boundary. We refer to this spatial allocation problem as boundary coverage. Possible swarm tasks that will involve boundary coverage include cooperative load manipulation for applications in construction, manufacturing, and disaster response.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
ContributorsPeruvemba Kumar, Ganesh (Author) / Berman, Spring M (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Bazzi, Rida (Committee member) / Syrotiuk, Violet (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
In this dissertation, new data-driven techniques are developed to solve three problems related to generating predictive models of the immune system. These problems and their solutions are summarized as follows. The first problem is that, while cellular characteristics can be measured using flow cytometry, immune system cells are often analyzed only after they are sorted into groups by those characteristics. In Chapter 3 a method of analyzing the cellular characteristics of the immune system cells by generating Probability Density Functions (PDFs) to model the flow cytometry data is proposed. To generate a PDF to model the distribution of immune cell characteristics a new class of random variable called Sliced-Distributions (SDs) is developed. It is shown that the SDs can outperform other state-of-the-art methods on a set of benchmarks and can be used to differentiate between immune cells taken from healthy patients and those with Rheumatoid Arthritis. The second problem is that while immune system cells can be broken into different subpopulations, it is unclear which subpopulations are most significant. In Chapter 4 a new machine learning algorithm is formulated and used to identify subpopulations that can best predict disease severity or the populations of other immune cells. The proposed machine learning algorithm performs well when compared to other state-of-the-art methods and is applied to an immunological dataset to identify disease-relevant subpopulations of immune cells denoted immune states. Finally, while immunotherapies have been effectively used to treat cancer, selecting an optimal drug dose and period of treatment administration is still an open problem. In Chapter 5 a method to estimate Lyapunov functions of a system with unknown dynamics is proposed. This method is applied to generate a semialgebraic set containing immunotherapy doses and period of treatment that is predicted to eliminate a patient's tumor. The problem of selecting an optimal pulsed immunotherapy treatment from this semialgebraic set is formulated as a Global Polynomial Optimization (GPO) problem. In Chapter 6 a new method to solve GPO problems is proposed and optimal pulsed immunotherapy treatments are identified for this system.
ContributorsColbert, Brendon (Author) / Peet, Matthew M (Thesis advisor) / Acharya, Abhinav P (Committee member) / Berman, Spring M (Committee member) / Crespo, Luis G (Committee member) / Yong, Sze Z (Committee member) / Arizona State University (Publisher)
Created2021