Ouyang, Bowei
(2024)
Olfactory Navigational Strategies in Aqueous Water Plumes and Deterministic Casting Algorithm Analysis.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
This dissertation explores olfactory navigational strategies in aqueous water plumes and analyzes deterministic casting algorithms, providing insights into the complex dynamics of odor-guided navigation. The study comprises two main parts: an investigation of simple olfactory algorithms in various odor landscapes, and an in-depth analysis of a deterministic casting algorithm. In the first part, two local algorithms, bilateral search and temporal comparison (”casting”), are compared for navigating to an odor source in various air and water plumes. Using planar laser-induced fluorescence (PLIF) datasets, these algorithms were simulated under different flow conditions and odor source configurations. The model parameters are optimized to maximize success rates and minimize path tortuosity, revealing the trade-offs between exploration and direct navigation. My findings demonstrate that both algorithms can be tuned to successfully locate odor sources in a wide range of odor landscapes, with performance varying based on plume characteristics and algorithm parameters. The second part focuses on a deterministic casting algorithm, examining its fixed points, stability, basin of attraction, and bifurcation behavior. Mathematically, I prove the existence of two fixed points and analyze their stability in relation to key parameters such as velocity, sensor length, and casting angle. Through bifurcation analysis, transitions from stable behavior to chaos are observed as the casting angle varies. I also apply the algorithm\ to real water plume data, optimizing parameters for success rate and efficiency. This research contributes to our understanding of olfactory navigation in complex environments and has implications for both biological systems and artificial olfactory navigation in robotics. The insights gained from this study could inform the development of more efficient and adaptive navigation strategies in various fields, from environmental monitoring to search and rescue operations.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
20 December 2024 |
| Date Type: |
Publication |
| Defense Date: |
31 October 2024 |
| Approval Date: |
20 December 2024 |
| Submission Date: |
5 December 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
141 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Mathematics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Olfactory Navigation, Chemical Plumes, Casting Algorithm, Bilateral Algorithm, Water Plumes, Air Plumes, Fixed Points, Basin of Attraction, Bifurcation Analysis, Deterministic Casting, Navigation Success Rate, Path Tortuosity, Parameter Optimization, Planar Laser-induced Fluorescence (PLIF), Flow Direction, Sensor Length, Casting Angle, Hill Function, Odor Source Localization, Navigation Algorithms, Dynamical Systems, Turbulent Plumes, Latin Hypercube Sampling, Neural Mechanisms, Biomimetic Navigation |
| Date Deposited: |
20 Dec 2024 14:05 |
| Last Modified: |
10 Apr 2025 14:38 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47201 |
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