Toward Scalable Morphogenetic Engineering: Natural Computing in SPH Swarm Control

Author

Allen C. McBride, University of Tennessee, KnoxvilleFollow

Date of Award

5-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Computer Science

Major Professor

Bruce J. MacLennan

Committee Members

Lynne E. Parker, James S. Plank, Louis J. Gross

Abstract

Artificial morphogenesis (or morphogenetic engineering) seeks inspiration from developmental biology to engineer self-organizing systems. The Morphgen language uses partial differential equations (PDEs) to express artificial morphogenetic processes as spatial fields describing large numbers of agents in the continuum limit. I present an approach to compile such systems of PDEs by discretizing their behavior to derive controllers for finite numbers of agents of finite size. This approach builds on a generalization of methods to control swarms of robots based on the computational fluid dynamics technique of smoothed particle hydrodynamics (SPH). I address potential scalability and efficiency challenges in SPH robotics by proposing embodied kernel functions that allow key operations to be offloaded to the physical environment. These kernel functions assume the ability of agents to produce local physical fields with certain properties. I especially explore the possibility of cell-like agents able to control the secretion rate of morphogens that diffuse and are degraded in an aqueous medium. I present strategies to mitigate gaps between simplifying assumptions and expected realities. I explore in simulation the validity, accuracy, and robustness of these strategies and of my overall approach.

Recommended Citation

McBride, Allen C., "Toward Scalable Morphogenetic Engineering: Natural Computing in SPH Swarm Control. " PhD diss., University of Tennessee, 2022.
https://trace.tennessee.edu/utk_graddiss/7111