Computer Science ETDs

Publication Date

12-1-2015

Abstract

Human beings are driven to explore distant new worlds as we seek to better understand our place in the Universe. Because of the inherent dangers of human spaceflight, we often send robots as surrogate explorers, controlled from millions of miles away by teams of capable rover drivers here on Earth. As technology continues to advance, scientists and engineers aspire to build low-cost, durable, fully autonomous rovers to succeed today's tele-operated extraplanetary explorers. Here we aim to advance this goal by designing and programming robots that can successfully navigate unknown and variable environments. We present a swarm robotics system that mimics the foraging behaviors of seed-harvester ants, employing evolutionary computation and machine learning to mitigate the adverse effects of unreliable information, variable environments, congestion bottlenecks, and sparse resources. We describe a central-place foraging algorithm (CPFA) whose parameters are evolved by a genetic algorithm (GA) to maximize foraging performance under different experimental conditions. We find that foraging for resources in heterogeneous clusters requires more complex communication, memory, and environmental sensing than strategies evolved in previous work. Additionally, we observe sub-linear scaling in resources collected per robot as swarm size increases, which we attribute to the 'bottleneck' constraint imposed by central-place foraging. Finally, we augment our foraging robot swarm with machine learning and statistical models, demonstrating that combining our existing biologically-inspired CPFA with a cluster exploitation algorithm produces more efficient total resource collection compared to each algorithm acting alone. While our system is designed to be a demonstration platform for swarm robotics research, this work provides a foundation for designing and implementing autonomous robot swarms that can function outside of the academic research laboratory. The ability of robot swarms to tolerate sensor noise, adapt to variable environments, distribute work across large teams, and identify and exploit heterogeneously-distributed resources are all critical factors for successful remote exploration missions on distant worlds.

Language

English

Keywords

Swarm robotics, Biologically-inspired computation, Central-place foraging, Genetic algorithms, Agent-based models

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Advisor

Moses, Melanie E.

First Committee Member (Chair)

Tapia, Lydia

Second Committee Member

Fierro, Rafael

Third Committee Member

Winfield, Alan F.T.

Project Sponsors

National Science Foundation, Defense Advanced Research Projects Agency, James S. McDonnell Foundation

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