Semester

Fall

Date of Graduation

2024

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Yu Gu

Committee Member

Jason Gross

Committee Member

Amr El-Wakeel

Abstract

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach provides a flexible alternative to biologically inspired models, enabling agents to dynamically allocate tasks based on local resource availability and energy constraints. In a simulated grid-based environment built using the PettingZoo Multi-Agent API, agents engage in a foraging task by locating, gathering, and returning resources to a central base. Agent communication is limited to implicit auction-based interactions, where agents encountering one another may exchange resources based on localized bidding informed by opportunity cost. Each agent’s utility function considers the current going rate for foraged resources, battery costs, and remaining energy, allowing agents to self-organize, prioritize tasks, and allocate resources adaptively without explicit role assignments. Performance metrics, including task completion rate, market efficiency, agent specialization index, equilibrium stability in resource pricing, and emergent behaviors, are used to evaluate the system’s efficiency and adaptability. Results demonstrate that economic principles, particularly those grounded in opportunity cost and supply-demand dynamics, can effectively drive task allocation in swarm robotics, fostering emergent specialization and resource prioritization that enhance overall swarm performance. This work contributes to swarm robotics and multi-agent systems by showing that economic models can facilitate adaptive, decentralized coordination, providing a foundation for future research on complex, self-organizing systems.

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