Date of Award

6-3-2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Departments

Computer Engineering; Computer Science and Engineering; Electrical Engineering; Electrical and Computer Engineering

First Advisor

Michael Schimpf

Second Advisor

Burak Kurkcu

Abstract

The primary objective of this project is to design and implement a ROS2-based multi-agent robotic prototype capable of demonstrating aggregation and formation-control behavior in a controlled indoor environment. The project explores how multiple mobile robots can coordinate through decentralized interaction rules, peer state information, and namespaced ROS2 communication rather than relying on a single centralized controller.

The project began with theoretical study and MATLAB/Simulink simulation of artificial potential fields, singleintegrator dynamics, aggregation, desired-distance formation control, and obstacle-repulsion concepts. These simulations provided an idealized environment for evaluating the general behavior of the control strategy before physical hardware limitations were introduced.

The later stages of the project focused on translating the simulation-based control approach into a physical multirobot system using Raspberry Pi-based mobile robots, ROS2, Cyclone DDS, LiDAR, IMU data, odometry, filtered state estimation, and mecanum drivetrain control. This transition revealed several practical challenges, including localization drift, sensor noise, wireless communication reliability, namespace management, command routing, and drivetrain error.

The final system demonstrates the core deployment pipeline needed for prototype multi-robot coordination: robot bringup, hardware and topic verification, peer discovery, odometry-based state sharing, velocity-command generation, namespaced command publication, and supervised indoor testing. While the system is not a field-ready autonomous swarm, it provides a working foundation for future development and documents the trade-offs involved in moving from multi-agent control theory to physical robotic deployment.

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