Michael Fulton

Michael Fulton
University of Minnesota Twin Cities | UMN · Department of Computer Science and Engineering

Master of Science
PhD Candidate at UMN Twin Cities

About

19
Publications
3,192
Reads
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191
Citations
Introduction
I study robotics (particularly underwater robots) with a focus on applications where robots work with humans. I primarily study human-robot interaction and robot perception (using computer vision and deep learning), with the intent of creating systems that can work collaboratively with humans in challenging environments.
Additional affiliations
August 2020 - December 2020
University of Minnesota Twin Cities
Position
  • Research Assistant
Description
  • I was a graduate teaching assistant for CSCI 5551(Introduction to Robotics), where I was responsible for creating programming assignments, teaching several lectures to the whole course, managing online course systems (Canvas and Zoom), grading student work, answering student questions via email, and holding weekly office hours.
January 2018 - May 2021
University of Minnesota Twin Cities
Position
  • Teaching Assisstant
Description
  • I was a graduate teaching assistant for CSCI 4601 (Operating Systems). My duties included creating large programming assignments for the course, teaching two weekly lab sections, holding office hours, grading student work, and answering student questions over email.
May 2016 - August 2016
CSpeed
Position
  • Summer Intern
Description
  • I was a summer intern, working on programming internal productivity software, backup software, and profiling scientific devices.
Education
August 2017 - January 2020
University of Minnesota Twin Cities
Field of study
  • Computer Science (Robotics)
September 2013 - May 2017
Clarkson University
Field of study
  • Computer Science

Publications

Publications (19)
Article
In this article, we propose, implement, and evaluate a motion-based communication system for field robots: robots that operate in dynamic, unstructured, outdoor environments. We perform two pilot studies to guide our development of the system, then evaluate it alongside an audio communication system, an LCD display, and a system of blinking LEDs. W...
Preprint
Full-text available
Direct communication between humans and autonomous underwater vehicles (AUVs) is a relatively underexplored area in human-robot interaction (HRI) research, although many tasks (\eg surveillance, inspection, and search-and-rescue) require close diver-robot collaboration. Many core functionalities in this domain are in need of further study to improv...
Preprint
Full-text available
With the end goal of selecting and using diver detection models to support human-robot collaboration capabilities such as diver following, we thoroughly analyze a large set of deep neural networks for diver detection. We begin by producing a dataset of approximately 105,000 annotated images of divers sourced from videos -- one of the largest and mo...
Preprint
Full-text available
This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris. The dataset has two versions, TrashCan-Material and TrashCan-Instance, corresponding to different object class c...
Preprint
Full-text available
In this paper we present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform's prelimi...
Preprint
Full-text available
This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary...
Preprint
Full-text available
In this paper, we explore the use of motion for robot-to-human communication on three robotic platforms: the 5 degrees-of-freedom (DOF) Aqua autonomous underwater vehicle (AUV), a 3-DOF camera gimbal mounted on a Matrice 100 drone, and a 3-DOF Turtlebot2 terrestrial robot. While we previously explored the use of body language-like motion (called ki...
Article
This paper explores the design and development of a class of robust diver-following algorithms for autonomous underwater robots. By considering the operational challenges for underwater visual tracking in diverse real-world settings, we formulate a set of desired features of a generic diver following algorithm. We attempt to accommodate these featu...
Preprint
Full-text available
This paper presents novel probabilistic algorithms for localization of autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms, based on the principles of the Bayes filter, work by fusing bathymetry information with depth and altitude data from an AUV. Four different Bayes filter-based algorithms are used to design the localizat...
Preprint
Full-text available
In this paper, we propose a novel method for underwater robot-to-human communication using the motion of the robot as "body language". To evaluate this system, we develop simulated examples of the system's body language gestures, called kinemes, and compare them to a baseline system using flashing colored lights through a user study. Our work shows...
Preprint
This paper explores the design and development of a class of robust diver-following algorithms for autonomous underwater robots. By considering the operational challenges for underwater visual tracking in diverse real-world settings, we formulate a set of desired features of a generic diver following algorithm. We attempt to accommodate these featu...
Article
Full-text available
Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. A step towards this goal is the successful detection of trash in unde...

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