Victoria M Edwards

Pennovation Center

3401 Grays Ferry Avenue

Philadelphia, PA 19146

I am an experimental roboticist working at the intersection of Robotics, Complex Systems Theory and Dynamical Systems Theory. I have interests in the full range of multi robot systems, including robot teams with sophisticated capabilities and coordinated task strategies, as well as large scale collectives demonstrating emergent behavior. Throughout my research I have experimented with unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs). To increase our understanding of ongoing environmental changes, it will be important to use heterogeneous robot teams to capture data at different space and times scales. My research objective is to combine environment models with models from Complex Systems theory to control robot teams monitoring dynamic environments. Throughout my dissertation, I have made contributions to solving the multi robot task allocation problem for robot teams deployed in dynamic environments. Our results demonstrate the power of complex systems theory for analyzing robotic systems to enable increased scalability and flexibility for operating in uncertain and dynamic environments. Overall, two themes emerge in my research.

1) How do we design and coordinate multi robot systems to perform missions in uncertain and dynamic environments? and

2) How do we use robot teams to help understand natural phenomena, e.g., changing water temperatures.

I hope increasing the capability of multi robot systems results in tools that can contribute to improving our climate resiliency.

I am currently a PhD candidate at the University of Pennsylvania, Mechanical Engineering and Applied Mechanics Department (MEAM). I work in the Scalable Autonomous Robotics Lab with Dr. Ani Hsieh as a member of the GRASP Lab. Prior to my PhD I was a Karles Fellow working as an Autonomous Systems Scientist at the US Naval Research Lab. I recieved my Masters of Science in Robotics from the University of Michigan, and I recieved my Bachelors of Arts in Computer Science and Mathematics from Colby College. When I am not working to improve robot teams you can find me walking my corgi, knitting, or baking cakes.

news

Oct 30, 2024	Our paper was accepted and presented at DARS 2024 titled: “A Macroscopic Ensemble Modeling Approach to Collaborative Task Assignment in Dynamic Environments” Photo taken by Danna Ma on October 30 at Cornell Tech, NYC
May 15, 2024	I was awarded the University of Pennsylvania School of Engineering and Applied Sciences (SEAS) Outstanding Teaching Award, May 2024.
Oct 5, 2023	Come to our workshop “Robotics for the Blue Economy and Climate Resiliency” at IROS 2023 in Detroit Michigan. Program starts at 0830 - 1730 and includes outstanding speakers, poster presentations, networking opportunities and much more! For more information about the workshop look here.
Oct 4, 2023	Our paper was accepted and presented at IROS 2023 in Detroit, Michigan. Titled: “On Collaborative Robot Teams for Environmental Monitoring: a Macroscopic Ensemble Approach”
May 11, 2023	I was awarded the University of Pennsylvania School of Engineering and Applied Sciences (SEAS) Outstanding Teaching Award, May 2023.
Nov 30, 2022	Our paper was accepted and presented at DARS 2022 titled: “Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring”
Nov 7, 2022	I was awarded MEAM Department Spring 2022 Outstanding TA Award. Presented by Dr. Prashant Purohit, and nominated by Dr. Nadia Figueroa for work done in MEAM 520: Introduction to Robotics
Aug 10, 2022	ScalAR Lab Group Summer 2022
May 24, 2022	ScalAR Lab Demos for ICRA 2022 in Philadelphia
May 1, 2021	I was awarded the NDSEG Fellowship, 2021 Cohort

selected publications

Delay induced swarm pattern bifurcations in mixed reality experiments

Edwards, Victoria, deZonia, Philip, Hsieh, M Ani, Hindes, Jason, Triandaf, Ioana, and Schwartz, Ira B

Chaos: An Interdisciplinary Journal of Nonlinear Science 2020

Abs

Swarms of coupled mobile agents subject to inter-agent wireless communication delays are known to exhibit multiple dynamic patterns in space that depend on the strength of the interactions and the magnitude of the communication delays. We experimentally demonstrate communication delay-induced bifurcations in the spatio-temporal patterns of robot swarms using two distinct hardware platforms in a mixed reality framework. Additionally, we make steps toward experimentally validating theoretically predicted parameter regions where transitions between swarm patterns occur. We show that multiple rotation patterns persist even when collision-avoidance strategies are incorporated, and we show the existence of multi-stable, co-existing rotational patterns not predicted by usual mean field dynamics. Our experiments are the first significant steps towards validating existing theory and the existence and robustness of the delay-induced patterns in real robotic swarms.
On Collaborative Robot Teams for Environmental Monitoring: A Macroscopic Ensemble Approach

Edwards, Victoria, Silva, Thales C, Mehta, Bharg, Dhanoa, Jasleen, and Hsieh, M Ani

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023

Abs

With the rapidly changing climate and an increase in extreme weather events, it is necessary to have better methods to monitor and study the impacts of these phenomena on urban river environments. Multi-robot environmental monitoring has long focused on strategies that assign individual robots to distinct regions or task objectives. While these methods have seen success for Autonomous Surface Vehicles (ASVs), the spatial expanse and temporal variability of rivers impose an increased burden on existing techniques, necessitating computationally intensive replanning. Alternative methods aim to model and control teams of robots by prescribing global constraints on the system, using the insight that robots’ transitions between tasks are stochastic and time-based. These methods do not require replanning because robots will perform different tasks achieving the overall desired system state, focusing on temporal switching alone limits their overall descriptive power. In this paper, we present a method that considers collaborations between robots to inform task switching based on spatial proximity. Our results suggest that in unknown environments macroscopic models provide increased flexibility for individual robot task execution as compared to coverage control methods.
A Macroscopic Ensemble Modeling Approach to Collaborative Task Assignment in Dynamic Environments

Edwards, Victoria, Silva, Thales C, and Hsieh, M Ani

17th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024

Abs

Monitoring a dynamic environment with robot teams requires continuously solving the multi-robot task allocation (MRTA) problem in response to environmental changes. The adaptive assignment of robots to different parts of the workspace as the environment changes makes this a Single-Task robots, Mulit-Robot tasks, and Time-extended Assignment (ST-MR-TA) problem. Solutions to this problem can be classified as either macroscopic, where the assignment is obtained using a mean-field model of the team dynamics, or microscopic, where the assignment is posed as a resource allocation problem. While macroscopic techniques are scalable with team sizes and number of tasks, they lack expressiveness. On the other hand, microscopic techniques are more expressive, but often require expensive replanning when environmental conditions change. In this work, we propose an alternative macroscopic formulation to the ST-MR-TA problem that results in a time-varying task assignment that can correspond to a changing environment. Our analysis of the macroscopic model uncovers parameter regimes where time-varying populations exist and further investigates the break down when the system is not well-mixed, i.e., when team sizes are small. Simulation validation shows that our proposed feedback control is necessary for small teams.