Victoria M Edwards

Pennovation Center

3401 Grays Ferry Avenue

Philadelphia, PA 19146

I am an experimental roboticist working on heterogeneous robotic teaming. I am interested in how robot teams work together to achieve complex tasks in unstructured environments. This spans interests from large scale collectives demonstrating emergent behavior, to smaller scale teams with sophisticated capabilities and coordinated task strategies. I use tools from dynamical systems theory and probability theory to bridge the gaps between algorithmic methods to deploy teams of robots and existing modeling methods which describe the evolution of the team. Identifying the underlying connections between the model and the algorithm hits at a fundamental question of understanding the continuous world in relation to a discretized algorithm.

news

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	Paper accepted and will be presented at IROS 2023 in Detroit, Michigan. Titled: “On Collaborative Robot Teams for Environmental Monitoring: a Macroscopic Ensemble Approach”
May 11, 2023	Awarded the University of Pennsylvania School of Engineering and Applied Sciences (SEAS) Outstanding Teaching Award, May 2023.
Nov 30, 2022	Paper accepted and presented at DARS 2022 titled: “Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring”
Nov 7, 2022	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

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.
Stochastic nonlinear ensemble modeling and control for robot team environmental monitoring

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

16th International Symposium on Distriubted Autonomous Robotic Systems (DARS) 2022

Abs

We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.