publications

2025

1. Distributed Adaptive Macroscopic Ensemble Task Allocation of Heterogeneous Robot Teams in Dynamic Environments

   Victoria Edwards, and M Ani Hsieh

   In Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems, 2025

   Abs

   Robot teams that build highly predictive models of environments like the ocean require effective methods to collect sensor data. A robot team can collect this data, but the challenge is continuously assigning robots to informative sampling locations. Existing methods design specialized solutions for individual robots that lack the necessary flexibility. Instead, recent extensions allow macroscopic ensemble allocation to adapt with environmental changes, but currently relies on impractical centralized assumptions. To address the need for centralization, we introduce a heterogeneous formulation with unmanned aerial vehicles and a communication task where a small portion of the surface team ferries information between robots performing spatially distributed sampling tasks. We show a distributed adaptive macroscopic allocation solution with similar performance to the centralized strategy.

2. Flow-Based Localization and Mapping for Multi-Robot Systems

   Arjun Kumar, Thales C Silva, Victoria Edwards, and 1 more author

   IEEE Robotics and Automation Letters, 2025

2024

1. A Macroscopic Ensemble Modeling Approach to Collaborative Task Assignment in Dynamic Environments

   Victoria Edwards, Thales C Silva, and M Ani Hsieh

   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.

2023

1. On Collaborative Robot Teams for Environmental Monitoring: A Macroscopic Ensemble Approach

   Victoria Edwards, Thales C Silva, Bharg Mehta, and 2 more authors

   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.

2022

1. Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness

   Tahiya Salam, Victoria Edwards, and M Ani Hsieh

   IEEE Robotics and Automation Letters, 2022
2. Stochastic nonlinear ensemble modeling and control for robot team environmental monitoring

   Victoria Edwards, Thales C Silva, and M Ani Hsieh

   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.

3. Towards Understanding Underwater Weather Events in Rivers Using Autonomous Surface Vehicles

   Alice K. Li, Yue Mao, Sandeep Manjanna, and 10 more authors

   In OCEANS 2022, Hampton Roads, 2022
4. Proportional Control for Stochastic Regulation on Allocation of Multi-Robots

   Thales C Silva, Victoria Edwards, and M Ani Hsieh

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

2021

1. Swarm shedding in networks of self-propelled agents

   Jason Hindes, Victoria Edwards, Klimka Szwaykowska Kasraie, and 2 more authors

   Scientific Reports, 2021
2. Interacting Swarm Sensing and Stabilization

   Ira B Schwartz, Victoria Edwards, and Jason Hindes

   arXiv preprint arXiv:2106.01824, 2021
3. Critical transition for colliding swarms

   Jason Hindes, Victoria Edwards, M Ani Hsieh, and 1 more author

   Physical Review E, 2021

2020

1. Delay induced swarm pattern bifurcations in mixed reality experiments

   Victoria Edwards, Philip deZonia, M Ani Hsieh, and 3 more authors

   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.

2. Unstable modes and bistability in delay-coupled swarms

   Jason Hindes, Victoria Edwards, Sayomi Kamimoto, and 2 more authors

   Physical Review E, 2020
3. Stability of milling patterns in self-propelled swarms on surfaces

   Jason Hindes, Victoria Edwards, Sayomi Kamimoto, and 2 more authors

   Physical Review E, 2020
4. Establishing Reliable Robot Behavior using Capability Analysis Tables

   Victoria Edwards, Loy McGuire, and Signe Redfield

   arXiv preprint arXiv:2007.11739, 2020
5. Torus bifurcations of large-scale swarms having range dependent communication delay

   Ira B Schwartz, Victoria Edwards, Sayomi Kamimoto, and 4 more authors

   Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020

2018

1. Calibrating mixed reality for scalable multi-robot experiments

   Victoria Edwards, Peter Gaskell, and Edwin Olson

   In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2016

1. Segregation of heterogeneous robotics swarms via convex optimization

   Victoria Edwards, Paulo Rezeck, Luiz Chaimowicz, and 1 more author

   In Dynamic Systems and Control Conference, 2016