EASeL

Our lab performs research in dynamical systems, robotics, and pattern recognition. The central theme of our research is to model and control complex systems comprising multiple robotic, animal, and/or virtual agents at both physical and cognitive levels. We often find ourselves studying and seeking inspiration from collective behavior across species. Applications of our work range from environmental monitoring and crowd management to machine inspection and scalable robotics.

Underwater virtual environment inspired by Great Lakes for studying human-swarm interaction

Ground robot test bed for human-swarm interaction

Virtual environment for studying crowd behavior and agent based models

Three-dimensional reconstruction of collective behavior in the wild

EEG headset for quantifying emotional and cognitive response to visual stimuli

Network reconstruction using model based and model-free methods. Applications in collective behavior and fault prediction in industrial robots

SPLASH is a hybrid platform that can fly and float for monitoring aquatic invasive species in disconnected water bodies. Designed and fabricated by Senior design team 2018

Subscope is a platform with a robotic arm monitoring aquatic invasive species. Designed and fabricated by Senior Design team 2019

Lab members

PI

Sachit Butail, cv, dissertation, google scholar, github

Current Graduate Students

Arunim Bhattacharya (PhD, ME)
Oluwatobi Kushimo (PhD, ME)
Sarah Fatima (MS, EE)
Brennan Williams (MS, ME)

Current Interns/Undergraduate Students

David Robinson
Boga Mzila-Ndlovu

Past graduates with thesis/dissertation

Elham Mohammadi Jorjafki (ME MS 2018) thesis
Kiran Maridi (ME MS 2018) thesis
Hari Boddeeti (ME MS 2019) thesis
Joseph Kempel (ME MS 2019) thesis
Kate Chwistek (ME MS 2020) thesis
Rafal Krzysiak (ME MS 2021) thesis
Sathish V (CSE, PhD, IIIT-Delhi, 2021) dissertation

Projects (current)

Integrating perception and cognition into telemanipulation for nuclear waste management (2024-2025)

This project seeks to enable seamless human-machine integration in telemanipulation by using data-driven models and control strategies within a virtual hot-cell environment used to handle nuclear waste. The specific research objectives are to utilize eye tracking, feature classification, Markov modeling, and virtual reality to enable a gaze-based human-machine interface that can be used to infer human intent and perform simple telemanipulation actions with eye tracking. Research supported by Argonne National Lab.

The role of stress in human crowd dynamics during emergency situations (2023-2026)

This project, in collaboration with VirginiaTech, University of Virginia, and TU Delft, seeks to understand how stress spreads among evacuating groups. Experiments will be performed in an instrumented environment with small and large groups to isolate causal relationships among individuals along multiple postural and physiological measures. Mathematical models drawing from experimental data analysis will be validated with further experiments and pushed to create extreme evacuation situations that are otherwise unfeasible to test in a lab. Research supported by National Science Foundation

Cues and actions for efficient nonverbal human-robot communication (2020-2022)

This project develops novel methods to advance human-robot intelligence through a series of experimental studies and rigorous mathematical analysis. The experiments involve tasks designed to exploit the strengths of robots and humans; robots are able to repetitively explore a large environment and humans have better awareness of the situation and domain expertise. The experimental tasks are inspired by the difficult problem of monitoring the vast number of invasive aquatic species threatening the Great Lakes region. The mathematical analysis is aimed at discovering effective robot actions in response to changes in human cognitive load, and efficient nonverbal interaction strategies between humans and robots. Research supported by National Science Foundation

Projects (Past)

Human-assisted robotic sampling of aquatic microorganisms (2021-2022)

This project will focus on testing and improving the design of a robotic device for sampling aquatic microorganisms. One such organism that we will focus on is the spiny water flea (Bythotrephes longimanus), an invasive microorganism notorious for its ecological and economic harm to the Great Lakes system. The sampling device has been designed to collect multiple samples at varying depths without cross contamination and tested in local water bodies. Once field-tested, a remote controlled robotic boat will be designed to deploy this device in nearshore regions of Lake Michigan. Research supported by the Illinois-Indiana Sea Grant.

Agent-based Modeling Toward Effective Testing and Contact-tracing During the COVID-19 Pandemic (2020-2021)

This project, carried out in collaboration with Dynamical Systems Laboratory in New York University, focuses on developing agent-based models to address social and mobility constraints as we respond to COVID19. The model will afford the simulation of critical what-if scenarios and will include the evaluation of different testing policies and mitigation actions, thereby constituting a valuable support to policy makers involved in the containment and eradication of the epidemic. Research supported by National Science Foundation

Multi-robot platform for environmental monitoring (2020-2021)

This project aims to enable hardware and virtual swarm robotic platforms for collaborative environmental monitoring. The robotic platform will consist of multiple ground robots that can seamlessly collaborate with a human through visual cues for monitoring structured environments. The virtual platform will simulate multiple UAVs that can be controlled by a human operator for monitoring unstructured environments. Research supported by NASA, Illinois Space Grant Consortium

Causal Relationships Underlying the Collective Dynamic Behavior of Swarms (2016-2019)

Living in groups affords several benefits for animals such as better feeding opportunities and reduced predation risks. In both instances-foraging and predator avoidance-critical information is transmitted nonverbally throughout the group, at different time scales. This project, carried out in collaboration with Dynamical Systems Laboratory, New York University, seeks to demonstrate that an information-theoretic approach can be used to measure social animal behavior. The research objective is to establish a rigorous model-free framework to study causal relationships in animal interactions validated by a series of hypothesis-driven experiments on zebra fish to emphasize unidirectional information transfer. Research supported by National Science Foundation through a sub-award from New York University, PI, Maurizio Porfiri

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