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)
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

Projects (Past)

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

Intellectual Merit: A robust measure of cognitive load using EEG during an identification task has been identified; a rich underwater virtual environment with realistic fish movement and AUV dynamics was developed; movement correlates of prior knowledge, cognitive load, and situational awareness have been evaluated in terms of their use in adaptive control during search and rescue operations; the hypothesis that robots can communicate complex information nonverbally to humans in a virtual dynamic environment was tested. Broader Impacts: Models of collective behavior for five common species of fish found in Lake Michigan have been developed; interactive activities on human-swarm robotics and invasive fish species were organized for a teen STEM Café and STEM Fest. Products: 5 journal publications, and 1 conference publication.

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.

Intellectual Merit: the award led to the design, development, and testing of a new underwater sampling system that can sample up to six commercial cod-ends. The design went through multiple iterations to address cross contamination between cod ends and automation. Field tests were performed in local lakes to compare the efficacy of sampling. Broader Impacts: This award has helped train a graduate student and an undergraduate student in research. Products: An automatic sampling system (publication ongoing).

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.

Intellectual Merit: the award helped i) develop and implement a new agent-based modeling framework to inform the decision-making and policy about testing and active surveillance policies during the COVID-19 outbreak, ii) utilize the framework to answer several "what if" questions on efficacy of vaccines, role of location mobility, comparing the response of different cities, and response to different strains, and iii) address questions related to the possibility of exposure to COVID-19 during emergency evacuations and in the identification of drivers of rise and fall in infections during waves of infections. Broader Impacts: This research has enabled new open-source agent-based modeling platform, and train two graduate students. Products: 8 publications.

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

Intellectual Merit: the award enabled the design, development and testing of a multi-target tracking system that can track multiple ground robots in a 9x18 m indoor environment. The tracking system was used in an experimental study to investigate human teleoperation and search under different knowledge constraints Broader Impacts: This award has helped train an undergraduate student in research. Products: One journal publication.

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

Intellectual Merit: the sub-award has focused on i) data-driven modeling and investigation of leader-follower relationships in zebrafish; and ii) information-theoretic validation and analysis of the effect of psychoactive compounds, and robotic replicas on zebrafish behavior. Broader Impacts: This research has enabled new objective measures of social animal behavior. This specific sub-award has helped train two undergraduate students and one Masters student in research. Products: 11 publications.

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