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. Motivated students interested in pursuing a PhD in dynamical systems should email their resume and a statement of purpose to sbutail@niu.edu
Lab members
PI
Sachit Butail, cv, dissertation, google scholar, github
Current Graduate Students
Arunim Bhattacharya
Di'Quan Ishmon
Current Interns/Undergraduate Students
Abdullah Thahab
Joshua Schrank
Soka Suliman
Nicholas Ruiz
Past graduates
Projects (current)
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.
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
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
Projects (Past)
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