SIRI Projects
Purdue Projects
Intelligent Swarm Formation Control with Safety Guarantees
PI: Phil Pare
This project focuses on the safe formation control of robot swarms, emphasizing robust safety guarantees within collective behaviors. By harnessing principles from control theory, our aim is to develop algorithms that optimize swarm coordination while ensuring safety conditions are met for all time. This project will center on addressing challenges like collision avoidance, specialized formation behavior, and adaptability in dynamic environments and will integrate theoretical models into practical applications through simulation and real-world testing. Students will have the opportunity to work with real quadrotor drones, develop and write Python code, and have their ideas tested in Purdue's indoor flight test facility.
Web Service Automatic Music Transcription
PI: Yung-Hsiang Lu
Motivation: Automatic Music Transcription (AMT) converts audio recordings to music notations by using computer programs. AMT is useful in many scenarios. For example, after a musician improvises and records the music, AMT can convert the recording to a score. Another example is to help a music student practice. The student may record, and the score generated by AMT can help identify mistakes. Moreover, AMT can also be used to preserve folk music that does not have scores. Ideal AMT can detect instruments, pitches, onsets and offsets, velocities, and dynamics. Project goals: AMT tools exist but are not perfect. The state-of-the-art AMT tools often fail to recognize musical instruments, pitch, and duration, especially when multiple instruments are played simultaneously. This project aims to create a web service that offers AMT technology. The website has the following functions:
- Users can upload music in the form of MIDI (Music Instructions Digital Interface). The website synthesizes audio from MIDI.
- Users can upload audio files. The web service generates the music and sheet music.
- Users can upload transcription programs. The web service executes the programs using audio files and evaluates the programs' accuracy. The results are shown in a leaderboard.
- This project will improve an existing project that already provides some of these functions.
Technical Interests: Signal processing, machine learning, web services, music.
Cognitively aware intelligent tutoring system for psychomotor learning
PI: Neera Jain
We are looking for an undergraduate researcher to support development of a cognitively aware intelligent tutoring system for a psychomotor task. The task itself is learning how to land a quadrotor manually in a 2D quadrotor simulator module. The student will gain experience in the following areas:
- Sensor fusion
- Classification algorithms for human behavior
- System identification of human cognitive behavior models
- Synthesizing optimal control policies or algorithms that assist humans by responding to human behavior
- Improving generated formative feedback to assist in human learning
Technical Interests: This project requires experience with Python and MATLAB. While not required, prior experience with the OpenAI API and psycho-physiological/behavioral sensors (E.g. fNIRS, ECG, eyetracking) would be beneficial.
Driver-Autonomous Vehicle Interaction Research
PI: Brandon Pitts
This project focuses on integrating multimodal driver data, e.g., vehicle control, psychophysiological metrics, eye tracking, and in-vehicle behavior, to characterize driver interactions with autonomous vehicles (AVs) and provide real-time support to drivers. Students involved in this research will 1) learn about and interact with advanced driving simulation, 2) combine diverse data streams into a single platform (e.g., using Python), 3) process and analyze data, 3) develop user interfaces that respond to real-time driver data, 4) control or recommend actions for vehicles and drivers in real-time, and 5) develop functional prototypes to demonstrate project outcomes. We are seeking students with interests in networking, front-end development, physiological data analysis, and computer vision. Programming skills, particularly in Python, MATLAB, etc., and an interest in Human Factors research are highly desirable.
Technical Interests: Programming, Computer Networks, Real-time data analysis, Human Factors
Preferred Educational Background: Computer Science/Engineering, Electrical Engineering, Industrial Engineering, and/or relevant technical fields.
Penn State Projects
Decision Support System to Enhance Situational Awareness in High-Risk, Low-Time Scenarios
PI: Tahira Reid Smith
Many professionals such as fire fighters, emergency medical service workers, and police officers that often have to respond to situations that are high risk and with very little time to respond. Such work environments can challenge workers’ situational awareness, and in some cases lead to fatal errors. The recent media has highlighted several instances in which incorrect assessment of situations lead to fatal outcomes . In this project, students will contribute to answering the following broad research question: “How can we support the situational awareness of workers in high risk, low time scenarios?” Students will learn about the specific scenarios of interest and may build upon the work completed by a prior team.
Technical Interests: Students with a passion to make a difference. Programming skills are highly desirable.
Resilient Autonomous Systems
PI: Rômulo Meira-Góes
In this project, students will develop techniques for designing safe and resilient autonomous systems. Potential research tasks include (1) integrating computer vision and decision-making techniques, (2) designing and testing different control techniques both classical and learning-based techniques, and (3) developing demonstrations. Students will also get hands-on experience applying these techniques to real-world case studies, such as autonomous vehicles, and mobile robots.
Technical Interests: Students are expected to have some background in programming and will learn basic control theory, and programming in ROS.
PURL Lab Project
PI: Eric N. Johnson
The Pennsylvania State University Unmanned Aircraft Systems Research Laboratory (PURL) performs advanced unmanned aircraft systems research, including flight-testing with a variety of research systems. PURL is within Penn State College of Engineering’s Aerospace Engineering Department. The laboratory includes dedicated research vehicle systems (airplane, helicopter, multirotor, and more), a comprehensive set of simulation tools, dedicated space for indoor flight with motion capture systems, areas for aircraft maintenance/storage, an avionics workshop. The laboratory’s recognized strengths are in adaptive/reliable flight control, vision- based control, and conducting flight validation. Most projects involve teaming with government/industrial partners and/or other academic units. The laboratory’s graduates are highly sought after due to their knowledge/skills in control theory, software development, use of simulation, and familiarity with both a multi-disciplinary work environment and a safety culture.
Improving Flexibility for Robotic Assembly of Small Parts
PI: Ilya Kovalenko
Small part assemblies have sub-components that can have various orientations and require careful handling to avoid damage. Previously, we have developed a robotic system that uses machine vision to identify part orientations and deposits these parts for assembly. However, this robotic system is not very flexible to changes in part specifications and does not respond well to anomalies in the system (e.g., tangled springs or obscured parts). Human operator feedback and assistance is often required to ensure an effective assembly process. The overall objective of the proposed research is to develop a semi-automated robotic assembly system that adapts to changes in subassembly configurations and operator capabilities. As part of the research, we want to explore the integration of voice-commanded operations during the assembly process through Large Language Models (LLMs). For example, when the system detects errors by machine vision, it notifies the human operator and can be corrected through voice commands improving the process to reduce error rates and assembly time. This approach not only increases productivity but also increases accessibility to customized products.
Technical Interests: Coding.
