Autonomous Unmanned Vehicles (AUVs): AI-enhanced Control and Navigation
Professor: Jinjun Shan
Contact Info: jjshan@yorku.ca
Lab Website: https://lassonde.yorku.ca/users/jjshan
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: Autonomous Unmanned Vehicles (AUVs) are systems that are capable of maneuvering in the air, on the ground, above or under the water. There are a number of potential applications for AUVs in civilian, military and security areas, for example, defense patrol duties, agricultural activities, forest fire monitoring and control, grid monitoring, boarder control, search, surveillance, and rescue. AUVs have great potential benefits to Canada for numerous reasons; many of which are connected with our large uninhabited land, the largest area of forests in the world, and the longest international border in the world.
This project is to develop AI-enhanced control and navigation algorithms for autonomous unmanned vehicles (drones, mobile robots, and self-driving cars) for indoor surveillance and monitoring applications.
Duties and Responsibilities: The successful student will be working with graduate students and research fellows on (a) programming; (b) hardware development and implementation. Through these activities, the student will gain experience in control and navigation system design, hardware and software development, etc. These experiences will be very helpful for the student’s future study and work.
Desired Technical Skills:
1. Good programming skills, MATLAB, C, and Linux
2. Enrolled in engineering degree
3. Familiar with ROS
4. Team player
Desired Course(s): Senior engineering students.
Other Desired Qualifications: none
Spacecraft Testbeds and On-Orbit Robotics
Professor: Michael Bazzocchi
Contact Info: mbazz@yorku.ca
Lab Website: https://www.astrolabresearch.com/
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA)
Open Positions: 1
Project Description : Currently, the Astronautics and Robotics Laboratory (ASTRO Lab) is seeking students for research projects in spacecraft dynamics and control emulation using robotic testbeds under the supervision of Prof. Michael Bazzocchi. Students will be engaged in projects related to robotic simulation, experimental emulation, and fabrication of systems related to spacecraft dynamics and control, including interaction dynamics for tasks such as space debris capture and removal. For more information about the lab and associated projects, please refer to https://www.astrolabresearch.com/
Duties and Responsibilities: Specific student project details are determined based on the students’ expertise as well as their research and professional goals. Generally, students are expected to formulate clear research objectives, perform a literature review to gain a working understanding in their research areas, construct a clear methodology and/or test procedure for their research and experiments, conduct simulations, fabricate prototypes, and conduct experiments, and document their research in formal technical reports or manuscripts for publication.
Desired technical skills: Students with some experience in the following areas are especially encouraged to apply: orbital mechanics, spacecraft dynamics and control, robotics. Applicants should have proficiency in technical writing and experience in computer programming, CAD experience is an asset.
Desired Course(s): Space or Robotics Engineering (or similar).
Other Desired Qualifications: Applicants with interests in long-term research projects or in other projects within the ASTRO Lab research areas are also encouraged to apply.
Assistive Robotics for Aging in Place
Professor: Michael Bazzocchi
Contact Info: mbazz@yorku.ca
Lab Website: https://www.astrolabresearch.com/
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA)
Open Positions: 1
Project Description: Currently, the Astronautics and Robotics Laboratory (ASTRO Lab) is seeking students for research projects in the data collection and development of assistive technologies for aging adults under the supervision of Prof. Michael Bazzocchi. Students will be engaged in projects related to data processing, motion capture, biomechanics, ergonomics, computer vision, assistive technologies, assistive robotics, device fabrication, and participant trials. For more information about the lab and associated projects, please refer to https://www.astrolabresearch.com/
Duties and Responsibilities: Specific student project details are determined based on the students’ expertise as well as their research and professional goals. Generally, students are expected to formulate clear research objectives, perform a literature review to gain a working understanding in their research areas, construct a clear methodology and/or test procedure for their research and experiments, conduct simulations, fabricate prototypes, and conduct experiments, and document their research in formal technical reports or manuscripts for publication.
Desired Technical Skills: Students with some experience in the following areas are especially encouraged to apply: biomechanics, computer vision, robotics, aging, and assistive technologies. Applicants should have proficiency in technical writing and experience in computer programming, CAD experience is an asset.
Desired Course(s): Biomechanical or Robotics Engineering (or similar).
Other Desired Qualifications: Applicants with interests in long-term research projects or in other projects within the ASTRO Lab research areas are also encouraged to apply.
Simulation Design for Space Situational Awareness
Professor: Regina Lee
Contact Info: reginal@yorku.ca
Lab Website: https://nanosatellite.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: Space Situational Awareness (SSA) refers to the development and application of technologies for the detection, tracking, characterization, and prediction of objects in space, with the goal of ensuring the safety, security, and long-term sustainability of space operations.
In our laboratory, we develop advanced imaging technologies and data-driven algorithms to support SSA applications. Our work contributes to several major initiatives, including the upcoming Redwing mission, a series of stratospheric balloon campaigns, and the UPMSat mission led by our partners in Spain. These projects provide real-world platforms for validating novel sensing concepts and operational SSA methodologies.
During the summer term, students will work closely with industry partners—including Kepler Space, MDA, and Magellan Aerospace—to enhance high-fidelity space environment simulations. These simulations will support the design, testing, and optimization of SSA algorithms for object detection, tracking, and risk assessment in increasingly congested orbital regimes.
This project offers hands-on experience at the intersection of space systems engineering, sensor technology, and AI-enabled data analytics, preparing students to contribute meaningfully to the future of safe and sustainable space operations.
Duties and Responsibilities: Student(s) will be improving the existing simulator to include various aspects of Space environment and demonstrate close-proximity operations
Desired Technical Skills: STK preferred, MATLAB, Python
Desired Course(s): N/A
Other Desired Qualifications: N/A
Exploring Thermospheric Disturbance Patterns via Low Earth Orbiters: The detection of acoustic gravity waves generated by the supersonic speed of the solar terminator.
Professor: Spiros Pagiatakis
Contact Info: spiros@yorku.ca
Lab Website: https://www.yorku.ca/spiros/spiros.html
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellite missions have significantly advanced our understanding of the Earth system by providing time variability estimates of the gravity field, valuable insights into thermospheric density, and space weather, among many others. These missions cary on their payload 3D electrostatic accelerometers that measure non-gravitational accelerations, which include atmospheric drag, Solar Radiation Pressure, Earth Radiation Pressure, and Thermal Radiation Pressure, among others.
In early 1970s, researchers theorized that traveling ionospheric disturbances (TID) could be generated during a solar eclipse. Later, they predicted that the supersonic motion of the solar terminator may generate wave-like atmospheric disturbances throughout the Earth’s upper atmosphere in form of acoustic gravity waves (AGWs); this was based on evidence that the propagation of radio waves was substantially affected around the solar terminator. Low Earth Orbit (LEO) satellites transition abruptly through the solar terminator before and after their transition to the Earth’s umbra and are expected to be disturbed by the AGWs.
In a recent study published in the Geophysical Research Letters, (Tzamali & Pagiatakis, 2024) we showed that characteristic GRACE and GRACE-FO disturbances could be attributed to acoustic gravity waves (AGWs) around the solar terminator. In the proposed study we aim to to develop sophisticated data analyses methodologies applied to many years of accelerometer measurements to verify the detectability of AGWs and investigate their nature and characteristics. Further, it is envisaged that this study will shed light into the physics of other phenomena that are holistically described as “space weather” that affect the LEO missions destined to study the Earth system at large.
Duties and Responsibilities: Explore and understand the design and payload of the relevant missions; collect datasets from NASA/JPL and ESA databases; perform specialized analysis of the data through design and/or improvement of existing models and algorithms; interpret results and define strategies for future advancements in the field
Desired Technical Skills: Knowledge and space missions and space environment; space hardware and payload design; orbit dynamics; dynamic/control systems; data analytics.
Desired Course(s): Space Engineering: Dynamics of Space Vehicles, Space Hardware, Time series and spectral analysis, Control systems
Other Desired Qualifications: Scientific Programming
Detection and Accurate Modelling of Thruster Activations in Low Earth Orbiters for Mission Design and Science Operations: The GRACE Mission Case
Professor: Spiros Pagiatakis
Contact Info: spiros@yorku.ca
Lab Website: https://www.yorku.ca/spiros/spiros.html
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description:
GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellite missions have significantly advanced our understanding of the Earth system by providing time variability estimates of the gravity field, valuable insights into thermospheric density, and space weather. These missions carry on their payload 3D high precision electrostatic accelerometers that measure non-gravitational accelerations, which include atmospheric drag, Solar Radiation Pressure, Earth Radiation Pressure, and Thermal Radiation Pressure, among others. These non-gravitational accelerations, measured on orbit and studied via data-driven models, provide valuable information of the Earth’s upper atmosphere and space weather.
This project aims to investigate the detection of thruster activations in Low Earth Orbiters, why they occur, and why their accurate modelling is critical for both mission design and science operation.
Areas of study:
• Why thrusters are activated (drag compensation, attitude control, collision avoidance manoeuvres, orbit maintenance especially during high solar activity).
• How thruster usage is predicted during the design phase to size propellant tanks, define mission lifetime, and ensure collision avoidance capability.
• How thruster firings appear in accelerometer measurements and contaminate signals: The GRACE mission and upcoming Grace-like missions in the next decade.
• Modelling and removing thruster-induced accelerations to recover atmospheric drag solar radiation pressure and gravity-related signals.
• Examine the physical phenomena that induce thruster activations, such as enhanced drag during geomagnetic storms and variability during equatorial crossings.
Duties and Responsibilities: Explore and understand the design and payload of the relevant missions; collect datasets from NASA/JPL and ESA databases; perform specialized analysis of the data through design and/or improvement of existing models and algorithms; interpret results and define strategies for future advancements in the field
Desired Technical Skills: Knowledge and space missions and space environment; space hardware and payload design; orbit dynamics; dynamic/control systems; data analytics.
Desired Course(s): Space Engineering: Dynamics of Space Vehicles, Space Hardware, Time series and spectral analysis, Control systems
Other Desired Qualifications: Scientific Programming
Application of AI to improve GPS-based smartphone positioning
Professor: Sunil Bisnath
Contact Info: sbisnath@yorku.ca
Lab Website: gnsslab.lassonde.yorku.ca
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Low-cost GPS, and now broadly GNSS (Global Navigation Satellite System), chips in smartphones are increasing in capabilities. Coupled with users being able to access raw measurements from some models, the York GNSS Lab has been applying high-performance measurement processing techniques to improve positioning accuracy from 10s of metres to the decimetre level. This solution can also be integrated with measurements from other smartphone sensors, such as inertial measurement unit (IMU). Applications for such performance include navigation, augmented reality apps, gaming, etc. Research areas of interest include: precision of raw satellite ranging measurements, GNSS/IMU integration, optimal estimation, performance of cellphone antennas, availability of measurements in obstructed, urban environments, tuning of processing methods for such measurements, and solution testing. In recent years, AI (specifically machine learning) approaches have been considered to mitigate measurement errors or replace standard industry position estimation approaches. As part of a Google-sponsored research initiative, this project will support continued investigations into AI-based GNSS measurement error identification and mitigation, including AI model development, GNSS feature engineering, signal environmental classification and smartphone positioning improvements.
Duties and Responsibilities: Working with a team of graduate students in the collection, analysis and processing of smartphone GNSS data, and the tuning of GNSS measurement processing algorithms. This work is globally leading-edge, so there is the high likelihood of conference or journal paper preparation experience as well.
Desired Technical Skills: Knowledge of GNSS specifically, optimal estimation, and Geomatics in general, would be very helpful, but not absolutely required. As well as the scientific method, machine learning, and data analysis skills. Coding ability in Python and C/C++ will be very helpful.
Desired Course(s): Current BEng or BSc student in Geomatics Engineering, Space Engineering, Civil Engineering, Mechanical Engineering, Software Engineering, Electrical Engineering, Computer Science or a related field. Strong math and coding backgrounds are significant assets. Knowledge of GNSS and/or optimal estimation are bonuses.
Other Desired Qualifications: A quick learner. Focused and organized. Hard working. Curious. Strong communications skills. Proven ability to work in a group and individually. Highly motivated. Can work independently. Interested in graduate school.