Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Aashman Gupta, Sophomore
Lead Presenter's Name
Aashman Gupta
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Cagri Kilic
Abstract
This project evaluates the feasibility of sustained onboard AI autonomy for a solar-powered Mars rover by directly linking solar energy availability to achievable compute performance. While Mars solar irradiance and edge computing performance have been studied independently, no unified framework currently couples surface power generation to autonomy throughput in an experimentally validated manner. The project will begin with a simulation of solar power generation for a 1 m² rover-mounted array across a Martian sol, accounting for seasonal variation, dust opacity, and array configuration (fixed versus sun-tracking). The resulting power profile will then be coupled to representative compute platforms running autonomy workloads spanning high-, moderate-, and low-compute demand classes, including object detection, visual odometry, and terrain classification. Unlike purely theoretical studies, this work emphasizes hardware testing. Simulated power availability will be replicated in the laboratory using programmable benchtop power supplies to measure real computing throughput, duty cycle, and sustained power draw. The outcome will be a validated feasibility framework that future researchers can use as a baseline reference when designing AI-enabled Mars rover systems.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Artificial Intelligence and Robotics Commons, Space Vehicles Commons, Systems Engineering and Multidisciplinary Design Optimization Commons
Feasibility of High-Throughput Onboard AI for Mars Rovers Under Solar Constraints
This project evaluates the feasibility of sustained onboard AI autonomy for a solar-powered Mars rover by directly linking solar energy availability to achievable compute performance. While Mars solar irradiance and edge computing performance have been studied independently, no unified framework currently couples surface power generation to autonomy throughput in an experimentally validated manner. The project will begin with a simulation of solar power generation for a 1 m² rover-mounted array across a Martian sol, accounting for seasonal variation, dust opacity, and array configuration (fixed versus sun-tracking). The resulting power profile will then be coupled to representative compute platforms running autonomy workloads spanning high-, moderate-, and low-compute demand classes, including object detection, visual odometry, and terrain classification. Unlike purely theoretical studies, this work emphasizes hardware testing. Simulated power availability will be replicated in the laboratory using programmable benchtop power supplies to measure real computing throughput, duty cycle, and sustained power draw. The outcome will be a validated feasibility framework that future researchers can use as a baseline reference when designing AI-enabled Mars rover systems.