Student Research Symposium Program Portal: Submission #133
Submission information
Submission Number: 133
Submission ID: 8416
Submission UUID: ebb69ecf-e385-45d8-860d-15aa1415a2a4
Submission URI: /student-research/symposium/research-symposium-program-portal
Submission Update: /student-research/symposium/research-symposium-program-portal?token=6fNywzpQji9HIDbaN3FAgtImrrxeP76VIlij2lO1yvM
Created: Wed, 03/05/2025 - 10:28 AM
Completed: Wed, 03/05/2025 - 10:31 AM
Changed: Mon, 04/14/2025 - 12:42 PM
Remote IP address: 146.201.10.11
Submitted by: Anonymous
Language: English
Is draft: No
Webform: Research Symposium Program Portal WF
Submitted to: Student Research Symposium Program Portal
| First Name | Liliana |
|---|---|
| Last Name | Carlson |
| Pronouns | |
| FSU Student Email | lrc24b@fsu.edu |
| Student Photo |
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| Major(s) | Biomedical Engineering |
| Student Bio | Liliana Carlson is a senior in High School at Ohana Institute from Santa Rosa Beach, FL, with a passion for robotics, engineering, and physics. She has extensive experience in competitive robotics, serving as a FIRST Robotics Competition (FRC) team captain and mentor and leading FTC (FIRST Tech Challenge) projects focused on autonomous programming and drivetrain optimization. Liliana’s expertise includes probabilistic decision-making models, odometry tuning, and swerve drive calibration. In recognition of her leadership, technical skills, and dedication to STEM, she was named a 2024 FRC Dean’s List Finalist. Beyond robotics, Liliana has a strong academic foundation in physics and mathematics, pursuing advanced coursework in calculus and engineering. She is passionate about pushing the boundaries of automation and robotics and applying innovative problem-solving approaches to real-world challenges. |
| Poster Title | Maximizing FRC Autonomous Efficiency with Probabilistic Decision-Making Models |
| Abstract | This paper explores the application of probabilistic decision-making models to enhance the efficiency and reliability of autonomous operation in FIRST Robotics Competition (FRC) robots. Traditional deterministic autonomous programs often struggle with variability in sensor data, drivetrain inconsistencies, and unforeseen obstacles, leading to suboptimal performance. Robots can dynamically adjust their actions based on real-time conditions by integrating probabilistic models, optimizing movement strategies, and increasing scoring efficiency. Drawing from research in autonomous vehicles and Bayesian networks, this study examines how probabilistic frameworks improve adaptability and decision-making in uncertain environments. Key findings demonstrate that probabilistic approaches enhance FRC autonomous strategies by enabling real-time adjustments, reducing error rates, and maximizing competitive performance. The results suggest that future FRC teams can benefit from incorporating probabilistic modeling techniques to develop more robust and flexible autonomous routines. |
| Research Mentor Name | Milinda Jay Stephenson, Ph.D |
| Research Mentor's College (or High School) | Florida State University Panama City |
| Research Mentor's Department (or Subject) | English |
| Research Mentor's Email | mjay@fsu.edu |
| Additional Research Mentor(s) | |
| Co-presenter(s) | |
| Keywords | Autonomous, Decision-Making, Robotics, Probabilistic |
| Poster Session/Number | C- 2 R - 5 |
| Work | Complete |
| Presentation Modality | Face to Face Poster session |
| Poster PDF | |
| Poster Thumbnail |
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| I will be printing my poster | No |
| Year | 2025 |
| Annual description | 5th annual Undergraduate Research Symposium, April 17, 2025 |
| Update URL | https://pc.fsu.edu/student-research/symposium/research-symposium-program-portal?element_parents=elements/poster_thumbnail&ajax_form=1&_wrapper_format=drupal_ajax&token=6fNywzpQji9HIDbaN3FAgtImrrxeP76VIlij2lO1yvM |