Student Research Symposium Program Portal: Submission #133

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.