Curtrell Trott

Papers

Total Citations

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About

Curtrell Trott’s research lies at the intersection of robotics, computer vision, and machine learning, with a focus on enabling autonomous systems to perceive and interact with dynamic environments. His most cited work, “The 3D Position Estimation and Tracking of a Surface Vehicle Using a Mono-Camera and Machine Learning” (2022, 6 citations), introduces a passive vision-only technique that leverages machine learning to estimate a target’s 3D position from a single camera feed—a critical capability for coordinating multi-robot operations. This contribution addresses a fundamental challenge in field robotics, where accurate spatial awareness is essential for tasks like collaborative navigation and object tracking. In his earlier paper, “Human Pose Estimation in UAV-Human Workspace” (2021, 2 citations), Trott explores how unmanned aerial vehicles can safely and effectively interpret human body language in shared workspaces, advancing human-robot interaction for applications in search-and-rescue and industrial automation. Though early in his career, Trott’s work demonstrates a clear trajectory toward building robust, perception-driven systems that bridge the gap between vision algorithms and real-world robotic autonomy. His research is particularly relevant for students and engineers working on low-cost, scalable solutions for multi-agent coordination.