Kevin Coleman

Papers

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About

Kevin Coleman is a researcher in robotics and control systems, with a focus on nonlinear estimation and disturbance rejection for autonomous vehicles. His most cited work, "Invariant-EKF Design for a Unicycle Robot under Linear Disturbances" (2020), introduces a novel application of invariant extended Kalman filters to address the challenge of estimating both state and unknown disturbances in unicycle robots. By leveraging the system's symmetry properties, Coleman demonstrates how position measurements alone can be used to simultaneously estimate vehicle state and disturbance dynamics generated by linear time-invariant systems. This contribution bridges theoretical invariance principles with practical robotics, offering a robust framework for navigation under uncertain environmental conditions. While his citation count is still growing, Coleman's work represents an important step toward more resilient autonomous systems, particularly in scenarios where disturbances are structured but unknown. His research holds promise for applications in mobile robotics, autonomous navigation, and real-time state estimation under adversarial or uncertain conditions.