Amee Trivedi

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About

Amee Trivedi is a rising researcher at the intersection of robotics, wireless communications, and autonomous navigation. Her work focuses on leveraging the unique properties of millimeter wave (mmWave) signals for high-precision localization and robotic path planning. Trivedi’s most impactful contribution, "Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification" (2022, 20 citations), demonstrates how mmWave’s high angular and temporal resolution can be harnessed for robust target localization, a critical capability for next-generation autonomous systems. She extends this work in "Path Planning Under Uncertainty to Localize mmWave Sources" (2023, 4 citations), where she develops an estimation and path planning algorithm that enables mobile robots to efficiently locate wireless signals in cluttered indoor environments using directionality and Extended Kalman filtering. Trivedi also contributes to cybersecurity in smart manufacturing with "Arming IDS Researchers with a Robotic Arm Dataset" (2022, 2 citations), providing a vital resource for intrusion detection in Industry 4.0. Her research is pivotal for advancing autonomous navigation in GPS-denied environments and securing cyber-physical systems, making her a notable voice in the integration of wireless sensing and robotics.