Christopher J. Lowrance
Papers
8
Total Citations
82
H-Index
5
About
Christopher J. Lowrance is a researcher at the forefront of intelligent robotic systems, with a primary focus on wireless communication and autonomous navigation. His work addresses the critical challenge of enabling robots to operate effectively in dynamic, GPS-denied environments by predicting and optimizing link quality—a key factor for reliable multi-robot coordination. Lowrance’s seminal survey, "Link Quality Estimation in Ad Hoc and Mesh Networks" (2017, 31 citations), provides a foundational taxonomy of estimation techniques, while his innovative active and incremental learning framework (2018, 14 citations) and fuzzy-based machine learning model (2016, 13 citations) advance real-time, adaptive prediction for robot networks. Beyond communication, he has pioneered deep learning approaches for autonomous control, including convolutional neural network image classification for path-following (2018, 5 citations) and reinforcement learning via deep Q-networks for navigation (2019, 4 citations). His work on direction-of-arrival estimation using radio signal strength (2016, 7 citations) further demonstrates his ability to leverage wireless signals for localization. By integrating machine learning, fuzzy logic, and multi-radio control, Lowrance’s research directly enhances robotic autonomy, reliability, and decision-making in complex, real-world scenarios.
Research Focus
Key Achievements
Top Papers
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- 3A fuzzy-based machine learning model for robot prediction of link quality13 citations · 2016
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- 6Autonomous Navigation via a Deep Q Network with One-Hot Image Encoding4 citations · 2019
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