Tyrell Lewis
Papers
3
Total Citations
35
H-Index
3
About
Tyrell Lewis is a researcher at the forefront of autonomous environmental sensing and robotics, specializing in the detection and localization of hazardous pollutant plumes using unmanned vehicles. His work addresses critical challenges in chemical, biological, radiological, and nuclear (CBRN) threat containment, where timely source identification is essential for public safety. Lewis’s most impactful contribution is his comprehensive review on plume source detection using unmanned vehicles for environmental sensing, which has garnered 27 citations and serves as a foundational resource for researchers in the field. He further advances this domain by developing configurable simulation strategies for testing plume source localization algorithms with autonomous multisensor mobile robots, enabling robust validation of detection methods in complex atmospheric dispersion scenarios. Additionally, Lewis explores the intersection of artificial intelligence and autonomous navigation, as demonstrated in his work on deep reinforcement learning (DRL) for unmanned ground vehicles. His virtual testing and policy deployment framework for Ackermann-steered robots provides a scalable approach to training AI-driven navigation systems in unfamiliar environments. With a growing citation impact and a focus on bridging simulation and real-world deployment, Lewis is shaping the future of autonomous systems for environmental monitoring and hazard response.
Research Focus
Key Achievements
Top Papers
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