Date of Award
12-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering
Committee Chair/Advisor
Dr. Fatemeh Afghah
Committee Member
Dr. Yongqiang Wang
Committee Member
Dr. Yongkai Wu
Abstract
Wildfires have increased in frequency & severity over the past several decades, demanding faster, safer, & more continuous situational awareness than traditional wildfire management methods that rely on satellite, manned aircraft, & ground-sensor pipelines can reliably provide. This thesis designs, develops, evaluates & presents a fully autonomous UAV agent trained in a high-fidelity digital twin environment for wildfire detection & monitoring. The FIRETWIN digital twin environment used in this thesis incorporates the Cosys-AirSim plugin in Unreal Engine 5.3 and renders 3D terrain, vegetation, and physics-based fire & smoke modeling, enabling reproducible training & evaluation in a realistic environment. The proposed model architecture combines Proximal Policy Optimization (PPO) modeled reinforcement learning with a frozen pretrained vision–language model to deliver semantic-based reward shaping & directional guidance from dual onboard cameras, using a segmented top-down view for immediate directional navigation guidance & an angled horizon view for longer-range scene cues.
This final dual-camera, VLM-guided agent is compared against a selection of five ablated model variants as benchmarks, including a baseline PPO agent without VLM-guided reward shaping, and evaluated on metrics including the total evaluation episode reward, the percentage of time the UAV detects wildfire in its field-of-view, and the time-to-detection. These six models are evaluated under five different scenarios to evaluate their ability to detect & localize wildfire in the immediate field-of-view, conduct short-range & long-range search & exploration, and respond to adverse environmental conditions such as high wind & low lighting. Overall, the simulation results presented in this thesis validate the core objective of this research, demonstrating that semantic reward shaping from VLM guidance has the potential to significantly improve autonomous, AI-enabled wildfire search & monitoring, developing a robust & efficient trained model with the potential for future real-world implementation and field deployment.
Recommended Citation
Webb, Christopher A., "VLM-Guided Reinforcement Learning in a Digital Twin Environment for Autonomous UAV-Led Wildfire Monitoring & Response" (2025). All Theses. 4632.
https://open.clemson.edu/all_theses/4632
Included in
Controls and Control Theory Commons, Navigation, Guidance, Control and Dynamics Commons, Robotics Commons, Systems and Communications Commons