Ph.D. Dissertation Defense: Krishna Muvva
Monday, September 22, 2025
11 AM
In person: 211 Schorr Center
Zoom: https://unl.zoom.us/j/92764614781
"LEARN TO FLY: ENABLING DEEP LEARNING BASED PERCEPTION AND CONTROL IN AERIAL ROBOTICS"
Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS-degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.
Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with filter pruning achieved the lowest tracking RMSE of 0.8 meters and significantly reduced inference latency and energy consumption. Furthermore, a TD3-based co-design strategy for joint perception and motion control maintained a 2-meter RMSE while reducing filter usage by 44% com- pared to baselines, achieving robust tracking in evasive scenarios.
Learn to Localize presents a cooperative UAV-UGV localization framework that fuses GNSS data with deep learning-based detections, reducing uncertainty by over 1 meter in XY and 1.5 meters in Z. To improve visual reliability, we propose a perception failure recovery mechanism using LSTM-based recurrent networks that generalize across YOLOv8–YOLOv11. Additionally, this work introduce a 30,000-image UAV- UGV dataset, including 7,500 sequential frames suitable for training Markovian failure recovery models.
Learn to Evade proposes a probabilistic sensor fusion framework that models and mitigates sensor-specific perception failures. Ray-miss events from ToF sensors are handled with grid-based belief propagation, while CNN-based false negatives are smoothed with a Hidden Markov Model. Event camera limitations are addressed with diffusion-based belief retention. These uncertainty models are integrated into an obstacle-aware MPC framework capable of safely navigating around both static and moving UAV obstacles, even under degraded sensing conditions. The proposed system achieves accurate trajectory tracking and safe avoidance, making it one of the few efforts to treat UAVs themselves as dynamic obstacles.
Collectively, this dissertation advances the reliability, adaptability, and resource efficiency of autonomous UAVs through the synergistic design of perception and control. It provides new methods, datasets, and real-world validation to support robust aerial autonomy in real-time and safety-critical applications.
Committee:
Dr. Marilyn Wolf, Chair
Dr. Santosh Pitla, C-Chair
Dr. Stephen Scott
Dr. Benjamin Riggan
Dr. Carl Nelson