Resilient Navigation

Resilient navigation focuses on developing systems capable of reliable and safe navigation even under challenging conditions, such as dynamic obstacles, sensor failures, and extreme weather. Current research emphasizes robust state estimation using multi-modal sensor fusion and Bayesian approaches, incorporating learned models like LSTM networks and employing techniques like learning from hallucination to generate diverse training data for improved performance. These advancements are crucial for enhancing the safety and reliability of autonomous vehicles, robots, and other systems operating in unpredictable environments, with applications ranging from autonomous racing to underwater exploration.