Hazard Detection

Hazard detection research focuses on automatically identifying and classifying potential dangers in diverse environments, from planetary surfaces to construction sites and roadways. Current efforts leverage advanced machine learning techniques, including deep learning models like convolutional neural networks and multimodal large language models, along with stochastic algorithms for handling uncertainty in sensor data. These methods aim to improve safety and efficiency in autonomous systems, robotic missions, and human-centered applications by providing real-time hazard assessment and avoidance capabilities. The development of robust, reliable hazard detection systems is crucial for advancing autonomous navigation, ensuring mission success in challenging environments, and enhancing workplace safety.