A Neural Network Based Hazard Detection Algorithm for Planetary Landing

In this paper an hazard detection system algorithm, based on neural networks, able to reconstruct an hazard map of the landing area from a single image during the descent, is presented. During algorithms development it is very difficult to consider in advance all the types of morphological structures that can be encountered; the neural network approach allows to obtain a flexible system, able to operate also in conditions not explicitly considered during the project. Different networks analyze images at different scales, extracting from graphical information a certain number of indexes ascribable to physical properties, such as shadows, surface roughness and slopes. Then, these values are fused in a unique hazard map. Different network training methods are investigated: the use of both artificial and real images is compared. Results for different scenarios in a lunar landing case are shown and discussed, in order to highlight the effectiveness of the proposed system. Sensitivity to environmental parameters, such as light conditions, trajectory inclination and camera attitude is investigated. Finally, possible future improvements are suggested.