Deciding where to go next is a challenging task for humans. However, for robots in unknown environments, this becomes even more demanding. In planetary explorations, the robots are continuously challenged with the task of exploring novel areas, yet so far, humans decide for the robots where to go. Even then, prioritizing the next target based on previous knowledge is complex. In our proposed work, the robot utilizes data about its surroundings from drone or satellite images. Alternatively, a volumetric representation can be reduced to form a suitable input. From the input, tiles are selected and embedded by different autoencoder variants. The robot can select the most promising next exploration goal through the distance in the embedding to the previous samples. In this work, a variational autoencoder, a Wasserstein autoencoder, and a spherical autoencoder are evaluated against each other. The latter two variants yield a high information gain when evaluated on satellite data from the Netherlands. Additionally, the framework was employed on data from an analog mission in the Tabernas desert. Through the framework, the robots get an understanding of which goals yield the most information gain and, therefore, can quickly improve their knowledge about their surroundings.

Exploring unknown environments, such as caves or planetary surfaces, requires a quick understanding of the surroundings. Beforehand, only aerial footage from satellites or images from previous missions might be available. The proposed ensemble based anomaly detection framework utilizes previously gained knowledge and incorporates it with insights gained during the mission. The modular system consists of different networks which are combined to determine anomalies in the current surroundings. By utilizing data from other missions, simulations or aerial photos, a precise anomaly detection can be achieved at the start of a mission. The system can further be improved by training new networks during the mission, which can be incorporated into the ensemble at runtime. This allows for synchronous execution of mission and training of models on a base station. The proposed system is tested and evaluated on an ANYmal C walking robot in different scenarios, however the approach is applicable for different kinds of mobile robots. The results show a clear improvement of ensembles compared to individual networks, while keeping a small memory footprint and low inference time on the mobile system.