Ensemble Based Anomaly Detection for Legged Robots to Explore Unknown Environments

Abstract

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.