When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety

Despite the recent developments in obstacle avoidance and other safety features, autonomous Unmanned Aerial Vehicles (UAVs) continue to face safety challenges. No previous work investigated the relationship between the behavioral uncertainty of a UAV and the unsafety of its flight. By quantifying uncertainty, it is possible to develop a predictor for unsafety, which acts as a flight supervisor. We conducted a large-scale empirical investigation of safety violations using PX4-Autopilot, an open-source UAV software platform. Our dataset of over 5,000 simulated flights, created to challenge obstacle avoidance, allowed us to explore the relation between uncertain UAV decisions and safety violations: up to 89% of unsafe UAV states exhibit significant decision uncertainty, and up to 74% of uncertain decisions lead to unsafe states. Based on these findings, we implemented Superialist (Supervising Autonomous Aerial Vehicles), a runtime uncertainty detector based on autoencoders, the state-of-the-art technology for anomaly detection. Superialist achieved high performance in detecting uncertain behaviors with up to 96% precision and 93% recall. Despite the observed performance degradation when using the same approach for predicting unsafety (up to 74% precision and 87% recall), Superialist enabled early prediction of unsafe states up to 50 seconds in advance.

COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation

Safe human-robot collaboration (HRC) has recently gained a lot of interest with the emerging Industry 5.0 paradigm. Conventional robots are being replaced with more intelligent and flexible collaborative robots (cobots). Safe and efficient collaboration between cobots and humans largely relies on the cobot's comprehensive semantic understanding of the dynamic surrounding of industrial environments. Despite the importance of semantic understanding for such applications, 3D semantic segmentation of collaborative robot workspaces lacks sufficient research and dedicated datasets. The performance limitation caused by insufficient datasets is called 'data hunger' problem. To overcome this current limitation, this work develops a new dataset specifically designed for this use case, named "COVERED", which includes point-wise annotated point clouds of a robotic cell. Lastly, we also provide a benchmark of current state-of-the-art (SOTA) algorithm performance on the dataset and demonstrate a real-time semantic segmentation of a collaborative robot workspace using a multi-LiDAR system. The promising results from using the trained Deep Networks on a real-time dynamically changing situation shows that we are on the right track. Our perception pipeline achieves 20Hz throughput with a prediction point accuracy of $>$96\% and $>$92\% mean intersection over union (mIOU) while maintaining an 8Hz throughput.