A key necessity for the safe and autonomous flight of Unmanned Aircraft Systems (UAS) is their reliable perception of the environment, for example, to assess the safety of a landing site. For visual perception, Machine Learning (ML) provides state-of-the-art results in terms of performance, but the path to aviation certification has yet to be determined as current regulation and standard documents are not applicable to ML-based components due to their data-defined properties. However, the European Union Aviation Safety Agency (EASA) published the first usable guidance documents that take ML-specific challenges, such as data management and learning assurance, into account. In this paper, an important concept in this context is addressed, namely the Operational Design Domain (ODD) that defines the limitations under which a given ML-based system is designed to operate and function correctly. We investigated whether synthetic data can be used to complement a real-world training dataset which does not cover the whole ODD of an ML-based system component for visual object detection. The use-case in focus is the detection of humans on the ground to assess the safety of landing sites. Synthetic data are generated using the methods proposed in the EASA documents, namely augmentations, stitching and simulation environments. These data are used to augment a real-world dataset to increase ODD coverage during the training of Faster R-CNN object detection models. Our results give insights into the generation techniques and usefulness of synthetic data in the context of increasing ODD coverage. They indicate that the different types of synthetic images vary in their suitability but that augmentations seem to be particularly promising when there is not enough real-world data to cover the whole ODD. By doing so, our results contribute towards the adoption of ML technology in aviation and the reduction of data requirements for ML perception systems.

Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW.

Through combining environmental sensing by the use of LIDAR, RADAR, stereo vision and other similar ranging sensors with automatic online flight path generation, unmanned helicopters gain the capability of automatic low-level flight in the presence of obstacles. Depending on sensor and planner choice, travel times will vary considerably for missions that includes low altitude flight segments in obstacle avoidance mode. The choice of sensors and necessary processing computers will also affect equipment weight and can adversely impact helicopter performance with respect to payload capability. For the purpose of preliminary helicopter design, the relationship between mission performance of a helicopter system in obstacle avoidance flight and resulting ideal helicopter size has not been looked at in the past. Therefore a simple helicopter preliminary sizing code for helicopters below 250 kg MTOW has been implemented and tuned through collecting size and weight data from similarly sized known helicopter configurations. A set of known, flight tested or simulated sensor suites has been selected and their performance in a reference helicopter mission has been simulated using a scenario which is based on the widely used Obstacle Field Navigation benchmark. Flight durations and velocities from these simulations are then used for preliminary vehicle sizing. For a reference mission, different resulting helicopter gross weights for each of the applied environment sensor types are presented and discussed.