Automatic helicopter flight in uncertain surroundings remains a challenging task due to sudden changes in environment, requiring fast response to guarantee safe and collision-free guidance. Increasing numbers of small unmanned aerial vehicles, which are not covered by air traffic control, pose a potential threat to rotorcraft operating in lower airspace. In order to provide collision avoidance in this scenario, the capability of reacting immediately to appearing obstacles and guiding the rotorcraft along feasible evasive trajectories is required. This paper presents an approach to short-term collision avoidance based on model predictive techniques. The proposed method, originally developed for automotive applications, finds optimal control inputs by predicting a set of trajectories utilizing a model resembling the helicopter dynamics. Compared to model predictive control no iterative optimization is adopted, resulting in deterministic execution time. The proposed method is evaluated by closed-loop simulations with a non-linear helicopter model. Additional hardware-in-the-loop simulations are conducted to examine the real-time capability of the approach.
Low-level helicopter operations are typical military missions, for example in forward air medical evacuation missions. Up to now, these types of missions are either carried out in manual flight or with a rather conservative automation on rare platforms. For the latter, the only possible intervention method for the pilot is to manually take over the helicopter. It can be envisaged that future platforms will provide more dynamic low-level automation capability. At the same time, it is very likely that the crew will have to fulfill other tasks like managing unmanned systems. This will fully decouple the pilot from the flight control task for periods of time and reduce the ability to quickly take over the helicopter under threat conditions. Therefore, automation functions need to be available to avoid threats and alter the planned path on short notice which further reduces a comprehensive system understanding and the ability for the pilot to intervene. This paper presents a multimodal cueing concept for human-machine shared control under automatic trajectory following low-level operation, which is being developed within the project "US-German Advanced Technologies for Rotorcraft Project Agreement". The system enables the helicopter to follow planned low-level paths and provides the pilot with tactile, auditive and visual cues. The trajectory following function is complemented with a collision avoidance method to create a "carefree" automation. Intermediate results for the multimodal cueing and interaction concept are presented, which were gathered from validation sessions and workshops with expert pilots at DLR's Air Vehicle Simulator (AVES).
Within the FaUSt (Fast Unmanned Scout) project, an existing UAV with intermeshing rotors was enhanced by two electric pusher propellers to extend the speed range for future manned-unmanned teaming research. The modeling of such a compound UAV has not yet been covered in the literature. To extract models for simulation and flight control design, dedicated flight tests were performed using a multi-axis binary noise signal for system identification with activated base flight control system. Two different system identification methods are applied to estimate bare-airframe models of the modified UAV at hover condition: one in the time domain, the other in the frequency domain. The time domain method can inherently be applied to correlated closed-loop data, while input correlations have to be accounted for when using the frequency domain method. The resulting models are analyzed in both time and frequency domain and the model eigenvalues and modes are compared in detail. The results indicate that the time-domain model matches low frequency modes of the vehicle more accurately, but both models show good model performance.
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