Applying unmanned aerial vehicles (UAV) has benefits for many different use-cases. Existing implementations of ground control stations (GCS) to manage UAVs in such scenarios already provide some support for the operation of multi-unit systems, i.e., ensembles. However, since they are usually designed for the operation of only one copter at once, this is often not sufficient to react quickly in dangerous situations, e.g., search and rescue scenarios. To address this problem, we propose an approach for easy observation and control of complete autonomous UAV ensembles: The Intention of our approach is to greatly reduce the number of personnel required for the operation of an UAV ensemble. Thereby, we generate the possibility for rapid intervention in potentially dangerous situations in order to prevent damage to the UAVs and the environment. In this paper, we present a software architecture for this safety-critical multi UAV ground control station including a fully implemented prototype which we also tested in a realistic environment.
Unmanned aerial vehicles (UAV) can support various scenarios, e.g., serve as measuring instruments for climate research, help rescue forces in disaster scenarios or autonomously inspect critical infrastructure. To accomplish their respective tasks, UAVs are often equipped with different sensors and in some scenarios used in ensembles to work together cooperatively. In this paper, we present the Block Definition Language (BDL) for modeling and executing UAV missions. The BDL supports both the use of exchangeable sensors and appropriate coordination mechanisms for robot ensembles. We demonstrate our plugin mechanism in a proof of concept using the BDL in conjunction with the robotic middleware ROS for hardware control and robot synchronization.
The inspection of large structures is increasingly carried out with the help of Unmanned Aerial Vehicles (UAVs). When navigating relative to the structure, multiple data sources can be used to determine the position of the UAV. Examples include track data from an installed camera and sensor data from the orientation sensors of the UAV. This paper deals with the fusion of this data and its use for navigation alongside the structure. For the sensor fusion, a concept is developed using a Kalman filter and evaluated simulatively in a prototype. The calculated position data are also fed into a vector flight control system, which dynamically calculates and flies a trajectory along the component using the potential field method. This is done taking into account obstacles detected by the onboard sensors of the UAV. The established concept is then implemented with the Robot Operating System (ROS) and evaluated simulatively.
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