Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors.

The sense and avoid (SAA) capability is a key issue in the integration of Unmanned Aircraft Systems (UAS) into the common airspace. Besides the lack of a regulatory framework in most countries, some technological challenges are still not resolved which holds back routine UAS applications. Most UAS or Remotely Piloted Aircraft Systems (RPAS) have no SAA capability. The separation for these aircrafts must be done solely by the Air Traffic Control (ATC) which is not cost effective. Furthermore, in the case of a lost link, there should be some kind of collision avoidance capability onboard; otherwise, if we want to maintain the current safety level of the air traffic system, a single unmanned airplane would block a significant sector of the airspace. Besides the safety and cost issues in the RPAS case, general aviation could benefit from a solid SAA system as well. An SAA instrument could support pilots in the case of conflicting instructions coming from the different layers of collision avoidance (for example ATC and onboard devices) and in some cases it could extend the range for the non-cooperative collision avoidance done by the pilots. An ideal SAA system would be cost effective, lightweight, and low-power in order to minimize the negative effect on cost, make application onboard small unmanned aerial vehicles (UAVs) possible, and maximize flight time of the aircraft.

Numerical simulation of complex computational fluid dynamics problems evolving in time plays an important role in scientific and engineering applications. Accurate behavior of dynamical systems can be understood using large scale simulations which traditionally requires expensive super-computing facilities. In the paper a Field Programmable Gate Array (FPGA) based framework is described to accelerate simulation of complex physical spatio-temporal phenomena. Simulating complicated geometries requires unstructured spatial discretization which results in irregular memory access patterns severely limiting computing performance. Data locality is improved by mesh node renumbering technique which results in a sequential memory access pattern. Additionally storing a small window of cell-centered state values in the on-chip memory of the FPGA can increase data reuse and decrease memory bandwidth requirements. Generation of the floating-point data path and control structure of the arithmetic unit containing dozens of operators is a very challenging task when the goal is high operating frequency. Efficiency and use of the framework is described by a case study solving the Euler equations on an unstructured mesh using finite volume technique. On the currently available largest FPGA the generated architecture contains three processing elements working in parallel providing 75 times speedup compared to a high performance microprocessor.

Forerunner UAV refers to an unmanned aerial vehicle equipped with a downward-looking camera flying in front of the advancing emergency ground vehicles (EGV) to notify the driver about the hidden dangers (e.g., other vehicles). A feasibility demonstration in an urban environment having a multicopter as the forerunner UAV and two cars as the emergency and dangerous ground vehicles was done in ZalaZONE Proving Ground, Hungary. After the description of system hardware and software components, test scenarios, object detection and tracking, the main contribution of the paper is the development and evaluation of encounter risk decision methods. First, the basic collision risk evaluation applied in the demonstration is summarized, then the detailed development of an improved method is presented. It starts with the comparison of different velocity and acceleration estimation methods. Then, vehicle motion prediction is conducted, considering estimated data and its uncertainty. The prediction time horizon is determined based on actual EGV speed and so braking time. If the predicted trajectories intersect, then the EGV driver is notified about the danger. Some special relations between EGV and the other vehicle are also handled. Tuning and comparison of basic and improved methods is done based on real data from the demonstration. The improved method can notify the driver longer, identify special relations between the vehicles and it is adaptive considering actual EGV speed and EGV braking characteristics; therefore, it is selected for future application.