In this brief, we consider the problem of 3-D path generation and tracking for unmanned air vehicles (UAVs). The proposed path generation algorithm allows us to find a path satisfying arbitrary initial and final conditions, specified in terms of position and velocity. Our method assumes that aircraft structural and dynamic limitations can be translated in a turn radius constraint; therefore, the generated paths satisfy a constraint on the minimum admissible turning radius. The proposed algorithm for the path tracking guarantees, under specified assumptions, that the tracking error, both in position and in attitude, asymptotically tends to zero. The work has been carried out with reference to the UAV of the Italian Aerospace Research Center (CIRA). Simulation results for both the path generation and the tracking algorithms are presented; the latter have been obtained using a detailed 6-degree-of-freedom model of the CIRA UAV in the presence of wind and turbulence.

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This paper presents a customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid. Obstacle detection and tentative tracking for track confirmation are based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and a multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. The developed technique has been tested using flight data gathered in a sense and avoid research project carried out by the Italian Aerospace Research Center and the Department of Industrial Engineering of the university of Naples “Federico II”. Performance evaluated in two near collision geometries allows estimating algorithm robustness in terms of sensitivity on weather and illumination conditions, detection range and false alarm rate, and overall tracking accuracy

A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy

This paper presents the flight test results of a fully adaptive algorithm for autonomous fixed wing aircrafts landing developed by CIRA, the Italian Aerospace Research Centre. The algorithm is designed and implemented in the framework of a complete autonomous guidance system, worked out by CIRA, able to allow autonomous way-points navigation, autonomous landing and autonomous collision avoidance for fixed wing aircrafts. The algorithm presented in the paper is designed to perform a fully adaptive autonomous landing starting from any point of the three dimensional space, based on the use of the DGPS/AHRS technology. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line landing trajectory re-planning, fully autonomy from pilot inputs, weakly instrumented landing runway, ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions. The flight tests have been conducted at an airfield in Caserta, in the south of Italy, close the CIRA. The paper is structured into several paragraphs describing the algorithm designed for the autolanding maneuver, the control system architecture and the methodologies developed in order to safely manage the possible presence of failures and/or unfavorable weather conditions, the preliminary results of the real time validation with hardware in the loop simulation and, finally, the performances achieved by using the CIRA experimental flying platform, with reference to the real flight experiments. Nomenclature X = position along X-Runway axis ) ( X V X = inertial velocity profile along X-Runway axis ) ( X V Z = inertial velocity profile along Z-Runway axis ) ( X H = position profile along Z-Runway axis 0 H = initial position along Z-Runway axis 0 X = initial position along X-Runway axis 0 X V = initial inertial velocity profile along X-Runway axis F H = desired final position along Z-Runway axis F X = desired final position along X-Runway axis XF V = desired final inertial velocity profile along X-Runway axis ZF V = desired final inertial velocity profile along Z-Runway axis