Ettore De Lellis

Morphological filtering and target tracking for vision-based UAS sense and avoid

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.

On-line trajectory generation for autonomous unmanned vehicles in the presence of no-fly zones

In this article, an algorithm for three-dimensional path generation and tracking for unmanned air vehicles in the presence of no-fly zones is proposed. The algorithm is based on a local optimization procedure aimed to find the shortest path between the waypoints in compliance with all path constraints. Vehicle structural and envelope limitations are accounted for by simple geometric constraints such as minimum curvature radius and flight path angle limitations, while no-fly zones are defined as cylindrical objects with infinite altitude. The algorithm is simple and it has a limited computational burden, at most quadratic with the number of zones to avoid. This makes the algorithm very suitable for real-time applications even in case of a high number of forbidden zones. Algorithm effectiveness has been demonstrated by means of numerical simulations in scenarios including the presence of no-fly zones not known before flight (for instance, in the case of sudden changes of weather conditions and/or detection of new fixed obstacles).

Flight Testing of a Fully Adaptive Algorithm for Autonomous Fixed Wing Aircrafts Landing

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.

Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid

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.

Flight Performance Assessment of Vision-based Detection and Tracking for UAS Sense and Avoid

This paper focuses on a vision-based detection and tracking algorithm for UAS non cooperative collision avoidance. It is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and a multi-frame tracking algorithm in stabilized coordinates that is used to confirm intruder detection. Once this detection is achieved, template matching-based algorithms are used to track the intruder in subsequent frames. The developed technique has been tested using flight data gathered in the framework of the 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”. First results are promising: considering two frontal encounters in completely different illumination conditions, range for confirmed detection of an ultra-light intruder aircraft is in both cases of the order of 2.5 km, no false tracks are generated, and angular measurement accuracy in North-East-Down coordinates is of the order of 0.1°. Analysis of flight images shows that potential sources of false detections, such as small dim clouds and sun glares, are correctly removed.

Challenges and Solutions for Vision-based Sense and Avoid

This paper focuses on sensing algorithms for vision-based non cooperative sense and avoid. Obstacle detection and tracking is based first on morphological filtering and local image analysis (detection), then on multi-frame processing in stabilized coordinates (tentative tracking), and finally on template matching and Kalman filtering-based state estimation (firm tracking). A conflict detection logic is introduced which uses an adaptive line-of-sight rate threshold based on the functional dependencies of the distance at closest point of approach in near collision conditions. The derived threshold takes into account ownship motion and estimated intruder azimuth, while assumptions are made regarding detection performance of the electro-optical system and intruder speed. The developed techniques have 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”. Achieved experimental results are promising and are discussed focusing on algorithm tuning and system performance in terms of probability of intruder declaration as a function of range, false alarm rate, tracking accuracy, and reliability of vision-based conflict detection.

Exploiting Forward Looking Radar Measurements and Digital Map Data Fusion for Altimetry Estimation during Low-Altitude Flight

In this paper a sensor fusion algorithm is proposed for an optimal estimation of the Above Runway Level of an aircraft during takeoff or approach, by the combined use in a Kalman filter of Laser Altimeter, GPS, DEMs (Digital Elevation Map) data and ground echoes detected by a forward looking radar. The algorithm was developed in the framework of the nationally funded project TECVOL, a collaboration between Italian Center for Aerospace Researches (CIRA) and the University of Naples (UNINA). The validation of the designed algorithm required the development of orographic trend models, DEMs error model, and a proper improvement of the laser altimeter model previously used in the framework of TECVOL, in order to take into account true terrain elevation. Numerical simulation tests and in-flight data collections have been used to validate the algorithm.