Giancarmine Fasano

Multi-Sensor-Based Fully Autonomous Non-Cooperative Collision Avoidance System for Unmanned Air Vehicles

This paper presents a fully autonomous multi-sensor anti-collision system for Unmanned Aerial Vehicles. This system is being developed by the Italian Aerospace Research Center in collaboration with the Department of Aerospace Engineering of the University of Naples “Federico II”. The research project is entitled TECVOL and is funded in the frame of the National Aerospace Research Program. The system prototype will be initially installed onboard a manned laboratory aircraft equipped for automatic control, therefore flight tests will verify the adequacy of attained performances for supporting fully autonomous flight. The obstacle detection and tracking function is performed by a multi-sensor configuration made up by a pulsed Ka-band radar, two visible (panchromatic and color) video cameras, two infrared video cameras, and two computers. One computer is dedicated to real time sensor fusion and communication with the radar and the flight control computer (by means of a deterministic data bus), the other is devoted to image processing. On the basis of the tracking estimates and of a Collision Avoidance Software, the flight control computer generates and follows in real-time a proper escape trajectory. In order to evaluate the performance of the collision avoidance system, numerical simulations have been performed taking into account the obstacle detection sensors’ accuracy, unmanned aircraft’s and intruder’s flight dynamics, navigation system accuracy and latencies, and collision avoidance logic. The relevant results helped to assess overall system performances and are discussed in depth.

Flight Test of a Radar-Based Tracking System for UAS Sense and Avoid

Presented here is an analysis of an extensive flight campaign aimed at characterizing peculiarities, advantages, and limitations of an obstacle detection and tracking system based on a pulse radar. The hardware and software prototypical sensing system was installed onboard an optionally piloted flying laboratory from the very light aircraft (VLA) category. Test flights with a single intruder aircraft of the same class were carried out to demonstrate autonomous noncooperative unmanned aerial system (UAS) collision avoidance capability and to evaluate the level of achievable situational awareness. First, the adopted architecture and the developed tracking algorithm are presented. Subsequently, flight data gathered in various relative flight geometries, covering chasing flights and quasi-frontal encounters, are analyzed in terms of radar performance, including detection range and range and angle measurement accuracies. The analysis describes the impact of ground echoes and navigation uncertainties, system tracking reliability, and achievable accuracy in estimation of relative position and velocity. On the basis of Global Positioning System (GPS) data gathered simultaneously with obstacle detection flight experiments, a detailed error analysis is conducted. Special emphasis is given to the validation of proposed methodology to separate between intruder and ground echoes, which is a critical aspect for light aircraft due to their limited radar cross sections (RCS) and flight altitudes. In conclusion the radar demonstrates its potential to attain adequate situational awareness, however the limits of single sensor tracking are also pointed out. Above all the negative impact of poor angular accuracy on missed detection and false alarm rates is pointed out.

A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object

This paper presents a customized three-dimensional template matching technique for autonomous pose determination of uncooperative targets. This topic is relevant to advanced space applications, like active debris removal and on-orbit servicing. The proposed technique is model-based and produces estimates of the target pose without any prior pose information, by processing three-dimensional point clouds provided by a LIDAR. These estimates are then used to initialize a pose tracking algorithm. Peculiar features of the proposed approach are the use of a reduced number of templates and the idea of building the database of templates on-line, thus significantly reducing the amount of on-board stored data with respect to traditional techniques. An algorithm variant is also introduced aimed at further accelerating the pose acquisition time and reducing the computational cost. Technique performance is investigated within a realistic numerical simulation environment comprising a target model, LIDAR operation and various target-chaser relative dynamics scenarios, relevant to close-proximity flight operations. Specifically, the capability of the proposed techniques to provide a pose solution suitable to initialize the tracking algorithm is demonstrated, as well as their robustness against highly variable pose conditions determined by the relative dynamics. Finally, a criterion for autonomous failure detection of the presented techniques is presented.

An Innovative Procedure for Calibration of Strapdown Electro-Optical Sensors Onboard Unmanned Air Vehicles

This paper presents an innovative method for estimating the attitude of airborne electro-optical cameras with respect to the onboard autonomous navigation unit. The procedure is based on the use of attitude measurements under static conditions taken by an inertial unit and carrier-phase differential Global Positioning System to obtain accurate camera position estimates in the aircraft body reference frame, while image analysis allows line-of-sight unit vectors in the camera based reference frame to be computed. The method has been applied to the alignment of the visible and infrared cameras installed onboard the experimental aircraft of the Italian Aerospace Research Center and adopted for in-flight obstacle detection and collision avoidance. Results show an angular uncertainty on the order of 0.1° (rms).

Analysis of spaceborne tandem configurations for complementing COSMO with SAR interferometry

This paper analyses the possibility of using a fifth passive satellite for endowing the Italian COSMO-SkyMed constellation with cross- and along-track SAR interferometric capabilities, by using simultaneously flying and operating antennas. Fundamentals of developed models are described and potential space configurations are investigated, by considering both formations operating on the same orbital plane and on separated planes. The study is mainly aimed at describing achievable baselines and their time histories along the selected orbits. The effects of tuning orbital parameters, such as eccentricity or ascending node phasing, are pointed out, and simulation results show the most favorable tandem configurations in terms of achieved baseline components, percentage of the orbit adequate for interferometry, and covered latitude intervals.

Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems

This paper is focused on the development and the flight performance analysis of an image-processing technique aimed at detecting flying obstacles in airborne panchromatic images. It was developed within the framework of a research project which aims at realizing a prototypical obstacle detection and identification System, characterized by a hierarchical multisensor configuration. This configuration comprises a radar, that is, the main sensor, and four electro-optical cameras. Cameras are used as auxiliary sensors to the radar, in order to increase intruder aircraft position measurement, in terms of accuracy and data rate. The paper thoroughly describes the selection and customization of the developed image-processing techniques in order to guarantee the best results in terms of detection range, missed detection rate, and false-alarm rate. Performance is evaluated on the basis of a large amount of images gathered during flight tests with an intruder aircraft. The improvement in terms of accuracy and data rate, compared with radar-only tracking, is quantitatively demonstrated.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

Satellite Angular Velocity Estimation Based on Star Images and Optical Flow Techniques

An optical flow-based technique is proposed to estimate spacecraft angular velocity based on sequences of star-field images. It does not require star identification and can be thus used to also deliver angular rate information when attitude determination is not possible, as during platform de tumbling or slewing. Region-based optical flow calculation is carried out on successive star images preprocessed to remove background. Sensor calibration parameters, Poisson equation, and a least-squares method are then used to estimate the angular velocity vector components in the sensor rotating frame. A theoretical error budget is developed to estimate the expected angular rate accuracy as a function of camera parameters and star distribution in the field of view. The effectiveness of the proposed technique is tested by using star field scenes generated by a hardware-in-the-loop testing facility and acquired by a commercial-off-the shelf camera sensor. Simulated cases comprise rotations at different rates. Experimental results are presented which are consistent with theoretical estimates. In particular, very accurate angular velocity estimates are generated at lower slew rates, while in all cases the achievable accuracy in the estimation of the angular velocity component along boresight is about one order of magnitude worse than the other two components.

Cooperative UAV navigation based on distributed multi-antenna GNSS, vision, and MEMS sensors

This paper presents an algorithm for cooperative UAV navigation that exploits vision-based sensing, standalone GPS, differential GPS among antennas embarked on different flying platforms, and measurements obtained by inertial sensors and magnetometers. Unlike other cooperative navigation approaches, the developed technique is mainly aimed at improving navigation performance in outdoor environments, either in real time or off-line. The logical architecture and the main processing steps are discussed. Then, algorithms for differential GPS/vision processing and sensor fusion for navigation state estimation are introduced. Covariance analysis is used for theoretical performance assessment. The hardware system used for concept demonstration comprises a customized quadrotor and different GPS antennas and receivers, and is briefly described. Finally, first results from experimental tests are presented. In particular, the attitude solution obtained by differential GPS and vision is compared with the estimates provided by the onboard autopilot system.

Real Time Corner Detection for Miniaturized Electro-Optical Sensors Onboard Small Unmanned Aerial Systems

This paper describes the target detection algorithm for the image processor of a vision-based system that is installed onboard an unmanned helicopter. It has been developed in the framework of a project of the French national aerospace research center Office National d’Etudes et de Recherches Aérospatiales (ONERA) which aims at developing an air-to-ground target tracking mission in an unknown urban environment. In particular, the image processor must detect targets and estimate ground motion in proximity of the detected target position. Concerning the target detection function, the analysis has dealt with realizing a corner detection algorithm and selecting the best choices in terms of edge detection methods, filtering size and type and the more suitable criterion of detection of the points of interest in order to obtain a very fast algorithm which fulfills the computation load requirements. The compared criteria are the Harris-Stephen and the Shi-Tomasi, ones, which are the most widely used in literature among those based on intensity. Experimental results which illustrate the performance of the developed algorithm and demonstrate that the detection time is fully compliant with the requirements of the real-time system are discussed.