Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles

In recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task. Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity. During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms. In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system. All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV. All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis. Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA

Radar/electro-optical data fusion for non-cooperative UAS sense and avoid

Abstract This paper focuses on hardware/software implementation and flight results relevant to a multi-sensor obstacle detection and tracking system based on radar/electro-optical (EO) data fusion. The sensing system was installed onboard an optionally piloted very light aircraft (VLA). Test flights with a single intruder plane of the same class were carried out to evaluate the level of achievable situational awareness and the capability to support autonomous collision avoidance. System architecture is presented and special emphasis is given to adopted solutions regarding real time integration of sensors and navigation measurements and high accuracy estimation of sensors alignment. On the basis of Global Positioning System (GPS) navigation data gathered simultaneously with multi-sensor tracking flight experiments, potential of radar/EO fusion is compared with standalone radar tracking. Flight results demonstrate a significant improvement of collision detection performance, mostly due to the change in angular rate estimation accuracy, and confirm data fusion effectiveness for facing EO detection issues. Relative sensors alignment, performance of the navigation unit, and cross-sensor cueing are found to be key factors to fully exploit the potential of multi-sensor architectures

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5-11 December, to 17.5% (25/143 samples) in the week 12-18, to 65.9% (89/135 samples) in the week 19-25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool

Autonomous Take Off System: Development and Experimental Validation

This paper describes the design and validation process of an innovative Autonomous Take Off system, developed by the Italian Aerospace Research Center (CIRA) in the framework of the Italian national funded project TECVOL (Technologies for the Autonomous Flight). The autonomous take-off module is part of the autonomous Guidance, Navigation and Control prototype worked out by CIRA in the same project, where significant research effort has been devoted to achievement of high automation during all the flight phases, from take off to landing. The developed automated system allows take off, navigation through three-dimensional waypoints and landing of an aircraft without human intervention, also in presence of environmental disturbances and/or subsystem failures. In aerospace research and development activities not only functional requirements play an important role in the project, also process requirements and system engineering methods are fundamental for project success. In particular, the autonomous take off system development and validation process has been designed in order to be highly reliable but with a substantial reduction of needed time and costs. In the paper the process of design and validation applied to the proposed system development is examined in details, while providing also a description of the automatic take off system

A Real-Time Landing Gear Simulation Model For A Very Light Aircraft: Development And Experimental Validation

The main purpose of the paper is to present a landing gear model for autonomous on ground control design and Hardware In The Loop Real-Time simulations. This model is a component of the real-time aircraft simulator, developed by CIRA (the Italian Aerospace Research Centre) for its fixed wing flying platform used to in-flight test all the developed UAV functionalities and, in particular, the autonomous take-off and post touch down algorithms. The autonomous take-off and post touch down managing systems are part of the autonomous GNC (Guide Navigation and Control) prototype worked out by CIRA, in the framework of the national founded project TECVOL (Technologies for the Autonomous Flight). The landing gear model core, implemented in MATLAB/Simulink environment, is a set of flexible physical-based equations that can easily simulate different aircrafts and runways by changing few design parameters. Moreover, the model contains even the logic for managing the dynamics of the vehicle in case of velocity near to zero. It is coupled with a six degree of freedom aircraft rigid body dynamics equations also implemented in Simulink. The landing gear model inputs are: runway elevation; aircraft inertial state variable (position, velocity and attitude); nose gear steering angle; brake command; forces and moments in body axis whereas the outputs are the gear forces and moments in body axis and a Boolean signal that simulates the Weight on Wheels (WoW) sensors. For each wheel of the landing gear is defined the position in body frame. This assignment, along with runway elevation and aircraft inertial state variable, allows to calculate the velocity, position, and contact point in the inertial frame besides forces and moments, due to the interaction between tires and ground. A relevant advance in the use of a model that calculates the ground reaction separately for each wheel is the capacity to simulate the unbalanced dynamic loads normally encountered during on-ground aircraft operations. The normal tire force is computed as typical spring and damper reaction depending on the distance and relative velocity between the wheel and ground. Axial and lateral friction forces are computed using an approximation of tire theory and model reported from Pazmany (1986). The model is able to evaluate friction forces and moments at all velocities varying the values of friction coefficients. Probably the most difficult ground dynamics phenomenon to simulate in real-time is stopping the aircraft [2]. When the aircraft is stopped the friction forces and moments must be equal to the other external forces. At very low velocity, close to zero, if the usual equations of tire dynamics are applied, it can be computed unrealistic forces and moments due to some divisions by speed present in the utilized formulae. Our proposed solution foresees that the friction forces and moments magnitude decrease towards the external forces and moments when the velocity is decayed to zero. After the vehicle stop, only when the external forces (aerodynamic, thrust and weight forces) exceed the static friction reaction, the aircraft starts to move. To validate the model and to estimate the friction coefficient we designed specific experimental test to perform with aircraft on the runway. The comparison of experimental and simulated data demonstrates that the proposed model is able to describe the real landing gear behaviour. The model has been used to design and verify the on-ground control law of UAV GNC system

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

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

Advanced Sensing Issues for UAS Collision Avoidance

This paper presents the results of up to date investigations in the field of Obstacle Tracking System for Sense and Avoid applications of Unmanned Aerial Systems. In particular it highlights the most recent trends in this field, such as the adoption of Advanced Multi Sensor Data Fusion techniques in order to increase the awareness on the information of a collision threat and the adoption of lightweight onboard system architectures that can meet the requirements for installation onboard small Unmanned Aerial Systems. As a result of the experience with flight testing for a prototypical Sense and Avoid System developed within the framework of a project of the Italian Aerospace Research Centre, some advanced solutions have been proposed to solve the above reported issues. In particular, the adoption of Particle Filtering for realizing Multi-Sensor Data Fusion has been considered in order to get reliable and accurate estimates of the collision threat, such increasing flight awareness. A customized version of the Particle Filter has been developed and tested by exploiting data acquired during flight tests. Moreover, a specific study has been carried out considering the issues for the realization of an adequate Sense and Avoid system for small Unmanned Aerial System. An integrated configuration that includes ADS-B and Electro Optical sensor has been identified as the most effective solutions standing Sense and Avoid requirements. This solution requires that a great effort is put on software both in terms of data fusion algorithms and real time image processing. The results of this study are described at the end of the paper

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

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