An optimal 3D analytical solution for collision avoidance between aircraft

Abstract—This paper focuses on an optimal three-dimensional analytical solution for aircraft non-cooperative collision avoidance. Based on a geometric approach, an analytical solution to a proper kinematic optimization problem is here derived, which implies the simultaneous change of all control variables (speed module, track and slope angles), thus this approach resulting very suitable for real-time applications. In a pair-wise non-cooperative collision avoidance, the speed vector of the aircraft implementing the proposed control strategy is continuously changed with the aim of skimming the safety bubble surrounding the other aircraft (considered as an intruder). Under certain hypotheses, the proposed solution can be proved to be optimal with respect to the minimization of aircraft deviation from its nominal trajectory. Proper performance indexes have been defined and challenging conflict scenarios, where the other aircraft be haves even a pursuer, have been analyzed

Design process and real-time validation of an innovative autonomous mid-air flight and landing system

This paper describes the design process and the real-time validation of an innovative autonomous mid-air flight and landing system developed by the Italian Aerospace Research Center in the framework of the Italian national funded project TECVOL (Technologies for the Autonomous Flight). In the paper it is provided an insight of the whole development process of the system under study. In particular, the project framework is illustrated at first, then the functional context and the adopted design and testing approach are described, and finally the on-ground validation test rig on purpose designed is addressed in details. Furthermore, the hardware-in-the-loop validation of the autonomous mid-air flight and landing system by means of the real-time test rig is described and discussed

An EGNOS Based Navigation System for Highly Reliable Aircraft Automatic Landing

This paper presents the CIRA’s flying test facility for autonomous mid-air flight and landing on runways instrumented by Differential Global Positioning System (DGPS) base station. The aim of the paper is to describe the facility used for testing auto-landing and mid-air flight algorithms, using many different sources to determine a hybrid position of the vehicle with high precision level. Highly precise navigation is the core technology required for many applications, such as automated aerial refuelling, sea-based joint precision approach and landing systems, station-keeping, unmanned aerial vehicles swarming and formation flight, and unmanned ground vehicles convoys. Current navigation efforts presented in literature are focused on carrier-phase differential global positioning system integrated with inertial measurement data, but it must to emphasize that this solution may not be sufficient in certain tactical environments and low-cost Inertial Measurement Units (IMU) may not be able to bridge GPS outages with sufficient accuracy. To overcome this problem, we decided to use additional sensors to improve the integrity and reliability of navigation solutions. The sensors we used are: two GPS, a laser altimeter, an AHRS (Attitude Heading Reference System), and an ADS (Air Data System). By using the above listed sensor measures in a sensor fusion algorithm, we obtained a high precision level in navigation measurements. In the paper, after described the on-board and on-ground architectures of our flight test facility, we present the sensor fusion algorithm we developed and, finally, we describe some results obtained in the validation stages, consisting in both off line simulations and in-flight testing, of the proposed algorithm

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

This paper presents a fully autonomous multisensor anti-collision system for Unmanned Aerial Vehicles (UAVs). This system is being developed by the Italian Aerospace Research Center (CIRA) in collaboration with the Dept. of Aerospace Engineering of the University of Naples “Federico II”, within a research project named TECVOL, funded in the frame of the National Aerospace Research Program (PRO.R.A.) on UAV. The system prototype will be initially installed onboard a manned laboratory aircraft equipped for automatic control so that flight tests will verify the adequacy of attained performances for supporting fully autonomous flight. In order to perform the obstacle detection and identification function, a multisensor configuration has been designed in the TECVOL preliminary studies. The hardware configuration is made up by a pulsed Ka-band radar, two visible (panchromatic and colour) videocameras, two infrared (IR) videocameras, and two computers, one dedicated to sensor fusion and communication with the flight control computer and with the radar, the other devoted to image processing. They are connected to the Flight Control Computer by means of a deterministic data bus. On the basis of these tracking estimates and of a Collision Avoidance Software, the GNC computer generates and follows in real time a proper escape trajectory. In order to evaluate the performance of the entire collision avoidance system, numerical simulations have been performed taking into account the DS&A sensors’ accuracy, UAV’s and intruder’s flight dynamics, navigation system accuracy and latencies, collision avoidance logic, and practical real-time implementation issues. The relevant results helped to assess overall system performances. They are discussed in depth at the end of the paper

Multisensor based Fully Autonomous Non-Cooperative Collision Avoidance System for UAVs

This paper presents a fully autonomous multisensor anti-collision system for Unmanned Aerial Vehicles (UAVs). This system is being developed by the Italian Aerospace Research Center (CIRA) in collaboration with the Dept. of Aerospace Engineering of the University of Naples “Federico II”, within a research project named TECVOL, funded in the frame of the National Aerospace Research Program (PRO.R.A.) on UAV. The system prototype will be initially installed onboard a manned laboratory aircraft equipped for automatic control so that flight tests will verify the adequacy of attained performances for supporting fully autonomous flight. In order to perform the obstacle detection and identification function, a multisensor configuration has been designed in the TECVOL preliminary studies. The hardware configuration is made up by a pulsed Ka-band radar, two visible (panchromatic and colour) videocameras, two infrared (IR) videocameras, and two computers, one dedicated to sensor fusion and communication with the flight control computer and with the radar, the other devoted to image processing. They are connected to the Flight Control Computer by means of a deterministic data bus. On the basis of these tracking estimates and of a Collision Avoidance Software, the GNC computer generates and follows in real time a proper escape trajectory. In order to evaluate the performance of the entire collision avoidance system, numerical simulations have been performed taking into account the DS&A sensors’ accuracy, UAV’s and intruder’s flight dynamics, navigation system accuracy and latencies, collision avoidance logic, and practical real-time implementation issues. The relevant results helped to assess overall system performances. They are discussed in depth at the end of the paper

Unscented Kalman Filtering for Identification of a Re--entry Vehicle in Transonic Regime

Parameter identification methods for processing flight data are frequently used to validate and improve a preflight aerodynamic database and, specifically, to reduce the associated uncertainties. In this framework, the paper describes an identification methodology developed for the first flying test bed of the Italian Aerospace Research Center, a demonstrator of technologies relevant to future reusable launch vehicles. The analysis is focused on aerodynamic modeling of the reentry vehicle configuration in the transonic flow regime, in which flight control system performance is affected by a significant level of parameter uncertainty. The parameter estimation is formulated as a nonlinear filtering problem and solved through a multistep approach, in which the aerodynamic coefficients are identified first and, in a following phase, a set of model parameters is updated. In each step, an unscented Kalman filter is used as a recursive estimation algorithm. The methodology is applied to the flight data of the Dropped Transonic Flight Test mission of the vehicle, carried out during the winter of 2007. The reported results demonstrate the good characteristics of the technique in terms of convergence, reduction of uncertainty of the a priori aerodynamic model, and capability of extracting the information content from a rather limited set of flight data on vehicle response