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

This paper presents a visual-based approach that allows an Unmanned Aerial Vehicle (UAV) to detect and track a cooperative flying vehicle autonomously using a monocular camera. The algorithms are based on template matching and morphological filtering, thus being able to operate within a wide range of relative distances (i.e., from a few meters up to several tens of meters), while ensuring robustness against variations of illumination conditions, target scale and background. Furthermore, the image processing chain takes full advantage of navigation hints (i.e., relative positioning and own-ship attitude estimates) to improve the computational efficiency and optimize the trade-off between correct detections, false alarms and missed detections. Clearly, the required exchange of information is enabled by the cooperative nature of the formation through a reliable inter-vehicle data-link. Performance assessment is carried out by exploiting flight data collected during an ad hoc experimental campaign. The proposed approach is a key building block of cooperative architectures designed to improve UAV navigation performance either under nominal GNSS coverage or in GNSS-challenging environments.

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

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.