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 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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.