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Title: Autonomous collision avoidance for Unmanned Aerial Systems
Author: Melega, Marco
ISNI:       0000 0004 5355 5333
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2014
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Unmanned Aerial System (UAS) applications are growing day by day and this will lead Unmanned Aerial Vehicle (UAV) in the close future to share the same airspace of manned aircraft. This implies the need for UAS to define precise safety standards compatible with operations standards for manned aviation. Among these standards the need for a Sense And Avoid (S&A) system to support and, when necessary, substitute the pilot in the detection and avoidance of hazardous situations (e.g. midair collision, controlled flight into terrain, flight path obstacles, and clouds). This thesis presents the work come out in the development of a S&A system taking into account collision risks scenarios with multiple moving and fixed threats. The conflict prediction is based on a straight projection of the threats state in the future. The approximations introduced by this approach have the advantage of high update frequency (1 Hz) of the estimated conflict geometry. This solution allows the algorithm to capture the trajectory changes of the threat or ownship. The resolution manoeuvre evaluation is based on a optimisation approach considering step command applied to the heading and altitude autopilots. The optimisation problem takes into account the UAV performances and aims to keep a predefined minimum separation distance between UAV and threats during the resolution manouvre. The Human-Machine Interface (HMI) of this algorithm is then embedded in a partial Ground Control Station (GCS) mock-up with some original concepts for the indication of the flight condition parameters and the indication of the resolution manoeuvre constraints. Simulations of the S&A algorithm in different critical scenarios are moreover in-cluded to show the algorithm capabilities. Finally, methodology and results of the tests and interviews with pilots regarding the proposed GCS partial layout are covered.
Supervisor: Savvaris, Al; Tsourdos, A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available