Prototype Sense-and-Avoid System for UAVs

In this technical report we describe our efforts towards a field deployable Sense and Avoid system for Unmanned Aerial Vehicles (UAVs) which uses passive vision as the main sensing modality. The ability of UAVs to operate safely in the presence of other air traffic is critical towards acceptance of UAVs in civilian and military airspace. This will allow UAVs to be used to their fullest potential. In order to operate freely in the presence of manned airborne traffic UAVs must demonstrate a Sense and Avoid capability that meets or exceeds that of an equivalent human pilot. Furthermore this capability should be achieved without the use of cooperative communication with other aircraft or prior knowledge of other aircrafts’ flight plans. We describe our collision avoidance algorithm and software-in-theloop testing, vision based detection method with a 98% detection rate out to a range of 4.5 miles which exceeds the FAA regulation of 3 statute miles. A field deployable Sense and Avoid system must be able to operate with consistent performance across a variety of atmospheric conditions including cloud, fog and haze of various degrees that can occur under conditions commonly described as Visual Meteorological Conditions (VMC). In order to model the effect of all these conditions on the performance of the detection system we developed an atmospheric image formation modelling system that takes as inputs weather conditions and can predict the appearance of the image of an aircraft of given geometry at various ranges. This predictive model also determines the minimum resolution needed to guarantee the required detection performance. Passive vision provides the bearing to the intruding aircraft. Range estimation is only possible by executing additional maneuvers which causes significant mission interference. We investigate the feasibilty of a flash lidar system that can be used as a confirming sensor to further reduce the false positive rate and provide range of the intruding aircraft.

[1]  Shree K. Nayar,et al.  Vision in bad weather , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  E. Feron,et al.  Robust hybrid control for autonomous vehicle motion planning , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[3]  Tarak Gandhi,et al.  Performance characterization of the dynamic programming obstacle detection algorithm , 2006, IEEE Transactions on Image Processing.

[4]  Tarak Gandhi,et al.  Detection of obstacles in the flight path of an aircraft , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[6]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[7]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[8]  Robert F. Stengel,et al.  Toward intelligent flight control , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).