In this paper, a method for accurate path following for miniature air vehicles is developed. The method is based on the notion of vector fields, which are used to generate desired course inputs to inner-loop attitude control laws. Vector field path following control laws are developed for straight-line paths and circular arcs and orbits. Lyapunov stability arguments are used to demonstrate asymptotic decay of path following errors in the presence of constant wind disturbances. Experimental flight tests have demonstrated mean path following errors on less than one wingspan for straight-line and orbit paths, and less than three wingspans for paths with frequent changes in direction.

This paper presents a method for determining the GPS location of a ground-based object when imaged from a fixed-wing miniature air vehicle (MAV). Using the pixel location of the target in an image, with measurements of MAV position and attitude, and camera pose angles, the target is localized in world coordinates. The main contribution of this paper is to present four techniques for reducing the localization error. In particular, we discuss RLS filtering, bias estimation, flight path selection, and wind estimation. The localization method has been implemented and flight tested on BYU's MAV testbed and experimental results are presented demonstrating the localization of a target to within 3 meters of its known GPS location.

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This paper outlines an approach for automated landing of miniature aerial vehicles (MAVs). A landing algorithm defining the landing flight path as a function of height above ground, and the control strategies for following the path, are described. Two methods are presented for estimating height above ground, one based on barometric pressure measurements and the other utilizing optic-flow measurements. The development of an optic-flow sensor and associated sampling strategies are described. Utilizing estimates of height above ground from barometric pressure and optic-flow measurements, repeated landings were performed with a 1.5 m wingspan MAV. With height above ground estimated from barometric pressure measurements alone, landing errors averaged 7.6 m. When optic flow and barometric pressure measurements were combined to estimate height above ground, the average landing error was only 4.3 m.

This paper outlines a method for using vision-based feedback to accurately land a MAV on a visually identifiable target of approximately known location. The method presented is robust to wind, capable of handling both stationary and moving targets, and capable of correcting for camera misalignment, state estimation biases, and parameter estimation biases. Landing results from actual flight tests are presented which demonstrate the effectiveness of the proposed method.