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An integrated sensing approach that fuses vision and range information to land an autonomous class 1 unmanned aerial system (UAS) controlled by e-modification model reference adaptive control is presented. The navigation system uses a feature detection algorithm to locate features and compute the corresponding range vectors on a coarsely instrumented landing platform. The relative translation and rotation state is estimated and sent to the flight computer for control feedback. A robust adaptive control law that guarantees uniform ultimate boundedness of the adaptive gains in the presence of bounded external disturbances is used to control the flight vehicle. Experimental flight tests are conducted to validate the integration of these systems and measure the quality of result from the navigation solution. Robustness of the control law amidst flight disturbances and hardware failures is demonstrated. The research results demonstrate the utility of low-cost, low-weight navigation solutions for navigation of small, autonomous UAS to carryout littoral proximity operations about unprepared shipdecks. 

This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed algorithms.