In this thesis two methods were developed for precise maritime landing of an autonomous multirotor aircraft based on real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The first method called RTK-localized method (RLM) uses RTK GNSS measurements to localize a sea vessel and execute the landing. RLM was demonstrated outdoors in hardware and landed on a physically simulated boat called a mock-boat with an average landing error of 9.7 cm. The mock-boat was actuated to have boat-like motion and a forward velocity of ~2 m/s. This method showed that accurate landing is possible with RTK GNSS as the primary means of localizing a sea vessel. The localization was unaided by non-GNSS sensors or an estimator, but lacked full attitude estimation and measurement smoothing. The second method was called RTK-Estimation Method (REM) and provides a more complete and robust solution, particularly at sea. It includes a base (landing pad) estimator to fuse RTK GNSS measurements with a dynamic model of a sea vessel. In contrast to RLM, the estimator provides full attitude estimation and measurement smoothing. The base estimator consists of an EKF in conjunction with a complimentary filter and estimates the relative position, attitude, and velocity of a moving target using RTK GNSS and inertial measurements alone. REM was demonstrated outdoors in hardware for 18 flight tests. The same mock-boat from RLM was used as a substitute for a sea vessel, and the boat motion varied between tests. These dynamics were recorded and performances were compared. The rate of success was high given moderate mock-boat motion and degraded with more aggressive motion. Tests were conducted with forward velocities from 0 to 3 m/s and moderate to high wave like motion. Over all tests for REM, the multirotor landed with an average accuracy of 12.7 cm. The methods described depart from common methods given that the only sensors involved for tracking the sea vessel were RTK GNSS receivers and inertial measurement units. Most current methods rely on computer vision, and can fail in poor lighting conditions, in the presence of ocean spray, and other scenarios. The given solutions do not fail under such conditions. The multirotor was equipped with a standard off-the-shelf autopilot, PX4, and the methods function with common control and estimation schemes. The two methods are capable of landing on relatively small landing pads, on the order of 1 m by 1 m, at sea using measurements from satellites thousands of kilometers away.

Precision landing capability is a necessary development that must take place before unmanned aircraft systems (UAS) can realize their full potential in today's modern society. Current multirotor UAS are heavily reliant on GPS data to provide positioning information for landing. While generally accurate to within several meters, much higher levels of accuracy are needed to ensure safe and trouble-free operations in several UAS applications that are currently being pursued. Examples of these applications include package delivery, automatic docking and recharging, and landing on moving vehicles. The specific problem we consider is that of precision landing of a multirotor unmanned aircraft on a small barge at sea---which presents several significant challenges. Not only must we land on a moving vehicle, but the vessel also experiences random rotational and translational motion as a result of waves and wind. Because maritime operations often span long periods of time, it is also desirable that precision landing can occur at any time---day or night.In this work we present a complete approach for precision shipboard landing and address each of the aforementioned challenges. Our method is enabled by leveraging an on-board camera and a specialized landing target which can be detected in light or dark conditions. Features belonging to the target are extracted from camera imagery and are used to compute image-based visual servoing velocity commands that lead to precise alignment between the multirotor and landing target. To enable the multirotor to match the horizontal velocities of the barge, an extended Kalman filter is used to generate feed-forward velocity reference commands. The complete landing procedure is guided by a state machine architecture that incorporates corrections to account for wind, and is also capable of quickly reacquiring the landing target in a loss event. Our approach is thoroughly validated through full-scale outdoor flight tests and is shown to be reliable, timely, and accurate to within 4 to 10 centimeters.

Approved for public release, distribution is unlimited / This thesis is part of an ongoing research conducted at the Naval Postgraduate School to achieve the autonomous shipboard landing of Unmanned Aerial Vehicles (UAV). Two main problems are addressed in this thesis. The first is to establish communication between the UAV's ground station and the Autonomous Landing Flight Control Computer effectively. The second addresses the design and implementation of an autonomous landing controller using classical control techniques. Device drivers for the sensors and the communications protocol were developed in ANSI C. The overall system was implemented in a PC104 computer running a real-time operating system developed by The Mathworks, Inc. Computer and hardware in the loop (HIL) simulation, as well as ground test results show the feasibility of the algorithm proposed here. Flight tests are scheduled to be performed in the near future. / Lieutenant Junior Grade, Mexican Navy