Abstract
This paper presents a dynamic-model-aided navigation (DMAN) method for a small multirotor unmanned aerial vehicle. The method can be used for temporary navigation in cases where location and velocity measurements from external sources, e.g., global navigation satellite system, are missing or unreliable. The method combines proprioceptive measurements with a Kalman filter through a dynamic model to obtain the velocity and location of the vehicle. Acceleration and angular rate measurements from an inertial measurement unit, altitude measurements from a barometric altimeter, and proprioceptive measurements of the revolution speed of propellers are considered in the method. The dynamic model of the aerial vehicle relates the linear and angular velocities of the vehicle with the revolution speed of the propeller. The revolution speed is first converted into a thrust force and torque and then included in the model. The model avoids the singularity problem and describes processes and measurements in a three-dimensional space by representing attitude using quaternions instead of Euler angles. This study details two implementations of the DMAN method: extended Kalman filter (EKF) and unscented Kalman filter (UKF). The dynamic model is incorporated into the process model and measurement model of the implementations. A model that converts the revolution speed of propellers to thrust force and torque has been derived from unmanned aerial vehicle flight experiments. Experiments that implement the proposed method for quadrotor navigation verify the performance and state the limitations of the DMAN method. Compared with previous methods, the proposed method extends the application of DMAN to the three-dimensional space and obtains location and velocity measurements in a world coordinate system.
Highlights
Navigation is indispensable for autonomous flight of unmanned aerial vehicles (UAVs)
Experiments verify that if 3D-dynamic-model-aided navigation (DMAN) is used with the Global navigation satellite system (GNSS) measurement, it performs better than ecl-extended Kalman filter (EKF) in estimating the location and velocity
The 2D-DMAN works under the constraint of level flight, 3D-DMAN is applicable with no limitation in flight space
Summary
Navigation is indispensable for autonomous flight of unmanned aerial vehicles (UAVs). This paper proposes a DMAN method and verifies its performance by comparing the test results with those of other methods Another approach for navigation in GNSS-denied environments is use of tightly coupled GNSS/INS navigation product, such as NovAtel SPAN. Researches on DMAN can be classified in terms of the following six perspectives: navigation space, attitude representation in problem formulation, UAV type for the proposed method, estimation variables, verification approaches, and additional sensors required for implementation. This paper proposes a method that estimates position, attitude and velocity of a multirotortype UAV in three-dimensional (3D) space using a quaternion representation and verifies feasibility through experiments. Wang et al used the dynamic model for the navigation of multirotor UAVs [20] in 3D space Their method is based on the rationale that the body velocity is related to the accelerometer measurement.
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