A main problem in autonomous vehicles in general, and in UAV in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an AHRS based on the UKF using the TRIAD algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the EKF . The paper presents field experiment results using a real fixed-wing UAV . The results show good real-time performance with low computational cost in a microcontroller.
A well-known problem with gyrometers is bias. Therefore, different sensors have to be used to correct these biases. For instance, in @cite_6 they are corrected using three-axis accelerometers. In @cite_13 the use of eight accelerometers in a new configuration is proposed for measuring angular velocities in small UAV . Different approaches rely on magnetometers @cite_14 , to be able to estimate yaw angle in helicopters, or in GPS @cite_24 to estimate the position as well the attitude in fixed-wing UAV . Alternatively, other papers propose not to use gyrometers at all in conventional aircrafts, but several GPS receivers instead @cite_28 .
@cite_14 @cite_28 @cite_6 @cite_24 @cite_13
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Attitude determination systems that use inexpensive sensors and are based on computationally efficient and robust algorithms are indispensable for real-time vehicle navigation, guidance and control applications. This paper describes an attitude determination system that is based on two vector measurements of non-zero, non-colinear vectors. The algorithm is based on a quaternion formulation of Wahba's (1966) problem, whereby the error quaternion (q sub e ) becomes the observed state and can be cast into a standard linear measurement equation. Using the Earth's magnetic field and gravity as the two measured quantities, a low-cost attitude determination system is proposed. An iterated least-squares solution to the attitude determination problem is tested on simulated static cases, and shown to be globally convergent. A time-varying Kalman filter implementation of the same formulation is tested on simulated data and experimental data from a maneuvering aircraft. The time-varying Kalman filter implementation of this algorithm is exercised on simulated and real data collected from an inexpensive triad of accelerometers and magnetometers. The accelerometers in conjunction with the derivative of GPS velocity provided a measure of the gravitation field vector and the magnetometers measured the Earth's magnetic field vector. Tracking errors on experimental data are shown to be less than 1 degree mean and standard deviation of approximately 11 degrees in yaw, and 3 degrees in pitch and roll. Best case performance of the system during maneuvering is shown to improve standard deviations to approximately 3 degrees in yaw, and 1.5 degrees in pitch and roll. In this paper, a low-cost attitude estimation system is introduced. The system is developed with MEMS sensors including rate gyros and accelerometers. Composition and principle of the system are described. The rigid body kinematics is modeled with quaternion, such that eliminates attitude estimation singularities. The real-time Kalman filter is designed. Experiments were conducted in both static and dynamic conditions. The experimental results demonstrate that the algorithm and hardware are feasible and suitable for the application where critical accuracy and real-time requirements are needed. This paper presents a framework for the automation of a small UAV using a low cost sensor suite, MNAV, and an embedded computing platform, Stargate, which together provide a complete avionics package for aerial robotic applications. In order to provide a complete INS solution (i.e., attitude, velocity, position, and biases), an extended Kalman filter algorithm is developed and implemented in real-time. A devised control strategy utilizes multiple PID loops with a hierarchy enabling simple attitude stabilization to full waypoint navigation. The developed ground station unit, a laptop computer, communicates with the avionics package via 802.11b WiFi, displays the aircraft critical information, provides in-flight PID gain tunings, and uploads waypoints through a simple GUI. The system is installed in an off-the-shelf delta-wing R C aircraft and demonstrates its performance for aerial robotic applications The scheme to calculate attitude angle of UAV was introduced to replace high-priced angular velocity gyroscope, so as to lower the cost of UAV. A new eight-accelerometer configuration for attitude angle calculation of UAV was proposed. It can optimize algorithm, remove accumulative error in process of calculation and improve calculation precision through multi-sensor redundant information. The digital simulation results show that attitude angle calculation can satisfy current requirements and has low calculation error and time accumulation, which is of practical significance to research on inertia device.