The use of unmanned aerial vehicle (UAV) applications has grown rapidly over the past decade with the introduction of low-cost microelectromechanical system (MEMS)-based sensors that measure angular velocity, gravity, and magnetic field, which are important for an object orientation determination. However, the use of low-cost sensors has also been limited because their readings are easily distorted by unwanted internal and/or external noise signals such as environmental magnetic disturbance, which lead to errors in attitude and heading estimation results. In an extended Kalman filter (EKF) process, this study proposes a method for mitigating the effect of magnetic disturbance on attitude determination by using a double quaternion parameters for representation of orientation states, which decouples the magnetometer from attitude computation. Additionally, an online measurement error covariance matrix tuning system was implemented to reject the impact of magnetic disturbance on the heading estimation. Simulation and experimental tests were conducted to verify the performance of the proposed methods in resolving the magnetic noise effect on attitude and heading. The results showed that the proposed method performed better than complimentary, gradient descent, and single quaternion-based EKF.

In this study, an improved model of the electric propulsion system is proposed in order to analyze the performance of large-size multicopter unmanned aerial vehicles. The main improvement of the proposed model is to reflect the armature reaction of the motor, which effectively explains the significant performance degradation in high-power operation. The armature reaction is a phenomenon, in which the main field flux is interfered by a magnetic flux and, as the size and output of the motor increase, the effect of armature reaction also rapidly increases. Therefore, the armature reaction must be considered for the optimal design and performance analysis of large-size multicopter platforms. The model proposed in this study includes several mathematical models for propellers, motors, electric speed controllers, and batteries, which are key components of the electric propulsion system, and they can calculate key performance data, such as thrust and torque and power consumption, according to given product specifications and input conditions. However, estimates of the armature reaction constants and heat profiles of motors need to be obtained in advance through experimental methods, since there is not yet enough data available in order to derive an estimation model. In conclusion, a comparison with the static thrust test of some commercial products confirmed that the proposed model could predict performance in the high-power operation of electric propulsion systems for large multicopter platforms, although some errors were noted.

Since unmanned aerial vehicle (UAV) for industries are operated in complex low altitude environments, planning feasible paths is a necessary feature to achieve mission goals. D* Lite is applicable for industrial complex that uncertainties exist. This paper focuses on 3D path planning for quadcopter UAV based on D* Lite. Simulation results show that the algorithm can be applied in cluttered static and dynamic environments including unknown obstacles. In addition, when some waypoints exist, the proposed algorithm is able to optimize the global path by determining visit order. Therefore, this study is expected to contribute to increase the application of UAVs in industrial fields.

This paper describes a study of performance prediction of an electric propulsion system for multirotor UAVs. The electric propulsion system consists of motors, propellers, batteries and speed controllers, and significantly affects performance characteristics of the platform. The performance of the electric propulsion system for multirotor UAVs was predicted using an analytical model derived from the characteristics of each component, operation experiments and statistical analyses. Ground performance tests and endurance flights were performed to verify the reliability of the proposed performance prediction method. A quadrotor platform was designed to demonstrate the parcel delivery service used in the endurance flight. From the result of verification tests, it was confirmed that the proposed method has a good agreement.