Multi-Stage Fusion Algorithm for Estimation of Aerodynamic Angles in Mini Aerial Vehicle

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

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UAV Path Planning Based on Multi-Stage Constraint Optimization

Drones ◽

10.3390/drones5040144 ◽

2021 ◽

Vol 5 (4) ◽

pp. 144

Author(s):

Yong Shen ◽

Yunlou Zhu ◽

Hongwei Kang ◽

Xingping Sun ◽

Qingyi Chen ◽

...

Keyword(s):

Path Planning ◽

Three Dimensional ◽

Evolutionary Process ◽

Constraint Processing ◽

Multi Stage ◽

Planning Ability ◽

Path Constraints ◽

Aerial Vehicle ◽

Multi Level ◽

Processing Mechanism

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

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An Integrated Adaptive Kalman Filter for High-Speed UAVs

Applied Sciences ◽

10.3390/app9091916 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1916 ◽

Cited By ~ 5

Author(s):

Tiantian Huang ◽

Hui Jiang ◽

Zhuoyang Zou ◽

Lingyun Ye ◽

Kaichen Song

Keyword(s):

Kalman Filter ◽

High Speed ◽

Sensor Data ◽

Adaptive Kalman Filter ◽

Fusion Algorithm ◽

Multi Sensor Data Fusion ◽

Aerial Vehicle ◽

Robust Adaptive ◽

Tracking Filter ◽

Filter Algorithms

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

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Stepwise fusion algorithm with dual correction for multi-sensor navigation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418779931 ◽

2018 ◽

Vol 15 (3) ◽

pp. 172988141877993 ◽

Cited By ~ 2

Author(s):

Rong Wang ◽

Zhi Xiong ◽

Jianye Liu ◽

Yuxuan Cao

Keyword(s):

High Altitude ◽

Unmanned Aerial Vehicle ◽

Error Estimation ◽

Attitude Determination ◽

Vehicle Navigation ◽

Fusion Algorithm ◽

Aerial Vehicle ◽

Determination System ◽

Long Endurance ◽

Sensor Navigation

In high-altitude, long-endurance unmanned aerial vehicles, a celestial attitude determination system is used to enhance the inertial navigation system (INS)/global positioning system (GPS) to achieve the required attitude performance. The traditional federal filter is not applicable for INS/GPS/celestial attitude determination system information fusion because it does not consider the mutually coupled relationship between the horizontal reference error in the celestial attitude determination system and the navigation error; this limitation results in reduced navigation accuracy. This article proposes a novel stepwise fusion algorithm with dual correction for multi-sensor navigation. Considering the horizontal reference error, the celestial attitude determination system measurement model is constructed and the issues involved in applying the federal filter are discussed. Then, preliminary error estimation and horizontal reference compensation are added to the navigation architecture. In addition, a sequential update strategy is derived to estimate the attitude error with the compensated celestial attitude determination system based on the preliminary estimation. A stepwise correction filtering algorithm with interactive preliminary and sequential updates that can effectively fuse celestial attitude determination system measurements with the INS/GPS is constructed. High-altitude, long-endurance unmanned aerial vehicle navigation in a remote sensing task is simulated to verify the performance of the proposed method. The simulation results demonstrate that the horizontal reference error is effectively compensated, and the attitude accuracy is significantly improved after stepwise error estimation and correction. The proposed method also provides a novel multi-sensor integrated navigation architecture with mutually coupled errors; this architecture is beneficial in unmanned aerial vehicle navigation applications.

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Estimation of Aerodynamic Angles in a Mini Aerial Vehicle under Turbulent Atmosphere

AIAA Atmospheric Flight Mechanics Conference ◽

10.2514/6.2011-6517 ◽

2011 ◽

Cited By ~ 3

Author(s):

C. Ramprasadh ◽

Hemendra Arya

Keyword(s):

Turbulent Atmosphere ◽

Aerodynamic Angles ◽

Aerial Vehicle

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New Fusion Algorithm-Reinforced Pilot Control for an Agricultural Tricopter UAV

Mathematics ◽

10.3390/math8091499 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1499

Author(s):

Huu Khoa Tran ◽

Juing-Shian Chiou ◽

Viet-Hung Dang

Keyword(s):

Attitude Control ◽

Military Service ◽

Fitness Function ◽

Integrated Design ◽

Absolute Error ◽

Industrial Applications ◽

System Response ◽

Optimization Strategy ◽

Fusion Algorithm ◽

Aerial Vehicle

Currently, fuzzy proportional integral derivative (PID) controller schemes, which include simplified fuzzy reasoning decision methodologies and PID parameters, are broadly and efficaciously practiced in various fields from industrial applications, military service, to rescue operations, civilian information and also horticultural observation and agricultural surveillance. A fusion particle swarm optimization (PSO)–evolutionary programming (EP) algorithm, which is an improved version of the stochastic optimization strategy PSO, was presented for designing and optimizing controller gains in this study. The mathematical calculations of this study include the reproduction of EP with PSO. By minimizing the integral of the absolute error (IAE) criterion that is used for estimating the system response as a fitness function, the obtained integrated design of the fusion PSO–EP algorithm generated and updated the new elite parameters for proposed controller schemes. This progression was used for the complicated non-linear systems of the attitude-control pilot models of a tricopter unmanned aerial vehicle (UAV) to demonstrate an improvement on the performance in terms of rapid response, precision, reliability, and stability.

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