Collaborative positioning method via GPS/INS and RS/MO multi-source data fusion in multi-target navigation

Liu, Rui; Greve, Klaus; Cui, Pengyu; Jiang, Nan

doi:10.1080/00396265.2021.1883962

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…Multisensor fusion technology is a comprehensive and advanced decision-making process based on measurement results. In a multisensor system, since the information provided by different sensors may be different, they are mutually supportive or complementary [15]. Multisensor fusion technology utilizes multiple sensor resources to integrate redundant multisensor information in time and space, while eliminating errors or unreliable information, so as to obtain a consistent description or explanation of the object described.…”

Section: Features Of Multisensor Information Fusionmentioning

confidence: 99%

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

Liu

Wang

2021

Journal of Sensors

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With the advent of robots combined with artificial intelligence, robots have become an indispensable part of production and life. Especially in recent years, the collaboration between humans and machines has become a research trend in the field of robotics, with high work efficiency and flexibility. The advantages of safety and stability make intelligent robots the best choice for the current industrial and service industries with high labor intensity and hazardous working environments. This paper is aimed at studying the software design of an intelligent sensor robot system based on multidata fusion. In this paper, through the needle robot’s high precision requirements and the problem of fast response, a path design method based on the ant colony optimization (ACO) algorithm is proposed. Path planning is performed by intelligent robots for obstacle avoidance experiments, while global optimization is performed by the ant colony optimization (ACO) algorithm. For adaptive functions including obstacle reduction and path information length, the safest and shortest path is finally achieved through the ant colony optimization (ACO) algorithm. The experimental results show that using the ant colony optimization algorithm to perform simulation experiments and preprocessing operations on the data collected by the sensor can improve the accuracy and effectiveness of the data. The ant colony algorithm performs fusion and path planning, and on the basis of ensuring accuracy, it can speed up the convergence speed. Through the data analysis of obstacle avoidance experiments of intelligent robots, it can be concluded that it is very necessary for intelligent robots to install ultrasonic sensors and infrared sensors in obstacle avoidance, because the error between the test distance of the ultrasonic sensor and the infrared sensor and the actual distance is 0.001.

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Section: Features Of Multisensor Information Fusionmentioning

confidence: 99%

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

Liu

Wang

2021

Journal of Sensors

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“…For fully autonomous flight mode, from the perspective of mission necessity, it is necessary to install navigation equipment on the UAV. BDS (Beidou Satellite Positioning System) and INS (Inertial Navigation System), as two commonly used navigation systems in UAVs [5], can make full use of their advantages and compensate each other. In BDS/INS integrated navigation system, Kalman filter technology is usually used to calibrate position estimation error.…”

Section: Introductionmentioning

confidence: 99%

NASRUKF UAV based on combined navigation algorithm

Wang

Chen

et al. 2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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Combined navigation system is a navigation and positioning system composed of inertial navigation system and BeiDou satellite navigation system. Most of the navigation system models in combined navigation are nonlinear, but the traditional Kalman filtering algorithm is not well applied to nonlinear equations, and the Unscented Kalman filtering algorithm and Extended Kalman filtering algorithm which can be applied to nonlinear equations are constant in the fusion process of noise, so it will cause filtering divergence. In this paper, on the basis of Unscented Kalman filtering algorithm proposed will introduce the square root traceless Kalman filter algorithm, the algorithm through QR decomposition and Cholesk decomposition, the Sage-Husa algorithm combined with Square Root Unscented Kalman Filter algorithm, directly calculate the state error covariance matrix prediction and estimation of the square root factor, maintain the stability of the filtering, through practice proved that compared to Kalman filtering .The Nonlinear adaptive regression square root Kalman filter filter has a good navigation and positioning function, as the filtering is more convergent and the position accuracy can be within 5m, the speed error can be between 0.5m/s-1m/s. Compared with KF algorithm, the position error is increased by about 75%, and the speed error is increased by about 50%.

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Hybrid noise removal to improve the accuracy of inertial sensors using lifting wavelet transform optimized by genetic algorithm

Alaeiyan,

Mosavi,

Ayatollahi

2023

Alexandria Engineering Journal

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Collaborative positioning method via GPS/INS and RS/MO multi-source data fusion in multi-target navigation

Cited by 9 publications

References 15 publications

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

NASRUKF UAV based on combined navigation algorithm

Hybrid noise removal to improve the accuracy of inertial sensors using lifting wavelet transform optimized by genetic algorithm

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