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Combined agricultural robot navigation information fusion method based on rank filtering

A technology of integrated navigation and fusion method, which is applied in the field of information fusion of integrated navigation of agricultural robots based on rank filter

Active Publication Date: 2017-05-31
ANHUI SCI & TECH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The Kalman filter is generally applicable to the situation where the system is linear and the statistical characteristics of the noise obey the Gaussian distribution and is completely known; the traditional extended Kalman filter (extended Kalman filter, EKF) and its improved method can only deal with weak nonlinear systems and Gaussian noise The state estimation problem under the condition

Method used

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  • Combined agricultural robot navigation information fusion method based on rank filtering
  • Combined agricultural robot navigation information fusion method based on rank filtering
  • Combined agricultural robot navigation information fusion method based on rank filtering

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Embodiment

[0112] For verifying the feasibility of the present invention, emulation is carried out under Matlab, and filtering initial value and emulation parameter are set as follows:

[0113] The random constant drift of the gyro is 1° / h, and the random drift of white noise is 0.1° / h;

[0114] Accelerometer bias error is 1mg, white noise random drift is 0.1mg;

[0115] The MEMS strapdown inertial navigation system has an initial horizontal attitude angle error of 1° and a heading angle error of 3°;

[0116] The initial speed is 0m / s, and the initial speed error is 0m / s;

[0117] The initial position is 118° east longitude, 32° north latitude, 100m in altitude, and the initial position error is 0m;

[0118] RTK GPS position measurement accuracy is 0.1 meters;

[0119] MEMS strapdown inertial navigation system sampling period is 1ms, RTK GPS sampling period is 0.1s;

[0120] Maximum land unevenness level Maximum satellite signal fading level ξ max = 1, then the model number r in t...

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Abstract

The invention discloses a combined agricultural robot navigation information fusion method based on rank filtering. Interactive multi-model filtering is employed as an algorithm structure, an initial value of present-moment filtering is calculated based on an output result of each model filter at a previous moment, a rank filter is taken as a sub filter of model filtering steps to carry out filtering, a residual error and a residual error covariance of model filtering are utilized to update model probability, weight fusion of each rank filter estimate is carried out to acquire an interaction output result. Combined navigation data processing and resolution work are accomplished by a navigation computer according to a combined navigation system filtering model and an algorithm flow. The method is advantaged in that filtering precision of the combined navigation system under the complex environment can be improved, and an agricultural robot is made to have advantages of wide application scope and strong interference prevention capability.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor information fusion, and in particular relates to a rank filter-based combined navigation information fusion method for agricultural robots. Background technique [0002] Agricultural robots work in complex open and unstructured farmland environments (obstacles, soft and uneven land, occlusion, light changes, etc.), it needs to sense the surrounding environment through its own sensors, and understand the information it perceives. process, obtain its own position and attitude information, and then perform path planning and navigation control in real time to complete various tasks. Therefore, navigation technology is the key to realize the automatic operation of agricultural robots. The Kalman filter is generally applicable to the situation where the system is linear and the statistical characteristics of the noise obey the Gaussian distribution and is completely known; the traditional extended...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S19/49
Inventor 王磊李双喜张玉山王岳
Owner ANHUI SCI & TECH UNIV
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