CN113124869A - Transformer substation inspection robot navigation positioning method based on particle filtering - Google Patents
Transformer substation inspection robot navigation positioning method based on particle filtering Download PDFInfo
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Abstract
The invention discloses a navigation and positioning method for a transformer substation inspection robot based on particle filtering, and provides a high-precision positioning method suitable for the transformer substation inspection robot aiming at the defects of the cruise and positioning algorithm of the conventional transformer substation inspection robot. The method specifically comprises the steps of optimizing a particle swarm set formed by positions of the cruise robot by using an improved multi-particle swarm optimization algorithm, comparing an optimized value with an observed actual value, fusing the optimized value by using a Kalman filtering algorithm, and continuously and iteratively updating the established particle weights, so that the problem of local particle degradation is effectively solved, the cruise robot is more accurately positioned, and the method has guiding significance for establishing a subsequent automatic cruise route of the robot. In actual test, the cruising robot can move along the navigation route more accurately when establishing the automatic navigation route, if the cruising robot deviates, the cruising robot can automatically correct and return to the preset track, and therefore the cruising robot has strong practical significance.
Description
Technical Field
The invention relates to a navigation positioning method suitable for a transformer substation inspection robot.
Background
For a long time, the cruising operation of the substation equipment depends on manual inspection, the defects of high labor intensity, low working efficiency, single detection means and the like exist, and the manually detected data cannot be transmitted to the management information system at the first time after the detection is finished. The existing popularized unattended transformer substation also needs equipment maintenance and overhaul, and with the development of the robot technology, the inspection and overhaul of the transformer substation equipment by using the mobile robot become possible. The transformer substation cruise machine can realize intelligent patrol of a transformer substation to a certain extent, so that the patrol efficiency is improved, and meanwhile, the operation safety risk of patrol personnel is effectively reduced.
The intelligent inspection robot of present transformer substation mainly aims at the outdoor equipment of transformer substation, and the indoor power equipment of transformer substation on the one hand needs to have sufficient reliability, and on the other hand also needs in time to detect so that the discovery and overhaul have the equipment of potential hidden danger, ensures electric power system normal operating. Real-time detection of indoor equipment is of great significance to guarantee reliable and safe operation of the indoor equipment. The robot system adopting the combined track can conveniently and flexibly carry out real-time detection on indoor equipment, know the running state of the equipment and feed back the running state of the equipment in time.
In the cruising process of the transformer substation cruising robot, the most key element is to establish a local environment map, and the automatic cruising function is finally realized according to the combination of the environment map and the surrounding environment data. In order to establish a local environment map, the cruise robot needs to be accurately positioned, and the positioning technology is the foundation of the technology for establishing the local environment map.
In the present case, all existing positioning methods place a known obstacle in a known environment, then specify the coordinates of the obstacle, plan the cruising route of the robot, and determine the position of the cruising robot by the difference between the coordinates of the obstacle and the coordinates of the cruising route in real time. The method is characterized in that a common method is Kalman filtering, the calculation amount is large, and errors are required to obey Gaussian distribution, so that the robot cruise positioning algorithm at home and abroad is related to particle filtering, the particle lean problem is caused by using the particle filtering, and the particle lean problem refers to the phenomenon that in the particle filtering algorithm, along with continuous iteration of particles, the weight of a plurality of particles is reduced or even disappears, so that the particle overlapping phenomenon occurs, and therefore, the problem of particle lean is solved by using fewer particles has important meaning.
Disclosure of Invention
The invention provides a high-precision positioning method suitable for a substation inspection robot, aiming at the defects of the cruise positioning algorithm of the conventional substation inspection robot.
The method comprises the following implementation steps:
1.1 initializing working conditions of the inspection robot of the transformer substation, including a motion path of the inspection robot, initial motion coordinates, end coordinates and a position seat of an obstacleAccording to the method, the moving distance d of the cruise robot in the delta t time is determined by the formula d-2 pi rn/p according to the rotation number n of the photoelectric sensor of the wheel of the cruise robot in a certain time delta t and the resolution p of the photoelectric sensor, and the moving distance d of the left wheel and the right wheel of the cruise robot is1，d2From the formula
The movement increment delta D and the rotation angle increment delta theta of the cruise robot can be determined, wherein r is the wheel radius of the cruise robot, l is the wheel distance, and the cruise robot is obtained through the formula
The motion radius R of the cruise robot can be known, and the position and the posture of the cruise robot at the next moment can be obtained according to the formula:
x in the above formulat，yt，θtThe parameter represents the abscissa, the ordinate and the angle coordinate of the cruise robot under a spherical coordinate system, and N (t) is the external Gaussian distribution to which the particle filter must obey, such as the friction between wheels and the ground and other factors.
1.2 estimating the coordinates of the obstacle by the following formula
The barrier infrared sensor is positioned at the front end of the cruising robot (x)m，ym) As obstacle coordinates, (x)s，ys) Is the center coordinate of the sensor, L is the distance between the sensor and the geometric center of the cruise robot, and the value can be determined by
The coordinate calculation formula of the obstacle is obtained as follows:
can find out
ρtThe distance between the position of the obstacle and the center position of the robot,
the angle at which the obstacle is detected for the cruise robot sensor.
1.3, regarding the position of the cruise robot at the time t as a particle set, updating the particle set at the time t +1 according to a calculation formula (I) of the position of the cruise robot at the next time in the step (1), optimizing the position of the cruise robot by using a fusion enhanced particle swarm algorithm, and determining the weight W of the particle set.
And 1.4, predicting the actual value and the predicted value of the position of the robot by using a Kalman filtering algorithm according to the estimation equation of the position of the cruise robot in the step (2) and the optimized value of the position of the cruise robot in the step (3), and finally finishing the accurate positioning of the robot.
Further, in step 1.3, the weight iteration update formula of the particle set is
And t is the current time.
Further, when W is determined in the previous stept+1When the weight is smaller than the preset value in the step 1.3, the system returns to the step 1.1 to restart the position determination, otherwise, the cruise robot positioning is finished.
Compared with the prior art, the invention has the following advantages
The invention optimizes the particle swarm set formed by the positions of the cruise robots by using an improved multi-particle swarm optimization algorithm, compares an optimized value with an observed actual value, fuses the optimized value by using a Kalman filtering algorithm, and continuously and iteratively updates the established particle weights, thereby effectively solving the problem of local particle degradation, ensuring that the cruise robots are positioned more accurately, and having instructive significance for the establishment of subsequent automatic cruise routes of the robots.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, the present invention is embodied as follows.
Step 1, initializing working conditions of the substation inspection robot, including a movement path of the inspection robot, initial movement coordinates, end point coordinates and position coordinates of an obstacle, determining a moving distance d of the cruise robot in a certain time delta t by a formula d 2 pi rn/p according to the number n of rotations of a photoelectric sensor of wheels of the inspection robot in the certain time delta t and the resolution p of the photoelectric sensor, wherein the moving distance d of left and right wheels of the cruise robot is d1，d2From the formula
The movement increment delta D and the rotation angle increment delta theta of the cruise robot can be determined, wherein r is the wheel radius of the cruise robot, l is the wheel distance, and the cruise robot is obtained through the formula
Knowing the motion radius R of the cruise robot, the position and the posture of the cruise robot at the next moment can be obtained according to the following formula:
x in the above formulat，yt，θtThe parameter represents the abscissa, the ordinate and the angle coordinate of the cruise robot under a spherical coordinate system, and N (t) is the external Gaussian distribution to which the particle filter must obey, such as the friction between wheels and the ground and other factors.
Step 2, estimating the coordinates of the obstacle by the estimation formula
The obstacle infrared sensor is positioned at the front end of the cruising robot (x)m，ym) Is composed ofCoordinates of the obstacle, (x)s，ys) Is the center coordinate of the sensor, L is the distance between the sensor and the geometric center of the cruise robot, and the value can be determined by
The coordinate calculation formula of the obstacle is obtained as follows:
can find out
ρtThe distance between the position of the obstacle and the center position of the robot,
the angle at which the obstacle is detected for the cruise robot sensor.
And 3, regarding the position of the cruise robot at the time t as a particle set, updating the particle set at the time t +1 according to a calculation formula (I) of the position of the cruise robot at the next time in the step (1), optimizing the position of the cruise robot by using a fusion enhanced particle group algorithm, and determining the weight W of the particle set.
And 4, predicting the actual value and the predicted value of the position of the robot by using a Kalman filtering algorithm according to the estimation equation of the position of the cruise robot in the step 2 and the optimized value of the position of the cruise robot in the step 3, and finally finishing the accurate positioning of the robot.
Further, in step 1.3, the weight iteration update formula of the particle set is
And t is the current time.
Further, when W is determined in the previous stept+1When the weight is smaller than the preset value in the step 1.3, the system returns to the step 1.1 to restart the position determination, otherwise, the cruise robot positioning is finished.
Effects of the implementation
Through practical tests, the cruising robot can move along the navigation route more accurately when the automatic navigation route is established, and if the cruising robot deviates, the cruising robot can automatically correct and return to the preset track, so that the cruising robot has strong practical significance.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (3)
1. A transformer substation inspection robot navigation positioning method based on particle filtering is characterized by comprising the following steps:
(1) initializing working conditions of a transformer substation inspection robot, including a movement path, an initial movement coordinate, a final movement coordinate and a position coordinate of an obstacle of the inspection robot, determining a moving distance d of the cruise robot in a certain time delta t by a formula d-2 pi rn/p according to the rotation number n and photoelectric sensor resolution p of a photoelectric sensor of wheels of the inspection robot in the certain time delta t, wherein the moving distance d of left and right wheels of the cruise robot is d1，d2From the formula
The movement increment delta D and the rotation angle increment delta theta of the cruise robot can be determined, wherein r is the wheel radius of the cruise robot, l is the wheel distance, and the cruise robot is obtained through the formula
Knowing the motion radius R of the cruise robot, the position and the posture of the cruise robot at the next moment can be obtained according to the following formula:
x in the above formulat，yt，θtRepresenting a cruising robot inThe abscissa, ordinate and angular coordinate under the spherical coordinate system, n (t), are the external gaussian distributions to which the particle filter must comply, such as the friction between the wheel and the ground.
(2) Estimating the coordinates of the obstacle by the following estimation formula
The barrier infrared sensor is positioned at the front end of the cruising robot (x)m，ym) As obstacle coordinates, (x)s，ys) Is the center coordinate of the sensor, L is the distance between the sensor and the geometric center of the cruise robot, and the value can be determined by
The coordinate calculation formula of the obstacle is obtained as follows:
can find out
ρtThe distance between the position of the obstacle and the center position of the robot,
the angle at which the obstacle is detected for the cruise robot sensor.
(3) And (2) regarding the position of the cruise robot at the time t as a particle set, updating the particle set at the time t +1 according to a calculation formula (I) of the position of the cruise robot at the next time in the step (1), optimizing the position of the cruise robot by using a fusion enhanced particle swarm algorithm, and determining the weight W of the particle set.
(4) And (3) predicting the actual value and the predicted value of the position of the robot by using a Kalman filtering algorithm according to the position estimation equation of the cruise robot in the step (2) and the position optimized value of the cruise robot in the step (3), and finally finishing accurate positioning of the robot.
2. Root of herbaceous plantThe transformer substation inspection robot navigation and positioning method based on the particle filtering according to claim 1, characterized in that: the weight iteration updating formula of the particle set in the step (3) is
And t is the current time.
3. The transformer substation inspection robot navigation and positioning method based on the particle filtering is characterized in that: when determined Wt+1When the weight is smaller than the weight in the step (3), the system returns to the step (1) to restart the position determination, otherwise, the cruise robot positioning is finished.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103472828A (en) * | 2013-09-13 | 2013-12-25 | 桂林电子科技大学 | Mobile robot path planning method based on improvement of ant colony algorithm and particle swarm optimization |
| CN103487047A (en) * | 2013-08-06 | 2014-01-01 | 重庆邮电大学 | Improved particle filter-based mobile robot positioning method |
| CN105806345A (en) * | 2016-05-17 | 2016-07-27 | 杭州申昊科技股份有限公司 | Initialized positioning method for transformer substation inspection robot laser navigation |
| WO2018032933A1 (en) * | 2016-08-17 | 2018-02-22 | 国网山东省电力公司电力科学研究院 | Substation inspection robot navigation control system and method |
| CN109975817A (en) * | 2019-04-12 | 2019-07-05 | 南京工程学院 | A kind of Intelligent Mobile Robot positioning navigation method and system |
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- 2019-12-30 CN CN201911426553.2A patent/CN113124869A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103487047A (en) * | 2013-08-06 | 2014-01-01 | 重庆邮电大学 | Improved particle filter-based mobile robot positioning method |
| CN103472828A (en) * | 2013-09-13 | 2013-12-25 | 桂林电子科技大学 | Mobile robot path planning method based on improvement of ant colony algorithm and particle swarm optimization |
| CN105806345A (en) * | 2016-05-17 | 2016-07-27 | 杭州申昊科技股份有限公司 | Initialized positioning method for transformer substation inspection robot laser navigation |
| WO2018032933A1 (en) * | 2016-08-17 | 2018-02-22 | 国网山东省电力公司电力科学研究院 | Substation inspection robot navigation control system and method |
| CN109975817A (en) * | 2019-04-12 | 2019-07-05 | 南京工程学院 | A kind of Intelligent Mobile Robot positioning navigation method and system |
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