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Nonlinear model predictive control method applied to visual servo

A nonlinear model and predictive control technology, applied in the field of navigation and control, can solve problems such as correcting navigation deviations, achieve the effects of improving convergence speed, facilitating the solution of optimal solutions, and controlling efficiency advantages

Pending Publication Date: 2022-04-19
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional visual servo control method cannot ensure that the feature code is always within the field of view of the camera during the control process (Nazemzadeh P, Fontanelli D, Macii D, et al. Indoor localization of mobile robots through QR code detection and dead reckoning data fusion[J] .IEEE / ASME Transactions On Mechatronics,2017,22(6):2588-2599.) (Wang Jiaen, Xiao Xianqiang. Research on compound navigation method of mobile robot based on QR code visual positioning[J].Journal of Instrumentation,2018,39( 8):9.), if the control input is too large, after the unmanned vehicle cannot detect the signature, it will not be able to correct the navigation deviation through visual servoing

Method used

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  • Nonlinear model predictive control method applied to visual servo
  • Nonlinear model predictive control method applied to visual servo
  • Nonlinear model predictive control method applied to visual servo

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] like figure 1 A nonlinear model predictive control method applied to visual servoing is shown, including the following steps:

[0069] 1) When the four-Mecanum wheel unmanned vehicle passes through the AprilTag code, the pose of the unmanned vehicle is determined according to the image captured by the bottom camera. The specific process is as follows:

[0070] like figure 2 As shown, the AprilTag code is a visual benchmark library that is widely used in robotic systems due to its ability to store information and easy identification. When the four-Mecanum wheel unmanned vehicle moves to the vicinity of the AprilTag code, the camera located at the bottom of the unmanned vehicle can capture the AprilTag code, and then use the findcounters function in the Opencv image processing library to obtain the boundary of the AprilTag code, and then calculate the average of the boundaries. Get the midpoint of the AprilTag and use it as the coordinates of the bottom camera in the A...

Embodiment 2

[0106] The present invention is also applicable to the visual servoing of the differential wheel unmanned vehicle. Unlike the four Mecanum wheel unmanned vehicle, which can move in any direction, the two power wheels of the differential wheel unmanned vehicle are generally located on the left and right sides of the unmanned vehicle site. side, respectively apply different rotational speeds to the two wheels to achieve the steering of the ground. like Figure 4 As shown, the clockwise rotation is set to a square, and its kinematic model is as follows:

[0107]

[0108] where θ is the heading angle of the unmanned vehicle, is the derivative of the position and attitude of the unmanned vehicle, v is the straight speed of the unmanned vehicle with differential wheels, and ω is the angular velocity of the unmanned vehicle with differential wheels. The distance difference between the unmanned vehicle in the world coordinate system and the target point defined by the four-Mecan...

Embodiment 3

[0114] In some industrial scenarios, the unmanned vehicle needs to move from one target point to another. For this reason, we design the current map to consist of multiple nodes, and each node has a corresponding AprilTag code to record its location information. , when the unmanned vehicle starts from the starting node and passes through each node, the under-vehicle camera can obtain its own position information, judge the direction of the next node in the established route, and use the AprilTag code to realize the steering and node of the unmanned vehicle. movement between.

[0115] Image 6 It shows the distribution of AprilTag codes in a 4×4 meter map, where the distance between the codes is 2 meters, and the unmanned vehicle needs to move from the starting node 3 to the target node 7. In this process, when the unmanned vehicle detects the AprilTag code, in addition to using the visual servoing and nonlinear model predictive control algorithm to control the unmanned vehicl...

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Abstract

The invention discloses a nonlinear model prediction control method applied to visual servo, and the method comprises the steps: enabling an unmanned vehicle to be provided with four Mecanum wheels, enabling the unmanned vehicle to move in all directions through the special structures of the Mecanum wheels, and improving the flexibility of the unmanned vehicle; a vertically downward camera is mounted at the bottom of the unmanned vehicle and used for detecting feature codes on the ground and assisting positioning and navigation of the unmanned vehicle; nonlinear model predictive control and a kinematic model of the unmanned vehicle are combined, and the unmanned vehicle hierarchical control method based on visual servo is established and comprises linear model predictive control and PID control. An external nonlinear model predicts, controls and calculates the speed value of the unmanned vehicle, controls the unmanned vehicle to move to the position over the feature code under the condition that various constraints are met, and ensures that the feature code is always in the view field of the bottom camera in the control process; the internal PID controller is responsible for converting a speed instruction into the rotating speed of each motor and controlling the unmanned vehicle to move according to the instruction.

Description

technical field [0001] The invention belongs to the field of navigation and control, and relates to a nonlinear model predictive control method applied to visual servoing. Background technique [0002] In order to achieve the strategic goal of a manufacturing powerhouse, the use of large-scale robots in factories and warehouses to replace laborious tasks such as sorting and handling of goods will become a future development trend. With the development of Cyber ​​Physical System (CPS) and Information and Communication Technology (ICT), more and more smart factories are equipped with unmanned vehicles to sort goods and transport designated goods from the current area to the target area for processing or Centralized warehousing to improve the operational efficiency of the factory. [0003] Most autonomous vehicles rely on different on-board sensors to locate and navigate, such as lidar, cameras, and magnetic sensors. In modern factories, for complex transportation and sorting...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0223
Inventor 谢巍杨启帆刘彦汝杨奕斌廉胤东周雅静
Owner SOUTH CHINA UNIV OF TECH