CN105759825A - Algorithm for positioning control of automatic guided vehicle (AGV) robot based on fuzzy proportion integration differentiation (PID) - Google Patents

Abstract

The invention discloses an algorithm for positioning control of an automatic guided vehicle (AGV) robot based on fuzzy proportion integration differentiation (PID). The algorithm comprises the following steps: a step S1 of operating environment presetting; a step S2 of establishment of an environmental map; a step S3 of AGV robot positioning; a step S4 of AGV robot line operation; and a step S5 of AGV robot operation line correction. The algorithm is more suitable for control over the AGV robot; through a mode of adding the fuzzy PID control, the response speed and control operation accuracy of the AGV robot can be improved, and the AGV robot positioning accuracy is higher.

Description

A kind of AGV robot localization control algolithm based on fuzzy

Technical field

The present invention relates to robot control algorithm field, be specially a kind of AGV robot localization control algolithm based on fuzzy.

Background technology

AGV is namely: AutomatedGuidedVehicle is called for short AGV, current modal application such as AGV transfer robot or AGV dolly, main function concentrates on automatic logistics and removes transhipment, AGV transfer robot is automatically by goods transportation to appointed place by special ground marker navigation, modal guidance mode is that magnetic stripe guides, laser aiming；Current most advanced autgmentability is the most by force guided by the ultrahigh frequency RFID of Mick power U.S. scientific and technological development.The mode that magnetic stripe guides is conventional is also the mode that cost is minimum, but website is provided with certain limitation and place decoration style is had certain impact；Laser aiming cost is the highest also relatively high so generally not adopting to site requirements；RFID guides moderate cost, and its advantage is that guidance accuracy is high, and website arranges and more convenient meets the most complicated website layout, place entirety is fitted up environment without impact, and secondly RFID high security stability is also magnetic stripe navigation and laser navigation mode does not possess.

The Orientation control algorithm of existing AGV robot can not control the route of robot accurately, is therefore beneficial to the accurate control of robot and existing Orientation control algorithm, and the precision of location is relatively low.

Summary of the invention

It is an object of the invention to provide a kind of AGV robot localization control algolithm based on fuzzy, with the problem solving to propose in above-mentioned background technology.

For achieving the above object, the present invention provides following technical scheme: a kind of AGV robot localization control algolithm based on fuzzy, comprises the following steps:

S1: running environment is preset, the predetermined running environment of selected AGV robot, and some signal processing modules are set in this context, the position of each signal processing module is carried out labelling, and marking signal is inputted the control unit of AGV robot；

S2: set up environmental map, the position of all of signal processing module previous step collected processes, and sets up environmental map；

The location of S3:AGV robot, startup AGV robot, and set the end of run positional information of AGV robot, the control unit of AGV robot contrasts with the map of previous step foundation according to the positional information of terminal, automatically selects an optimal route；

The circuit of S4:AGV robot runs, circuit previous step determined is subdivided into several index points, the corresponding coordinate position of each index point, and AGV robot is using these index points terminal as an end line, after arriving place's index point, an index point is advanced still further below；

S5:AGV robot running route is corrected, in the process that AGV robot advances, adopt fuzzy PID algorithm, constantly AGV robot is positioned, and real-time positional information is made comparisons with the index point set in previous step, after deviation occurs, steering module to AGV robot sends instruction, chooses nearest index point, makes AGV robot revert to selected index point position, and carry out previous step, until AGV robot arrives the terminal set.

Preferably, the control unit mentioned in described step S1 adopts PLC.

Preferably, described signal processing module includes signal transmitting module, signal receiving module and processor module, the outfan of signal receiving module is electrically connected with the input of processor module, and the outfan of processor module is electrically connected with the input of signal transmitting module.

Preferably, in described step S3, the computing formula of minimum range is:

F (n)=g (n)+h (n)

Wherein, g (n) is the distance of start position and current location, and h (n) is the distance between current location and terminal.

Preferably, in described step S5, the attitude of AGV robot is:

$\mathrm{\θ}=arcsin\left(\frac{d_{c1}+d_{c2}}{L}\right);$

The error of index point is by AGV robot:

$e_y+\frac{d_{c1}+d_{c2}}{2}+\sqrt{L^2-{(d_{c1}-d_{c2})}^2}-d_0.$

Compared with prior art, the invention has the beneficial effects as follows: the algorithm of the present invention is more suitable for the control of AGV robot, and by adding the mode of fuzzy-adaptation PID control, it is possible to increase the response speed of AGV robot and control running precision, and the positioning precision for AGV robot is higher.

Accompanying drawing explanation

Fig. 1 is flow chart of the present invention；

Fig. 2 is the signal processing module structural principle block diagram of the present invention；

Fig. 3 is PID control structure theory diagram of the present invention；

3 groups of positioning precision experimental results of Fig. 4 present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention.

Referring to Fig. 1-4, the present invention provides a kind of technical scheme: a kind of AGV robot localization control algolithm based on fuzzy, comprises the following steps:

S1: running environment is preset, the predetermined running environment of selected AGV robot, and some signal processing modules are set in this context, the position of each signal processing module is carried out labelling, and marking signal is inputted the control unit of AGV robot, control unit adopts PLC, signal processing module includes signal transmitting module, signal receiving module and processor module, the outfan of signal receiving module is electrically connected with the input of processor module, and the outfan of processor module is electrically connected with the input of signal transmitting module；

S2: set up environmental map, the position of all of signal processing module previous step collected processes, and sets up environmental map；

The location of S3:AGV robot, startup AGV robot, and set the end of run positional information of AGV robot, the control unit of AGV robot contrasts with the map of previous step foundation according to the positional information of terminal, automatically selecting an optimal route, the computing formula of minimum range is:

F (n)=g (n)+h (n)

Wherein, g (n) is the distance of start position and current location, and h (n) is the distance between current location and terminal；

The circuit of S4:AGV robot runs, circuit previous step determined is subdivided into several index points, the corresponding coordinate position of each index point, and AGV robot is using these index points terminal as an end line, after arriving place's index point, an index point is advanced still further below；

S5:AGV robot running route is corrected, in the process that AGV robot advances, adopt fuzzy PID algorithm, the PID control structure theory diagram of the present invention is as shown in Figure 3, constantly AGV robot is positioned, and real-time positional information is made comparisons with the index point set in previous step, after deviation occurs, steering module to AGV robot sends instruction, choose nearest index point, make AGV robot revert to selected index point position, and carry out previous step, until AGV robot arrives the terminal set, the attitude of AGV robot is:

$\mathrm{\θ}=arcsin\left(\frac{d_{c1}+d_{c2}}{L}\right);$

The error of index point is by AGV robot:

$e_y+\frac{d_{c1}+d_{c2}}{2}+\sqrt{L^2-{(d_{c1}-d_{c2})}^2}-d_0.$

By implementing 3 groups of positioning precision experiments, from experimental result such as Fig. 4, it can be seen that the positioning precision for AGV robot of the present invention is higher.

Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, being appreciated that and these embodiments can be carried out multiple change, amendment, replacement and modification without departing from the principles and spirit of the present invention, the scope of the present invention be defined by the appended.

Claims (5)

1. the AGV robot localization control algolithm based on fuzzy, it is characterised in that comprise the following steps:

S1: running environment is preset, the predetermined running environment of selected AGV robot, and some signal processing modules are set in this context, the position of each signal processing module is carried out labelling, and marking signal is inputted the control unit of AGV robot；

S2: set up environmental map, the position of all of signal processing module previous step collected processes, and sets up environmental map；

The location of S3:AGV robot, startup AGV robot, and set the end of run positional information of AGV robot, the control unit of AGV robot contrasts with the map of previous step foundation according to the positional information of terminal, automatically selects an optimal route；

The circuit of S4:AGV robot runs, circuit previous step determined is subdivided into several index points, the corresponding coordinate position of each index point, and AGV robot is using these index points terminal as an end line, after arriving place's index point, an index point is advanced still further below；

S5:AGV robot running route is corrected, in the process that AGV robot advances, adopt fuzzy PID algorithm, constantly AGV robot is positioned, and real-time positional information is made comparisons with the index point set in previous step, after deviation occurs, steering module to AGV robot sends instruction, chooses nearest index point, makes AGV robot revert to selected index point position, and carry out previous step, until AGV robot arrives the terminal set.

2. a kind of AGV robot localization control algolithm based on fuzzy according to claim 1, it is characterised in that: the control unit mentioned in described step S1 adopts PLC.

3. a kind of AGV robot localization control algolithm based on fuzzy according to claim 1, it is characterized in that: described signal processing module includes signal transmitting module, signal receiving module and processor module, the outfan of signal receiving module is electrically connected with the input of processor module, and the outfan of processor module is electrically connected with the input of signal transmitting module.

4. a kind of AGV robot localization control algolithm based on fuzzy according to claim 1, it is characterised in that: in described step S3, the computing formula of minimum range is:

F (n)=g (n)+h (n)

Wherein, g (n) is the distance of start position and current location, and h (n) is the distance between current location and terminal.

5. a kind of AGV robot localization control algolithm based on fuzzy according to claim 1, it is characterised in that: in described step S5, the attitude of AGV robot is:

$\mathrm{\θ}=\mathrm{arc}sin\left(\frac{d_{c1}+d_{c2}}{L}\right);$

The error of index point is by AGV robot:

$e_y+\frac{d_{c1}+d_{c2}}{2}+\sqrt{L^2-{(d_{c1}-d_{c2})}^2}-d_0.$