CN116339329A - AGV scheduling path optimization method and system based on 5G Internet of things - Google Patents
AGV scheduling path optimization method and system based on 5G Internet of things Download PDFInfo
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Abstract
The invention discloses an AGV scheduling path optimization method and system based on a 5G Internet of things, wherein the method comprises the following steps: s1, receiving a scheduling order; s2, classifying the scheduling orders, and assigning weights to the classified orders; s3, distributing the dispatching sequence of the automatic navigation trolley according to the weight, and planning a route; s4, integrating the routes, adjusting the running speed of the automatic navigation trolley, and estimating the running time; s5, the automatic navigation trolley carries out transportation according to the received scheduling order, and data of the automatic navigation trolley exceeding the driving time are collected; s6, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to an analysis result. The invention transports the dispatching order through the automatic navigation small, is convenient for timely processing the emergent situation, and greatly improves the dispatching efficiency of the automatic navigation trolley.
Description
Technical Field
The invention relates to the field of AGV scheduling path optimization, in particular to an AGV scheduling path optimization method and system based on a 5G Internet of things.
Background
An AGV is a transport vehicle equipped with an electromagnetic or optical automatic guide device capable of traveling along a predetermined guide path, and having safety protection and various transfer functions, and is a transport vehicle having safety protection and various transfer functions, which does not require a driver's transport vehicle in industrial applications, uses a rechargeable battery as its power source, controls the setting of its travel path by a computer,
the data volume that produces, gathers and handles at the equipment in intelligent mill is more and more, 4G network has been difficult to satisfy intelligent demand, and WIFI communication mode has interference immunity weak, the transmission rate is low, connect interruption scheduling problem when switching in outdoor scene, can't cover the wide factory environment outside the workshop completely, the outdoor logistics vehicle can't carry out effectual real-time location and control, and traditional manual handling equipment also appears scheduling task in the outdoor scene and receive untimely, the low scheduling efficiency scheduling problem also easily, and AGVs have the autonomous execution task because of it, accurate positioning, autonomous obstacle avoidance, environmental protection high efficiency, intelligent function etc. characteristics are widely used, make AGVs be acknowledged as the best choice that realizes logistics transportation in the cargo transportation field, AGVs are used widely in the cargo transportation field because of making up the not enough of manual handling.
The current AGV has the conditions of equipment sudden fault, production plan change, road condition occurrence accidents and the like when in use, so that the AGV is very easy to produce scheduling errors due to the occurrence condition when in use, the use efficiency of the AGV is greatly reduced, and the problem of line conflict of the AGV is not considered in the AGV process, so that the AGV is very easy to generate the conflict of the paths in the dispatching diagram, and the use scheduling efficiency of the AGV is greatly reduced.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides an AGV scheduling path optimization method and system based on the 5G Internet of things, which are used for solving the technical problems existing in the related art.
For this purpose, the invention adopts the following specific technical scheme:
according to one aspect of the invention, there is provided an AGV scheduling path optimization method based on 5G Internet of things, the method comprising the following steps:
s1, receiving a scheduling order;
s2, classifying the scheduling orders, and assigning weights to the classified orders;
s3, distributing the dispatching sequence of the automatic navigation trolley according to the weight, and planning a route;
s4, integrating the routes, adjusting the running speed of the automatic navigation trolleys, estimating the running time, calculating whether different automatic navigation trolleys crossing the routes collide at the crossing points according to the running time, if the different automatic navigation trolleys reach the crossing points at the same time, normally running the automatic navigation trolleys at the first position according to the dispatching sequence, controlling the automatic navigation trolleys at other positions of the dispatching sequence to run to standby areas near the crossing points, and after the automatic navigation trolleys at the first position pass through the crossing points, sequentially controlling the automatic navigation trolleys in the standby areas to pass through the crossing points according to the dispatching sequence;
s5, the automatic navigation trolley carries out transportation according to the received scheduling order, and data of the automatic navigation trolley exceeding the driving time are collected;
s6, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to an analysis result.
Further, classifying the scheduling orders and assigning weights to the classified orders includes the steps of:
s21, presetting corresponding weights for different articles;
s22, classifying according to the types of the articles required by the scheduling order;
s23, giving weight to the classification result of the article types and preset weight.
Further, the automatic navigation trolley scheduling sequence is allocated according to the weight, and the route planning comprises the following steps:
s31, adjusting the running sequence of the automatic navigation trolley according to the weight;
s32, carrying out route planning according to the running sequence of the automatic navigation trolley, carrying out preset speed adjustment on different automatic navigation trolleys according to the distance between the starting point and the reaching destination of the automatic navigation trolley if the road occupation situation occurs according to the route planning of the automatic navigation trolley, wherein the distance between the starting point and the reaching destination of the automatic navigation trolley is coincident, carrying out speed adjustment again when the automatic navigation trolley exits from the coincident route planning, and controlling the time of the automatic navigation trolley route planning.
Further, a calculation formula for route planning according to the running sequence of the automatic navigation trolley is as follows:
Further, integrating the routes, adjusting the traveling speed of the automatic navigation cart, and estimating the traveling time includes the steps of:
s41, integrating route planning of different automatic navigation trolleys;
s42, adjusting the running speed of the automatic navigation trolley with the overlapped routes;
s43, estimating the running time of the automatic navigation trolley according to the scheduling order.
Further, the automatic navigation trolley carries out transportation according to the received scheduling order, and the data of the automatic navigation trolley exceeding the driving time is collected, which comprises the following steps:
s51, carrying out scheduling order transportation on the automatic navigation trolley according to the adjusted running speed and the route planning;
s52, comparing the time when the automatic navigation trolley is transported with the estimated time;
and S53, collecting data of the automatic navigation trolley according to the comparison result.
Further, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to the analysis result comprises the following steps:
s61, analyzing the collected automatic navigation trolley data;
s62, judging the automatic navigation trolley and the route planning of the automatic navigation trolley according to the analysis result;
s63, adjusting the automatic navigation trolley, the running speed of the automatic navigation trolley and the route planning of the automatic navigation trolley according to the judging result;
s64, recording the adjustment result, and detecting and maintaining the automatic navigation trolley with the running time exceeding the estimated time and the route planning of the automatic navigation trolley.
Further, estimating the time for the automatic navigation cart to travel according to the scheduled order includes the steps of:
calculating the time for completing the order scheduling of the automatic navigation trolley according to the distance required to be travelled by the automatic navigation trolley and the adjusted speed, and setting an error value for the calculated time.
According to another aspect of the present invention, there is provided an AGV scheduling path optimization system based on 5G internet of things, the system comprising:
the order receiving module is used for receiving the scheduling order;
the classification weighting module is used for classifying and weighting the received scheduling orders;
the automatic navigation trolley is used for carrying out cargo dispatching according to the dispatching order;
the route planning module is used for planning and integrating routes of the automatic navigation trolley, setting the running speed of the automatic navigation trolley and estimating the running time;
the data collection module is used for collecting the time for completing the order scheduling of the automatic navigation trolley and analyzing the automatic navigation trolley exceeding the estimated time;
the adjusting module is used for adjusting the running route and the running speed of the automatic navigation trolley according to the analysis result of the automatic navigation trolley;
and the maintenance module is used for detecting and maintaining the planned route and the automatic navigation trolley according to the analysis result.
Further, the automatic navigation trolley comprises a bearing trolley body, an order receiver, an automatic navigation module, a collecting camera module and a fault feedback module;
the carrying vehicle body is used for carrying and transporting the articles required by the dispatching order;
an order receiver for receiving a scheduling order;
the automatic navigation module is used for navigating a running route;
the acquisition camera module is used for shooting and recording the route during running;
and the fault feedback module is used for feeding back the fault of the fault feedback module.
The beneficial effects of the invention are as follows:
1. according to the invention, the automatic navigation trolley is used for transporting the scheduling order, so that the complex operation of manual scheduling and the use of manpower during scheduling are reduced, the scheduling efficiency is greatly increased, meanwhile, the automatic navigation trolley is convenient to feed back in time when the automatic navigation trolley is damaged and the road condition is in emergency, the path and the transportation speed of the automatic navigation trolley are convenient to adjust, the emergent condition is convenient to process in time, and the scheduling efficiency of the automatic navigation trolley is greatly improved.
2. According to the invention, the scheduling orders are classified and weighted, so that the scheduling orders are convenient to rapidly schedule according to the assigned weights, the efficiency of scheduling important articles is greatly improved, the path is convenient to optimize during scheduling, the scheduling efficiency is improved, meanwhile, the automatic navigation module integrates the lines of all automatic navigation trolleys on the same road section, the speed of the automatic navigation trolleys on the same road section is adjusted, the collision generated by the same path is convenient to reduce during driving of the automatic navigation trolleys, the path lines are planned in advance, the blocking condition of the automatic navigation trolleys on the same path is avoided, the time error of the automatic navigation trolleys during transportation is reduced, and the use efficiency of the scheduling path and the effect of the scheduling transportation are greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an AGV scheduling path optimization method based on the 5G Internet of things according to an embodiment of the invention;
fig. 2 is a schematic block diagram of an AGV scheduling path optimization system based on the 5G internet of things according to an embodiment of the present invention.
In the figure:
1. an order receiving module; 2. a classification weighting module; 3. an automatic navigation trolley; 31. a load-bearing vehicle body; 32. an order receiver; 33. an automatic navigation module; 34. collecting a camera module; 35. a fault feedback module; 4. a route planning module; 5. a data collection module; 6. and an adjustment module.
Description of the embodiments
For the purpose of further illustrating the various embodiments, the present invention provides the accompanying drawings, which are a part of the disclosure of the present invention, and which are mainly used for illustrating the embodiments and for explaining the principles of the operation of the embodiments in conjunction with the description thereof, and with reference to these matters, it will be apparent to those skilled in the art to which the present invention pertains that other possible embodiments and advantages of the present invention may be practiced.
According to the embodiment of the invention, an AGV scheduling path optimization method and system based on the 5G Internet of things are provided.
The invention is further described with reference to the accompanying drawings and the specific embodiments, as shown in fig. 1, the method for optimizing the AGV scheduling path based on the 5G internet of things according to the embodiment of the invention comprises the following steps:
s1, receiving a scheduling order;
s2, classifying the scheduling orders, and assigning weights to the classified orders;
specifically, classifying the scheduling orders, and assigning weights to the classified orders includes the following steps:
s21, presetting corresponding weights for different articles;
s22, classifying according to the types of the articles required by the scheduling order;
s23, giving weight to the classification result of the article types and preset weight.
S3, distributing the dispatching sequence of the automatic navigation trolley according to the weight, and planning a route;
specifically, the automatic navigation trolley scheduling sequence is allocated according to the weight, and the route planning comprises the following steps:
s31, adjusting the running sequence of the automatic navigation trolley according to the weight;
specifically, the delphie method is also called a specialization method, and is characterized by concentrating the knowledge and experience of an expert, determining the weight of each index, and obtaining a more satisfactory result in continuous feedback and modification. The basic steps are as follows: selecting experts, namely about 10-30 people with actual working experience and deeper theoretical maintenance in the technical field, sending N indexes with undetermined weights, related data and unified rules for determining weights to the selected experts, recovering the judging results of the experts, calculating the average value and standard deviation of weights of all indexes, returning the calculated results and supplementary data to all the experts, and requiring all the experts to determine the weights on a new basis until the deviation between the weights of all the indexes and the average value of the weights of all the indexes does not exceed a preset standard, namely the opinion of all the experts is basically consistent, and taking the average value of the weights of all the indexes at the moment as the weight of the index.
S32, carrying out route planning according to the running sequence of the automatic navigation trolley, carrying out preset speed adjustment on different automatic navigation trolleys according to the distance between the starting point and the reaching destination of the automatic navigation trolley if the road occupation situation occurs according to the route planning of the automatic navigation trolley, wherein the distance between the starting point and the reaching destination of the automatic navigation trolley is coincident, carrying out speed adjustment again when the automatic navigation trolley exits from the coincident route planning, and controlling the time of the automatic navigation trolley route planning.
Specifically, a calculation formula for route planning according to the running sequence of the automatic navigation trolley is as follows:
Specifically, the following method may be adopted for the map of the planned path:
visual mapping: the AGV is regarded as particles with negligible volume, various obstacles in the working environment are abstractly depicted by adopting convex polygonal geometric figures, a straight line is used for connecting the starting point of the AGV, the vertex of each obstacle and the target point of the AGV, a collision-free route planning chart under the working environment can be obtained by eliminating the straight line intersecting the straight line and the obstacles, the starting point of the AGV and the vertex information of each obstacle are obtained, and the geometric shape and the positioning information of the obstacles in the working environment are relatively stable.
Voronoi diagram method: through dividing the area of the plane, searching a point contained in each area, wherein the area where the point is located is a set of points closest to the adjacent area, applying the concept of the Voronoi graph method to the working environment of the AGV vehicle, and keeping the maximum distance between the traveling path of the AGV vehicle and the obstacles in the working environment, so that collision between the AGV vehicle and the obstacles is avoided to the maximum extent, and when the Voronoi graph is drawn, equidistant line segments of the obstacles in the working environment are drawn according to the geometric shape of the obstacles, so that a complete closed graph is obtained, a collision-free path planning graph is obtained, the effective avoidance of collision between the AGV vehicle and the obstacles is realized, and the optimal path selection can be carried out based on the planning graph.
Topology map method: the topological map has the advantages that the information, the digitalization and the abstraction are carried out on the environment, so that the acquisition and the measurement of physical information of obstacles are avoided, the error influence is weakened, only the information directly related to the path of the AGV is focused, namely, only the connection relation among specific nodes is considered, the precision requirement of model construction on position positioning is relaxed, the detailed problems of path information and the like which cannot be accessed by the AGV are eliminated, the information storage space of a model algorithm is greatly reduced, the space complexity is reduced, the requirement on the computing capability of a computer is relaxed, and the real-time updating of the information and the maintenance of the high searching efficiency are facilitated.
Specifically, for the same working environment and task starting and ending points, under the same evaluation system, even if different planning methods are adopted, the situation of obtaining the same and optimal total length of the planned path may exist, the operation efficiency of the selected path planning method becomes a key factor of the system working efficiency, the current point needs to be subjected to multi-directional expansion during path planning, the expansion candidate points are determined in a comparison mode, the total number of the expansion candidate points can reflect the process calculation amount, the information storage amount and the working efficiency of the planning method, and therefore the total number P of the expansion candidate points traversed when the system planning task is completed is selected as an evaluation index of the system operation efficiency.
S4, integrating the routes, adjusting the running speed of the automatic navigation trolleys, estimating the running time, calculating whether different automatic navigation trolleys crossing the routes collide at the crossing points according to the running time, if the different automatic navigation trolleys reach the crossing points at the same time, normally running the automatic navigation trolleys at the first position according to the dispatching sequence, controlling the automatic navigation trolleys at other positions of the dispatching sequence to run to standby areas near the crossing points, and after the automatic navigation trolleys at the first position pass through the crossing points, sequentially controlling the automatic navigation trolleys in the standby areas to pass through the crossing points according to the dispatching sequence;
specifically, integrating the route, adjusting the running speed of the automatic navigation trolley, and estimating the running time includes the following steps:
s41, integrating route planning of different automatic navigation trolleys;
s42, adjusting the running speed of the automatic navigation trolley with the overlapped routes;
s43, estimating the running time of the automatic navigation trolley according to the scheduling order.
Specifically, the estimating of the time of the automatic navigation trolley traveling according to the scheduled order includes the following steps:
calculating the time for completing the order scheduling of the automatic navigation trolley according to the distance required to be travelled by the automatic navigation trolley and the adjusted speed, and setting an error value for the calculated time.
Specifically, in addition to the possibility of collision at the road intersection, the AGV may collide with each other on the traveling road due to factors such as unequal traveling speeds and inconsistent traveling directions, and the collision between vehicles may occur on the traveling road, where the collision between the AGV road segments is mainly divided into three types: blocking conflicts, subtended conflicts, equidirectional conflicts.
S5, the automatic navigation trolley carries out transportation according to the received scheduling order, and data of the automatic navigation trolley exceeding the driving time are collected;
specifically, the automatic navigation trolley carries out transportation according to the received scheduling order, and collects the data of the automatic navigation trolley exceeding the driving time, and the method comprises the following steps:
s51, carrying out scheduling order transportation on the automatic navigation trolley according to the adjusted running speed and the route planning;
s52, comparing the time when the automatic navigation trolley is transported with the estimated time;
and S53, collecting data of the automatic navigation trolley according to the comparison result.
S6, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to an analysis result.
Specifically, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to the analysis result comprises the following steps:
s61, analyzing the collected automatic navigation trolley data;
s62, judging the automatic navigation trolley and the route planning of the automatic navigation trolley according to the analysis result;
s63, adjusting the automatic navigation trolley, the running speed of the automatic navigation trolley and the route planning of the automatic navigation trolley according to the judging result;
specifically, collision avoidance is performed through a time window method, based on a working environment constructed by a research institute, in any continuous time period, because at most any one cross node or road section of a road section in the working environment can only be occupied by a single AGV, when a certain cross node or road section of the road section in a certain time period is occupied by a certain AGV executing a carrying task, the occupied node or road section can not be used by other AGVs any longer, and the uninterrupted time span from the moment that the AGV enters to the moment that the AGV leaves the passing node or road section and the radiation range thereof is called a time window.
S64, recording the adjustment result, and detecting and maintaining the automatic navigation trolley with the running time exceeding the estimated time and the route planning of the automatic navigation trolley.
According to another embodiment of the present invention, as shown in fig. 2, there is provided an AGV scheduling path optimization system based on 5G internet of things, the system comprising:
the order receiving module is used for receiving the scheduling order;
the classification weighting module is used for classifying and weighting the received scheduling orders;
the automatic navigation trolley is used for carrying out cargo dispatching according to the dispatching order;
specifically, the automatic navigation trolley comprises a bearing trolley body, an order receiver, an automatic navigation module, a collecting camera module and a fault feedback module;
the carrying vehicle body is used for carrying and transporting the articles required by the dispatching order;
an order receiver for receiving a scheduling order;
the automatic navigation module is used for navigating a running route;
the acquisition camera module is used for shooting and recording the route during running;
and the fault feedback module is used for feeding back the fault of the fault feedback module.
Specifically, the automatic navigation trolley comprises an AGV system, and the AGV system is controlled by a logistics upper dispatching system, an AGV ground control system and an AGV vehicle-mounted control system, wherein the ground control system refers to fixed equipment of the AGV system and mainly takes charge of functions of task allocation, vehicle dispatching, route line management, traffic management, automatic charging and the like; after receiving the instruction of the upper system, the vehicle-mounted control system is responsible for the functions of navigation calculation, guidance realization, vehicle running, loading and unloading operation and the like of the AGV; the navigation/guidance system provides the absolute or relative position and heading of the system for the AGV stand alone.
The specific AGV ground control system, namely the AGV upper control system, is the core of the AGV system, and performs task allocation, vehicle management, traffic management, communication management and the like on a plurality of AGV single machines in the AGV system.
And (3) task management: task management is similar to the process management of a computer operating system, and provides an interpretation execution environment for an AGV ground control program; providing scheduling operation according to task priority and starting time; various operations on the task are provided, such as start, stop, cancel, etc.
Vehicle management: the vehicle management is a core module for AGV management, and the AGV management is distributed and scheduled to execute tasks according to the request of a material handling task, calculates the shortest travel path of the AGV according to the shortest travel time principle of the AGV, controls and directs the travel process of the AGV, and issues loading and unloading and charging commands.
Traffic management: according to the physical size, running state and path condition of the AGVs, providing measures for mutually and automatically avoiding the AGVs, and simultaneously avoiding a deadlock method for mutually waiting vehicles and a deadlock relieving method; the traffic management of AGVs mainly has walking section allocation and deadlock reporting functions.
And (3) communication management: the communication management provides the communication functions of the AGV ground control system, the AGV single machine, the ground monitoring system, the ground IO equipment, the vehicle simulation system and the upper computer. The communication between the AGVs uses a radio communication mode, a wireless network is required to be established, the AGVs only carry out two-way communication with a ground system, the AGVs do not carry out communication, and the ground control system adopts a polling mode to communicate with a plurality of AGVs; the communication with the ground monitoring system, the vehicle simulation system and the upper computer uses TCP/IP communication.
Vehicle drive: the trolley drive is responsible for collecting the state of the AGVs, sending a permission request of a walking section to traffic management, and simultaneously issuing the confirmation section to the AGVs.
The route planning module is used for planning and integrating routes of the automatic navigation trolley, setting the running speed of the automatic navigation trolley and estimating the running time;
the data collection module is used for collecting the time for completing the order scheduling of the automatic navigation trolley and analyzing the automatic navigation trolley exceeding the estimated time;
the adjusting module is used for adjusting the running route and the running speed of the automatic navigation trolley according to the analysis result of the automatic navigation trolley;
and the maintenance module is used for detecting and maintaining the planned route and the automatic navigation trolley according to the analysis result.
In summary, by means of the above technical scheme of the invention, the manual scheduling operation is simplified and the labor force is reduced during scheduling by the automatic navigation trolley 3, the scheduling efficiency is greatly increased, and the automatic navigation trolley 3 is convenient to feed back in time when damage occurs and road conditions occur suddenly by estimating the time for completing the scheduling order, so that the path and the transportation speed of the automatic navigation trolley 3 are convenient to adjust, the emergent conditions are convenient to process in time, and the scheduling efficiency of the automatic navigation trolley 3 is greatly improved.
In addition, the invention classifies and weights the dispatching orders, so that the dispatching orders are convenient for quick dispatching according to the distributed weights, the dispatching efficiency of important objects is greatly improved, the dispatching path is convenient to optimize, the dispatching efficiency is improved, meanwhile, the automatic navigation module 33 integrates the lines of the automatic navigation trolleys 3 on the same road section, the speed of the automatic navigation trolleys on the same road section is adjusted, the automatic navigation trolleys are convenient to reduce collision generated by the same path during driving, and the path lines are planned in advance, so that the situation of blockage of the automatic navigation trolleys 3 during the same path is avoided, the time error of the automatic navigation trolleys 3 during transportation is reduced, and the use efficiency of the dispatching path and the dispatching effect are greatly improved.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (8)
1. The AGV scheduling path optimization method based on the 5G Internet of things is characterized by comprising the following steps of:
s1, receiving a scheduling order;
s2, classifying the scheduling orders, and assigning weights to the classified orders;
s3, distributing the dispatching sequence of the automatic navigation trolley according to the weight, and planning a route;
s4, integrating the routes, adjusting the running speed of the automatic navigation trolleys, estimating the running time, calculating whether different automatic navigation trolleys crossing the routes collide at the crossing points according to the running time, if the different automatic navigation trolleys reach the crossing points at the same time, normally running the automatic navigation trolleys at the first position according to the dispatching sequence, controlling the automatic navigation trolleys at other positions of the dispatching sequence to run to standby areas near the crossing points, and after the automatic navigation trolleys at the first position pass through the crossing points, sequentially controlling the automatic navigation trolleys in the standby areas to pass through the crossing points according to the dispatching sequence;
s5, the automatic navigation trolley carries out transportation according to the received scheduling order, and data of the automatic navigation trolley exceeding the driving time are collected;
s6, analyzing the collected transportation data of the automatic navigation trolley, and adjusting the dispatching path of the automatic navigation trolley in real time according to an analysis result;
the step of classifying the scheduling orders and assigning weights to the classified orders comprises the following steps:
s21, presetting corresponding weights for different articles;
s22, classifying according to the types of the articles required by the scheduling order;
s23, weighting the classification result of the article types and preset weights;
the automatic navigation trolley scheduling sequence is distributed according to the weight, and the route planning comprises the following steps:
s31, adjusting the running sequence of the automatic navigation trolley according to the weight;
s32, carrying out route planning according to the running sequence of the automatic navigation trolley, carrying out preset speed adjustment on different automatic navigation trolleys according to the distance between the starting point and the reaching destination of the automatic navigation trolley if the road occupation situation occurs according to the route planning of the automatic navigation trolley, wherein the distance between the starting point and the reaching destination of the automatic navigation trolley is coincident, carrying out speed adjustment again when the automatic navigation trolley exits from the coincident route planning, and controlling the time of the automatic navigation trolley route planning.
2. The method for optimizing the AGV scheduling path based on the 5G Internet of things according to claim 1, wherein the steps of integrating the routes, adjusting the traveling speed of the automatic navigation cart, and estimating the traveling time comprise the steps of:
s41, integrating route planning of different automatic navigation trolleys;
s42, adjusting the running speed of the automatic navigation trolley with the overlapped routes;
s43, estimating the running time of the automatic navigation trolley according to the scheduling order.
3. The method for optimizing an AGV dispatch path based on the 5G internet of things according to claim 2, wherein the automatic navigation cart transports according to the received dispatch order and collects data of the automatic navigation cart exceeding the travel time, comprising the steps of:
s51, carrying out scheduling order transportation on the automatic navigation trolley according to the adjusted running speed and the route planning;
s52, comparing the time when the automatic navigation trolley is transported with the estimated time;
and S53, collecting data of the automatic navigation trolley according to the comparison result.
4. The method for optimizing the scheduling path of the AGV based on the 5G Internet of things according to claim 3, wherein the analyzing the collected transportation data of the automatic navigation cart and adjusting the scheduling path of the automatic navigation cart in real time according to the analysis result comprises the following steps:
s61, analyzing the collected automatic navigation trolley data;
s62, judging the automatic navigation trolley and the route planning of the automatic navigation trolley according to the analysis result;
s63, adjusting the automatic navigation trolley, the running speed of the automatic navigation trolley and the route planning of the automatic navigation trolley according to the judging result;
s64, recording the adjustment result, and detecting and maintaining the automatic navigation trolley with the running time exceeding the estimated time and the route planning of the automatic navigation trolley.
5. The method for optimizing the AGV scheduling path based on the 5G Internet of things according to claim 4, wherein the calculation formula for performing route planning according to the driving sequence of the automatic navigation trolley is as follows:
6. The method for optimizing the scheduling path of an AGV based on the 5G internet of things according to claim 5, wherein the estimating the time for which the automatic navigation cart travels according to the scheduling order comprises the steps of:
calculating the time for completing the order scheduling of the automatic navigation trolley according to the distance required to be travelled by the automatic navigation trolley and the adjusted speed, and setting an error value for the calculated time.
7. An AGV scheduling path optimization system based on a 5G internet of things for implementing the 5G internet of things-based AGV scheduling path optimization method of any one of claims 1-6, the system comprising:
the order receiving module is used for receiving the scheduling order;
the classification weighting module is used for classifying and weighting the received scheduling orders;
the automatic navigation trolley is used for carrying out cargo dispatching according to the dispatching order;
the route planning module is used for planning and integrating routes of the automatic navigation trolley, setting the running speed of the automatic navigation trolley and estimating the running time;
the data collection module is used for collecting the time for completing the order scheduling of the automatic navigation trolley and analyzing the automatic navigation trolley exceeding the estimated time;
the adjusting module is used for adjusting the running route and the running speed of the automatic navigation trolley according to the analysis result of the automatic navigation trolley;
and the maintenance module is used for detecting and maintaining the planned route and the automatic navigation trolley according to the analysis result.
8. The AGV scheduling path optimization system based on the 5G Internet of things according to claim 7, wherein the automatic navigation trolley comprises a bearing trolley body, an order receiver, an automatic navigation module, an acquisition camera module and a fault feedback module;
the carrying vehicle body is used for carrying and transporting articles required by the dispatching order;
the order receiver is used for receiving the scheduling order;
the automatic navigation module is used for navigating a running route;
the acquisition camera module is used for shooting and recording a route during running;
the fault feedback module is used for feeding back the fault of the fault feedback module.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005077187A (en) * | 2003-08-29 | 2005-03-24 | Alpine Electronics Inc | In-vehicle navigation device and route guiding method |
| CN106251016A (en) * | 2016-08-01 | 2016-12-21 | 南通大学 | A kind of parking system paths planning method based on dynamic time windows |
| CN110989570A (en) * | 2019-10-15 | 2020-04-10 | 浙江工业大学 | Multi-AGV anti-collision collaborative path planning method |
| CN112036773A (en) * | 2020-09-29 | 2020-12-04 | 劢微机器人科技(深圳)有限公司 | AGV trolley task allocation method, AGV trolley task allocation equipment, storage medium and device |
| CN115062868A (en) * | 2022-07-28 | 2022-09-16 | 北京建筑大学 | Pre-polymerization type vehicle distribution path planning method and device |
| US20230072997A1 (en) * | 2021-09-08 | 2023-03-09 | Tianjin Port Second Container Terminal Co., Ltd. | Intelligent horizontal transportation system and method for automatic side-loading/unloading container tarminal |
-
2023
- 2023-03-21 CN CN202310273824.5A patent/CN116339329B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005077187A (en) * | 2003-08-29 | 2005-03-24 | Alpine Electronics Inc | In-vehicle navigation device and route guiding method |
| CN106251016A (en) * | 2016-08-01 | 2016-12-21 | 南通大学 | A kind of parking system paths planning method based on dynamic time windows |
| CN110989570A (en) * | 2019-10-15 | 2020-04-10 | 浙江工业大学 | Multi-AGV anti-collision collaborative path planning method |
| CN112036773A (en) * | 2020-09-29 | 2020-12-04 | 劢微机器人科技(深圳)有限公司 | AGV trolley task allocation method, AGV trolley task allocation equipment, storage medium and device |
| US20230072997A1 (en) * | 2021-09-08 | 2023-03-09 | Tianjin Port Second Container Terminal Co., Ltd. | Intelligent horizontal transportation system and method for automatic side-loading/unloading container tarminal |
| CN115062868A (en) * | 2022-07-28 | 2022-09-16 | 北京建筑大学 | Pre-polymerization type vehicle distribution path planning method and device |
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