CN111273667A - Navigation control method and system for unmanned carrying vehicle - Google Patents

Navigation control method and system for unmanned carrying vehicle Download PDF

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Publication number
CN111273667A
CN111273667A CN202010097232.9A CN202010097232A CN111273667A CN 111273667 A CN111273667 A CN 111273667A CN 202010097232 A CN202010097232 A CN 202010097232A CN 111273667 A CN111273667 A CN 111273667A
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China
Prior art keywords
module
automated guided
unmanned
guided vehicle
vehicle
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CN202010097232.9A
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Chinese (zh)
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刘家骏
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Tangshan Dingxin Electric Instrument Engineering Co Ltd
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Tangshan Dingxin Electric Instrument Engineering Co Ltd
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Priority to CN202010097232.9A priority Critical patent/CN111273667A/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal

Abstract

The invention relates to the technical field of control of an unmanned conveying vehicle, and discloses a navigation control method and a navigation control system of the unmanned conveying vehicle, wherein the method comprises the following steps: the method comprises the steps that a scheduling server obtains an initial position and a first target position of goods to be carried; the scheduling server generates a first optimal path according to the initial position and the first target position; the scheduling server sends a first instruction containing the first optimal path to the unmanned transport vehicle which is located at the initial position and is in an idle state; and the unmanned transport vehicle responds to the first instruction, acquires the first position data by using the inertial navigation system, and transports the goods to be transported to the first target position according to the first position data and the first optimal path. The invention can realize the autonomous navigation of the unmanned carrier, does not depend on external information, does not need to modify a field, is not easy to be interfered by surrounding metal substances and human beings, and has high precision.

Description

Navigation control method and system for unmanned carrying vehicle
Technical Field
The invention relates to the technical field of control of an unmanned carrying vehicle, in particular to a navigation control method and system of the unmanned carrying vehicle.
Background
In recent years, in order to reduce production cost, reduce energy consumption, and improve production efficiency, more and more enterprises introduce industrial automation systems. Under the mainstream trend of industrial automation, the industry of automated guided vehicles (AGVs for short) is rapidly developed. The unmanned transport vehicle is an unmanned automatic transport vehicle which is provided with an automatic guide device, can run along a specified guide path, is provided with a programming and parking selection device and a safety protection device on a vehicle body, can efficiently, accurately and flexibly complete the transport task of materials, and is widely applied to the scenes of factory transport, warehouse logistics, medical equipment transportation, automatic parking and the like.
At present, a magnetic navigation control system is generally adopted by an unmanned transport vehicle to complete guiding, for example, chinese patent with publication number CN206618424U discloses an AGV barcode type magnetic tape structure, which is used as a magnetic tape for generating navigation signals, when a magnetic sensor of the AGV car passes through the barcode type magnetic tape, a pulse type magnetic field signal is detected, and by accumulating the detected pulse number, the driving distance of the AGV car is accurately detected, so that the AGV car is driven along the direction of the magnetic tape. However, this patent requires a magnetic tape to be attached to the road surface, is complicated to install, requires a new magnetic tape to be laid if the driving route is changed, is easily interfered by surrounding metallic substances, and is also easily damaged mechanically, which affects the normal operation of the automated guided vehicle.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a navigation control method of an unmanned transport vehicle, which can realize autonomous navigation of the unmanned transport vehicle, does not depend on external information, does not need to modify a field, is not easily interfered by surrounding metal substances and human beings, and has high precision.
The invention also aims to provide a navigation control system of the automatic guided vehicle, which can realize the automatic navigation of the automatic guided vehicle, does not depend on external information, does not need to modify a field, is not easy to be interfered by surrounding metal substances and human beings, and has high precision.
The above object of the present invention is achieved by the following technical solutions:
an automated guided vehicle navigation control method comprising: the method comprises the steps that a scheduling server obtains an initial position and a first target position of goods to be carried; the scheduling server generates a first optimal path according to the initial position and the first target position; the dispatch server sending a first instruction including the first optimal path to an automated guided vehicle in the initial position and in an idle state; and the automated guided vehicle responds to the first instruction, acquires first position data by using an inertial navigation system, and conveys the goods to be conveyed to the first target position according to the first position data and the first optimal path.
Through adopting above-mentioned technical scheme, can realize unmanned transport vehicle's autonomous navigation, do not rely on external information, need not to reform transform the place, also be difficult for receiving metallic substance and jamming around to the precision is high.
The present invention in a preferred example may be further configured to: after the dispatch server obtains an initial position and a first target position of a cargo to be handled and before the dispatch server sends a first instruction containing the first optimal path to an automated guided vehicle located at the initial position, the method further comprises: the scheduling server inquires the state of the unmanned transport vehicle within a preset range; when one unmanned carrying vehicle in an idle state exists in the preset range, the scheduling server generates a second optimal path according to the current position and the initial position of the unmanned carrying vehicle in the idle state, and sends a second instruction containing the second optimal path to the unmanned carrying vehicle in the idle state; the unmanned transport vehicle in the idle state responds to the second instruction, acquires second position and posture data by using the inertial navigation system, and drives to the initial position according to the second optimal path according to the second position and posture data; when at least two unmanned carrying vehicles in idle states exist in the preset range, the scheduling server screens out a first optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in idle states according to waiting time, and sends a third instruction containing a third optimal path to the first optimal unmanned carrying vehicle; the first optimal unmanned transport vehicle responds to the third instruction, acquires third posture data by using the inertial navigation system, and drives to the initial position according to the third posture data and the third optimal path; when only one unmanned carrying vehicle in the working state exists in the preset range, the dispatching server generates a fourth optimal path according to a second target position of the current task of the unmanned carrying vehicle in the working state and the initial position; after the current task is completed by the automated guided vehicle in the working state, the scheduling server sends a fourth instruction containing the fourth optimal path to the automated guided vehicle in the working state; responding to the fourth instruction by the unmanned transport vehicle which finishes the current task, acquiring fourth posture data by using the inertial navigation system, and driving to the initial position according to the fourth posture data and the fourth optimal path; when only at least two unmanned carrying vehicles in working states exist in the preset range, the scheduling server screens out a second optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in working states according to waiting time; after the second optimal automated guided vehicle completes the current task, the scheduling server sends a fifth instruction containing a fifth optimal path to the second optimal automated guided vehicle; the second optimal automated guided vehicle responds to the fifth instruction, acquires fifth pose data by using the inertial navigation system, and travels to the initial position according to the fifth optimal path according to the fifth pose data.
By adopting the technical scheme, when the initial position of the goods to be carried does not have an idle unmanned carrying vehicle, whether the unmanned carrying vehicle in the idle state exists or not needs to be searched in the preset range, if a plurality of unmanned carrying vehicles in the idle state exist, the optimal unmanned carrying vehicle is selected according to the waiting time to carry the goods, if the unmanned carrying vehicle in the idle state does not exist in the preset range, the unmanned carrying vehicle in the working state in the preset range needs to be searched, if the unmanned carrying vehicles in the working state exist, the optimal unmanned carrying vehicle needs to be selected according to the waiting time to carry the goods, so that the waiting time for carrying the goods can be reduced, and the goods carrying efficiency is improved.
The present invention in a preferred example may be further configured to: the scheduling server screens out a first optimal automated guided vehicle from the at least two automated guided vehicles in idle states according to waiting time, and the method comprises the following steps: the scheduling server respectively generates a first selectable optimal path of each unmanned carrier in the idle state according to the current positions and the initial positions of the at least two unmanned carriers in the idle state; the scheduling server acquires a first average speed of each unmanned carrier in the idle state, and predicts according to the first average speed and the first optional optimal path to obtain a first predicted duration of each unmanned carrier in the idle state; the scheduling server determines the automated guided vehicle corresponding to the first predicted time length with the minimum first predicted time length in the first predicted time length as the first optimal automated guided vehicle, and determines the first optional optimal path corresponding to the first optimal automated guided vehicle as the third optimal path; and/or the dispatch server screens out a second optimal automated guided vehicle from the at least two automated guided vehicles in the working state according to the waiting time, comprising: the scheduling server respectively generates a second optional optimal path of each unmanned carrier in the working state according to the current positions of the at least two unmanned carriers in the working state and a third target position of the current task; the scheduling server respectively generates a third optional optimal path of each unmanned carrier in the working state according to a third target position and the initial position of the current task of the at least two unmanned carriers in the working state; the scheduling server acquires a second average speed of each unmanned carrier in the working state, and predicts according to the second average speed, the second optional optimal path and the third optional optimal path to obtain a second predicted duration of each unmanned carrier in the working state; and the scheduling server determines the automated guided vehicle corresponding to the second predicted time length with the minimum second predicted time length in the second predicted time lengths as the second optimal automated guided vehicle, and determines a third optional optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
By adopting the technical scheme, the time from the current position to the initial position of the goods to be transported is predicted according to the average speed of the automated guided vehicle and the planned optimal path, the automated guided vehicle with short time is selected to transport the goods, the waiting time of each automated guided vehicle can be accurately predicted, support is provided for selecting the optimal automated guided vehicle, the waiting time for transporting the goods can be effectively shortened, and the goods transporting efficiency is improved.
The present invention in a preferred example may be further configured to: before the dispatch server obtains the first average speed of each automated guided vehicle in an idle state, the method further comprises: each unmanned transport vehicle in an idle state collects a plurality of first speeds in a first preset time period, calculates a first average speed according to the first speeds, and sends the first average speed to the scheduling server; or each unmanned transport vehicle in an idle state collects a plurality of first speeds in a first preset time period and sends the first speeds to the scheduling server; the scheduling server calculates a first average speed of each unmanned transport vehicle in an idle state according to the plurality of first speeds.
The present invention in a preferred example may be further configured to: before the dispatch server obtains the second average speed of each automated guided vehicle in the working state, the method further includes: each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period, calculates a second average speed according to the second speeds, and sends the second average speed to the dispatching server; or each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period and sends the second speeds to the scheduling server; and the dispatching server respectively calculates a second average speed of each unmanned transport vehicle in the working state according to the plurality of second speeds.
By adopting the technical scheme, the average speed is calculated by collecting the speed of the unmanned carrying vehicle in a certain time period, so that the accuracy of the average speed can be improved, and the prediction precision of the waiting time of each unmanned carrying vehicle is improved.
The second aim of the invention is realized by the following technical scheme:
an automated guided vehicle navigation control system, comprising: the acquisition module is used for acquiring an initial position and a first target position of the goods to be transported by the dispatching server; the first path generation module is connected with the acquisition module and used for generating a first optimal path by the scheduling server according to the initial position and the first target position; a first instruction sending module, connected to the first path generating module, for sending a first instruction including the first optimal path to the automated guided vehicle located at the initial position and in an idle state by the dispatch server; and the first navigation module is connected with the first instruction sending module and used for responding to the first instruction by the unmanned transport vehicle, acquiring first position and attitude data by using an inertial navigation system, and transporting the goods to be transported to the first target position according to the first position and attitude data and the first optimal path.
Through adopting above-mentioned technical scheme, can realize unmanned transport vehicle's autonomous navigation, do not rely on external information, need not to reform transform the place, also be difficult for receiving metallic substance and jamming around to the precision is high.
The present invention in a preferred example may be further configured to: further comprising: further comprising: the query module is connected with the acquisition module and used for querying the state of the unmanned transport vehicle in a preset range after the scheduling server acquires the initial position and the first target position of the goods to be transported; the second path generation module is connected with the query module and used for generating a second optimal path according to the current position of the unmanned carrying vehicle in the idle state and the initial position when the unmanned carrying vehicle in the idle state exists in the preset range; a second instruction sending module, connected to the second path generating module, configured to send, by the scheduling server, a second instruction including the second optimal path to the automated guided vehicle in the idle state; the second navigation module is respectively connected with the second instruction sending module and the first instruction sending module, and is used for responding to the second instruction by the unmanned transport vehicle in the idle state, acquiring second position and posture data by using the inertial navigation system, and driving to the initial position according to the second position and posture data and the second optimal path; the first screening module is connected with the query module and used for screening out a first optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in the idle state according to waiting time when at least two unmanned carrying vehicles in the idle state exist in the preset range; a third instruction sending module, connected to the first screening module, for sending, by the dispatch server, a third instruction including a third optimal path to the first optimal automated guided vehicle; the third navigation module is respectively connected with the third instruction sending module and the first instruction sending module, and is used for responding to the third instruction by the first optimal unmanned transport vehicle, acquiring third posture data by using the inertial navigation system, and driving to the initial position according to the third posture data and the third optimal path; the third path generating module is connected with the query module and used for generating a fourth optimal path according to the second target position and the initial position of the current task of the unmanned carrying vehicle in the working state when only one unmanned carrying vehicle in the working state exists in the preset range; a fourth instruction sending module, connected to the third path generating module, configured to send, by the scheduling server, a fourth instruction including the fourth optimal path to the automated guided vehicle in the operating state after the automated guided vehicle in the operating state completes the current task; the fourth navigation module is respectively connected with the fourth instruction sending module and the first instruction sending module, and is used for responding to the fourth instruction by the unmanned transport vehicle which completes the current task, acquiring fourth posture data by using the inertial navigation system, and driving to the initial position according to the fourth posture data and the fourth optimal path; the second screening module is connected with the query module and used for screening out a second optimal unmanned transport vehicle from the at least two unmanned transport vehicles in the working state according to the waiting time when only the at least two unmanned transport vehicles in the working state exist in the preset range; a fifth instruction sending module, connected to the second screening module, configured to send, by the scheduling server, a fifth instruction including a fifth optimal path to the second optimal automated guided vehicle after the second optimal automated guided vehicle completes the current task; and the fifth navigation module is respectively connected with the fifth instruction sending module and the first instruction sending module, and is used for responding to the fifth instruction by the second optimal unmanned transport vehicle, acquiring fifth pose data by using the inertial navigation system, and driving to the initial position according to the fifth pose data and the fifth optimal path.
By adopting the technical scheme, when the initial position of the goods to be carried does not have an idle unmanned carrying vehicle, whether the unmanned carrying vehicle in the idle state exists or not needs to be searched in the preset range, if a plurality of unmanned carrying vehicles in the idle state exist, the optimal unmanned carrying vehicle is selected according to the waiting time to carry the goods, if the unmanned carrying vehicle in the idle state does not exist in the preset range, the unmanned carrying vehicle in the working state in the preset range needs to be searched, if the unmanned carrying vehicles in the working state exist, the optimal unmanned carrying vehicle needs to be selected according to the waiting time to carry the goods, so that the waiting time for carrying the goods can be reduced, and the goods carrying efficiency is improved.
The present invention in a preferred example may be further configured to: the first screening module includes: a fourth path generating module, configured to generate, by the scheduling server, a first selectable optimal path for each idle automated guided vehicle according to the current positions and the initial positions of the at least two idle automated guided vehicles, respectively; the first time length prediction module is connected with the fourth path generation module and used for the scheduling server to acquire a first average speed of each unmanned transport vehicle in the idle state, and to predict according to the first average speed and the first optional optimal path to obtain a first predicted time length of each unmanned transport vehicle in the idle state; a first determining module, connected to the first time length predicting module, configured to determine, by the scheduling server, the automated guided vehicle corresponding to the smallest first predicted time length in the first predicted time lengths as the first optimal automated guided vehicle, and determine a first selectable optimal path corresponding to the first optimal automated guided vehicle as the third optimal path; and/or the second screening module comprises: a fifth path generating module, configured to generate, by the scheduling server, a second optional optimal path for each automated guided vehicle in the working state according to the current positions of the at least two automated guided vehicles in the working state and a third target position of the current task, respectively; a sixth path generating module, configured to generate, by the scheduling server, a third selectable optimal path for each automated guided vehicle in the working state according to a third target position and the initial position of the current task of the at least two automated guided vehicles in the working state, respectively; a second duration prediction module, connected to the fifth path generation module and the sixth path generation module, respectively, for the scheduling server to obtain a second average speed of each automated guided vehicle in a working state, and perform prediction according to the second average speed, the second optional optimal path, and the third optional optimal path to obtain a second predicted duration of each automated guided vehicle in the working state; a second determining module, connected to the second duration predicting module, configured to determine, by the scheduling server, the automated guided vehicle corresponding to the second predicted duration that is the smallest among the second predicted durations as the second optimal automated guided vehicle, and determine a third selectable optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
By adopting the technical scheme, the time from the current position to the initial position of the goods to be transported is predicted according to the average speed of the automated guided vehicle and the planned optimal path, the automated guided vehicle with short time is selected to transport the goods, the waiting time of each automated guided vehicle can be accurately predicted, support is provided for selecting the optimal automated guided vehicle, the waiting time for transporting the goods can be effectively shortened, and the goods transporting efficiency is improved.
The present invention in a preferred example may be further configured to: further comprising: the first speed acquisition module is used for acquiring a plurality of first speeds in a first preset time period by each unmanned transport vehicle in an idle state; a first average speed calculation module connected to the first speed acquisition module, for calculating the first average speed according to the plurality of first speeds for each empty automated guided vehicle; the first average speed sending module is respectively connected with the first average speed calculating module and the first time length predicting module and is used for sending the first average speed to the dispatching server by each unmanned transport vehicle in an idle state; or the first speed acquisition module is used for acquiring a plurality of first speeds in a first preset time period by each unmanned transport vehicle in an idle state; the first speed sending module is connected with the first speed acquisition module and used for sending the plurality of first speeds to the dispatching server by each unmanned carrier in an idle state; and the second average speed calculating module is respectively connected with the first speed sending module and the first time length predicting module and is used for the dispatching server to respectively calculate the first average speed of each unmanned transport vehicle in an idle state according to the plurality of first speeds.
The present invention in a preferred example may be further configured to: further comprising: the second speed acquisition module is used for acquiring a plurality of second speeds in a second preset time period by each unmanned transport vehicle in a working state; the third average speed calculation module is connected with the second speed acquisition module and used for calculating the second average speed of each unmanned transport vehicle in the working state according to the plurality of second speeds; the second average speed sending module is respectively connected with the third average speed calculating module and the second duration predicting module and is used for sending the second average speed to the dispatching server by each unmanned transport vehicle in a working state; or the second speed acquisition module is used for acquiring a plurality of second speeds in a second preset time period by each unmanned transport vehicle in the working state; the second speed sending module is connected with the second speed acquisition module and used for sending the plurality of second speeds to the dispatching server by each unmanned transport vehicle in a working state; and the fourth average speed calculation module is respectively connected with the second speed sending module and the second duration prediction module, and the scheduling server respectively calculates a second average speed of each unmanned transport vehicle in a working state according to the plurality of second speeds.
By adopting the technical scheme, the average speed is calculated by collecting the speed of the unmanned carrying vehicle in a certain time period, so that the accuracy of the average speed can be improved, and the prediction precision of the waiting time of each unmanned carrying vehicle is improved.
In summary, the invention includes at least one of the following beneficial technical effects:
1. the autonomous navigation of the unmanned carrier can be realized, the autonomous navigation does not depend on external information, a field does not need to be modified, the autonomous navigation is not easily interfered by surrounding metal substances and human beings, and the accuracy is high;
2. when no idle unmanned carrying vehicle exists at the initial position of the goods to be carried, whether an unmanned carrying vehicle in an idle state exists or not needs to be searched in a preset range, if a plurality of unmanned carrying vehicles in the idle state exist, the optimal unmanned carrying vehicle is selected according to the waiting time to carry the goods, if no unmanned carrying vehicle in the idle state exists in the preset range, the unmanned carrying vehicle in a working state in the preset range needs to be searched, and if a plurality of carrying vehicles in the working state exist, the optimal unmanned carrying vehicle needs to be selected according to the waiting time to carry the goods, so that the waiting time for carrying the goods can be reduced, and the goods carrying efficiency is improved;
3. according to the average speed of the automated guided vehicles and the time length of the planned optimal path from the current position to the initial position of the goods to be transported, the automated guided vehicles with short time are selected to transport the goods, the waiting time of each automated guided vehicle can be accurately predicted, support is provided for selecting the optimal automated guided vehicle, the waiting time for transporting the goods can be effectively shortened, and the goods transporting efficiency is improved.
Drawings
Fig. 1 is a flowchart of a method according to a first embodiment of the present invention.
Fig. 2 is a flowchart of a method according to an alternative embodiment of the first embodiment of the present invention.
Fig. 3 is a flowchart of the method of step S109 according to the first embodiment of the present invention.
Fig. 4 is a flowchart of a method of step S115 according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a second embodiment of the present invention.
Fig. 6 is a schematic structural diagram of an alternative implementation of the second embodiment of the present invention.
Fig. 7 and 8 are schematic structural diagrams of a first screening module according to a second embodiment of the present invention.
Fig. 9 and 10 are schematic structural diagrams of a second screening module according to a second embodiment of the present invention.
In the figure, 201, an obtaining module, 202, a first path generating module, 203, a first instruction sending module, 204, a first navigation module, 205, an inquiring module, 206, a second path generating module, 207, a second instruction sending module, 208, a second navigation module, 209, a first screening module, 2091, a fourth path generating module, 2092, a first time prediction module, 2093, a first determining module, 2094, a first speed collecting module, 2095, a first average speed calculating module, 2096, a first average speed sending module, 2097, a first speed sending module, 2098, a second average speed calculating module, 210, a third instruction sending module, 211, a third navigation module, 212, a third path generating module, 213, a fourth instruction sending module, 214, a fourth navigation module, 215, a second screening module, 2151, a fifth path generating module, 2152, a sixth path generating module, 2153. the system comprises a second duration prediction module, a 2154, a second determination module, a 2155, a second speed acquisition module, a 2156, a third average speed calculation module, a 2157, a second average speed sending module, a 2158, a second speed sending module, a 2159, a fourth average speed calculation module, a 216, a fifth instruction sending module, a 217 and a fifth navigation module.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The first embodiment of the invention:
referring to fig. 1, a navigation control method for an automated guided vehicle disclosed in an embodiment of the present invention mainly includes the following steps:
step S101, a scheduling server acquires an initial position and a first target position of a cargo to be carried;
in this embodiment, the initial position and the first target position of the goods to be handled may be stored in the RFID tag in advance, and the RFID tag is attached to the goods to be handled. When the RFID electronic tags need to be carried, carrying personnel hold the RFID card reader to scan the RFID electronic tags, read pre-stored initial position information and first target position information, and send the information to a dispatching server through a wireless communication network (such as WIFI, 4G, 5G and the like). The scheduling server is generally arranged in a scheduling room of the factory building, and scheduling personnel manage the cargo carrying condition of the whole factory building.
Step S102, a scheduling server generates a first optimal path according to an initial position and a first target position;
in this embodiment, the scheduling server calculates a shortest path from the initial position to the first target position according to an a-x algorithm, which is the first optimal path.
Step S103, the scheduling server sends a first instruction containing a first optimal path to the unmanned transport vehicle which is located at the initial position and is in the idle state;
in the present embodiment, the automated guided vehicle in the idle state is an automated guided vehicle that is not currently carrying a job and is in a standby state. All the unmanned transport vehicles in the plant send the positioning information and the state information (idle state and working state) of the unmanned transport vehicles to the scheduling server in real time. The scheduling server acquires the current unmanned transport vehicle which is located at the initial position and is in the idle state, and sends a first instruction containing a first optimal path to the current unmanned transport vehicle through the wireless communication network.
And step S104, responding to the first instruction, the automated guided vehicle acquires first position data by using the inertial navigation system, and carries the goods to be carried to a first target position according to the first position data and a first optimal path.
In this embodiment, after receiving first instruction, the automated guided vehicle analyzes and obtains first optimal path, then sends an indication signal to remind the carrier to place the goods to be carried on the vehicle, and this indication signal can be light flicker, music warning.
Each unmanned transport vehicle is provided with an accelerometer, an electronic compass, a gyroscope and other motion sensors, the acceleration of the unmanned transport vehicle is measured through the accelerometer, the magnetic heading of the unmanned transport vehicle is measured through the electronic compass, the rotation angular speed of the unmanned transport vehicle is measured through the gyroscope, the attitude information of the unmanned transport vehicle, such as a heading angle, a pitch angle, a roll angle and the like, is obtained through a fusion algorithm, the motion of the carrier in an inertial reference coordinate system is determined, and meanwhile, the position of the carrier in the inertial reference coordinate system can also be calculated. Data (first attitude data) such as a three-dimensional attitude, an azimuth and the like subjected to temperature compensation are obtained through an embedded low-power ARM processor, and zero-drift three-dimensional attitude azimuth data expressed by quaternions and Euler angles are output in real time by using a quaternion-based three-dimensional algorithm and a special data fusion technology.
As an alternative implementation manner of this embodiment, as shown in fig. 2, between step S101 and step S103, the method further includes the following steps:
step S105, the scheduling server inquires the state of the unmanned transport vehicle within a preset range;
in step S105, the preset range may be a circular area with the initial position as a center of a circle and the preset distance as a radius, where the preset distance is not constant, and when the scheduling server cannot inquire the unmanned carrier within the preset range, the preset distance may be appropriately increased, so as to expand the preset range, so as to find a suitable unmanned carrier.
Step S106, when an unmanned carrying vehicle in an idle state exists in a preset range, the scheduling server generates a second optimal path according to the current position and the initial position of the unmanned carrying vehicle in the idle state;
step S107, the scheduling server sends a second instruction containing a second optimal path to the unmanned transport vehicle in the idle state;
step S108, the unmanned transport vehicle in the idle state responds to a second instruction, the inertial navigation system is used for acquiring second position data, the unmanned transport vehicle drives to an initial position according to the second position data and a second optimal path, and the step S103 is carried out;
when there is one empty automated guided vehicle within the preset range, the dispatch server sends a transportation task to the empty automated guided vehicle regardless of whether there is an empty automated guided vehicle within the preset range that is closer to the initial position and is in a working state. And the dispatching server calculates the shortest path from the current position to the initial position, namely the second optimal path, and sends the shortest path to the unmanned transport vehicle through the wireless communication network. The automated guided vehicle can autonomously travel to the initial position, and the detailed navigation principle is shown in step S104, which is not described herein again.
Step S109, when at least two unmanned carrying vehicles in an idle state exist in a preset range, the scheduling server screens out a first optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in the idle state according to waiting time;
step S110, the dispatching server sends a third instruction containing a third optimal path to the first optimal unmanned transport vehicle;
step S111, the first optimal automated guided vehicle responds to a third instruction, obtains third posture data by using an inertial navigation system, drives to an initial position according to the third posture data and a third optimal path, and enters step S103;
when there are a plurality of automated guided vehicles in an idle state within a preset range, it is necessary to find an automated guided vehicle that can reach the initial position at the fastest speed, that is, the waiting time is the shortest. The dispatch server sends a third command to the automated guided vehicle with the shortest waiting time through the wireless communication network, and the automated guided vehicle autonomously travels to the initial position, and the detailed navigation principle refers to step S104, which is not described herein again.
Step S112, when only one unmanned carrying vehicle in the working state exists in the preset range, the dispatching server generates a fourth optimal path according to the second target position and the initial position of the current task of the unmanned carrying vehicle in the working state;
step S113, after the unmanned transport vehicle in the working state completes the current task, the scheduling server sends a fourth instruction containing a fourth optimal path to the unmanned transport vehicle in the working state;
step S114, the unmanned transport vehicle which completes the current task responds to a fourth instruction, an inertial navigation system is used for obtaining fourth position data, the unmanned transport vehicle drives to an initial position according to the fourth position data and a fourth optimal path, and the step S103 is executed;
when no unmanned transport vehicle in an idle state exists in the preset range, the unmanned transport vehicle in a working state is considered, and the working state refers to the current transport task. If only one automated guided vehicle exists in the preset range and the state of the automated guided vehicle is in a working state, the scheduling server sends a request for acquiring a second target position of the current task to the automated guided vehicle through the wireless communication network, the automated guided vehicle returns to the second target position, the scheduling server calculates and generates a fourth optimal path from the second target position to the initial position, and directly sends a fourth instruction to the automated guided vehicle after the automated guided vehicle completes the current task and requires the automated guided vehicle to travel from a place where the task is completed to the initial position, and specific navigation principles refer to step S104 and are not repeated herein.
Step S115, when only at least two unmanned carrying vehicles in working states exist in a preset range, the scheduling server screens out a second optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in working states according to waiting time;
step S116, after the second optimal automated guided vehicle finishes the current task, the scheduling server sends a fifth instruction containing a fifth optimal path to the second optimal automated guided vehicle;
in step S117, the second optimal automated guided vehicle responds to the fifth instruction, acquires the fifth pose data by using the inertial navigation system, and travels to the initial position according to the fifth optimal path according to the fifth pose data, and the process advances to step S103.
If a plurality of automated guided vehicles exist in the preset range and the states of the automated guided vehicles are working states, the scheduling server selects to which automated guided vehicle to send the fifth instruction according to the total time of each automated guided vehicle for completing the current task and driving to the initial state, namely the waiting time, and after the selected automated guided vehicle completes the current task, the scheduling server directly sends the fifth instruction to the selected automated guided vehicle to request the selected automated guided vehicle to drive from the point where the task is completed to the initial position, and the specific navigation principle refers to step S104, which is not described herein again.
In this alternative embodiment, when the automated guided vehicle travels to the home position, the dispatch server is notified that the automated guided vehicle has reached the home position, the dispatch server transmits a first command to the automated guided vehicle, and the automated guided vehicle starts a transport job in response to the first command.
Optionally, as shown in fig. 3, step S109 specifically includes:
step S1091, the scheduling server respectively generates a first selectable optimal path of each unmanned carrier in the idle state according to the current position and the initial position of at least two unmanned carriers in the idle state;
step S1092, the scheduling server obtains a first average speed of each unmanned carrier in the idle state, and predicts according to the first average speed and a first optional optimal path to obtain a first predicted duration of each unmanned carrier in the idle state;
step S1093, the scheduling server determines the automated guided vehicle corresponding to the minimum first predicted time length in the first predicted time lengths as a first optimal automated guided vehicle, and determines a first selectable optimal path corresponding to the first optimal automated guided vehicle as a third optimal path.
The first predicted time length calculated through the first average speed and the first optional optimal path is the waiting time, and in order to start the carrying task as soon as possible, the automated guided vehicle which can arrive at the initial position as soon as possible needs to be found, so that the calculated first predicted time lengths are compared, the automated guided vehicle corresponding to the minimum first predicted time length meets the requirement, the first optional optimal path calculated in the step S1091 is the third optimal path, and the scheduling server does not need to calculate again.
Further, before step S1092, the method may further include:
each unmanned transport vehicle in an idle state collects a plurality of first speeds in a first preset time period, calculates a first average speed according to the plurality of first speeds, and sends the first average speed to a scheduling server; or
Each unmanned transport vehicle in an idle state collects a plurality of first speeds in a first preset time period and sends the first speeds to a scheduling server; the scheduling server calculates a first average speed of each of the automated guided vehicles in the idle state according to the plurality of first speeds.
The first preset time period may be a time period during which the last transport job of the automated guided vehicle continues, may be a set of transport job continuation time periods of the latest transport job, or may be a certain time period within the last transport job continuation time period. The first speed can be collected through a speed measurement sensor, and the sampling frequency can be preset.
The scheduling server obtains the first average speed in two ways, one is that each automated guided vehicle calculates the first average speed of the automated guided vehicle and sends the first average speed to the scheduling server, and the other is that the scheduling server calculates a plurality of first speeds sent by the automated guided vehicle to obtain the first average speed.
Optionally, as shown in fig. 4, step S115 specifically includes:
step S1151, the scheduling server respectively generates a second optional optimal path of each unmanned carrier in the working state according to the current positions of the at least two unmanned carriers in the working state and a third target position of the current task;
step S1152, the scheduling server respectively generates a third selectable optimal path of each unmanned carrier in the working state according to a third target position and an initial position of the current task of at least two unmanned carriers in the working state;
step S1153, the scheduling server obtains a second average speed of each unmanned transport vehicle in the working state, and predicts according to the second average speed, the second optional optimal path and the third optional optimal path to obtain a second predicted duration of each unmanned transport vehicle in the working state;
in step S1154, the scheduling server determines the automated guided vehicle corresponding to the second predicted time length that is the smallest among the second predicted time lengths as the second optimal automated guided vehicle, and determines the third selectable optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
For each automated guided vehicle in a working state, a second optional optimal path from the current position of the automated guided vehicle to a third target position of a currently executed task and a third optional optimal path from the third target position to an initial position are calculated, and then a second average speed is combined, so that a second predicted time length of each automated guided vehicle, namely waiting time, can be predicted, and in order to start a carrying task as soon as possible, an automated guided vehicle which can come to the initial position the fastest needs to be found, therefore, the automated guided vehicle with the smallest waiting time is selected to complete the carrying task, and the third optional optimal path calculated in step S1152 is the fifth optimal path, and the dispatching server does not need to recalculate.
Further, before step S1153, the method further includes:
each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period, calculates a second average speed according to the plurality of second speeds, and sends the second average speed to the scheduling server; or
Each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period and sends the second speeds to the scheduling server; and the dispatching server respectively calculates a second average speed of each unmanned transport vehicle in the working state according to the plurality of second speeds.
The second preset time period may be a time period during which the last transport job of the automated guided vehicle continues, may be a set of transport job continuation time periods of the latest transport job, or may be a certain time period within the last transport job continuation time period. The second speed can be collected by the speed measuring sensor, and the sampling frequency can be preset.
The scheduling server obtains the second average speed in two ways, one is that each automated guided vehicle calculates the second average speed of the automated guided vehicle and sends the second average speed to the scheduling server, and the other is that the scheduling server calculates a plurality of second speeds sent by the automated guided vehicle to obtain the second average speed.
It should be noted that the optimal path and the optional optimal path in this embodiment may be calculated by an a-x algorithm.
The second embodiment of the invention:
referring to fig. 5, a navigation control system for an automated guided vehicle according to an embodiment of the present invention mainly includes: an obtaining module 201, configured to obtain an initial position and a first target position of a cargo to be transported by a scheduling server; the first path generating module 202 is connected to the obtaining module 201, and is configured to generate a first optimal path according to the initial position and the first target position by the scheduling server; a first instruction sending module 203, connected to the first path generating module 202, configured to send a first instruction including a first optimal path to the automated guided vehicle in an idle state at an initial position by a scheduling server; and the first navigation module 204 is connected with the first instruction sending module 203 and is used for responding to the first instruction by the automated guided vehicle, acquiring first position and posture data by using the inertial navigation system, and carrying the goods to be carried to the first target position according to the first position and posture data and the first optimal path.
In this embodiment, the initial position and the first target position of the goods to be handled may be stored in the RFID tag in advance, and the RFID tag is attached to the goods to be handled. When the RFID electronic tags need to be carried, carrying personnel hold the RFID card reader to scan the RFID electronic tags, read pre-stored initial position information and first target position information, and send the information to a dispatching server through a wireless communication network (such as WIFI, 4G, 5G and the like). The scheduling server is generally arranged in a scheduling room of the factory building, and scheduling personnel manage the cargo carrying condition of the whole factory building.
In this embodiment, the scheduling server calculates a shortest path from the initial position to the first target position according to an a-x algorithm, which is the first optimal path.
In the present embodiment, the automated guided vehicle in the idle state is an automated guided vehicle that is not currently carrying a job and is in a standby state. All the unmanned transport vehicles in the plant send the positioning information and the state information (idle state and working state) of the unmanned transport vehicles to the scheduling server in real time. The scheduling server acquires the current unmanned transport vehicle which is located at the initial position and is in the idle state, and sends a first instruction containing a first optimal path to the current unmanned transport vehicle through the wireless communication network.
In this embodiment, after receiving first instruction, the automated guided vehicle analyzes and obtains first optimal path, then sends an indication signal to remind the carrier to place the goods to be carried on the vehicle, and this indication signal can be light flicker, music warning.
Each unmanned transport vehicle is provided with an accelerometer, an electronic compass, a gyroscope and other motion sensors, the acceleration of the unmanned transport vehicle is measured through the accelerometer, the magnetic heading of the unmanned transport vehicle is measured through the electronic compass, the rotation angular speed of the unmanned transport vehicle is measured through the gyroscope, the attitude information of the unmanned transport vehicle, such as a heading angle, a pitch angle, a roll angle and the like, is obtained through a fusion algorithm, the motion of the carrier in an inertial reference coordinate system is determined, and meanwhile, the position of the carrier in the inertial reference coordinate system can also be calculated. Data (first attitude data) such as a three-dimensional attitude, an azimuth and the like subjected to temperature compensation are obtained through an embedded low-power ARM processor, and zero-drift three-dimensional attitude azimuth data expressed by quaternions and Euler angles are output in real time by using a quaternion-based three-dimensional algorithm and a special data fusion technology.
As an alternative implementation manner of this embodiment, as shown in fig. 6, the system further includes: the query module 205 is connected to the obtaining module 201, and is configured to query, by the scheduling server, a state of the automated guided vehicle within a preset range after the scheduling server obtains the initial position and the first target position of the cargo to be transported; the second path generating module 206 is connected to the querying module 205, and is configured to, when there is an empty automated guided vehicle within a preset range, generate a second optimal path by the scheduling server according to the current position and the initial position of the empty automated guided vehicle; a second instruction sending module 207, connected to the second path generating module 206, for sending a second instruction including a second optimal path to the automated guided vehicle in the idle state by the scheduling server; the second navigation module 208 is respectively connected with the second instruction sending module 207 and the first instruction sending module 203, and is used for responding to a second instruction by the unmanned transport vehicle in the idle state, acquiring second position and posture data by using the inertial navigation system, and driving to the initial position according to the second position and posture data and a second optimal path; the first screening module 209 is connected with the query module 205, and is configured to, when at least two unmanned vehicles in an idle state exist within a preset range, screen, by the scheduling server, a first optimal unmanned vehicle from the at least two unmanned vehicles in the idle state according to the waiting time; a third instruction sending module 210, connected to the first screening module 209, configured to send a third instruction including a third optimal path to the first optimal automated guided vehicle by the scheduling server; the third navigation module 211 is connected to the third instruction sending module 210 and the first instruction sending module 203, and configured to, in response to the third instruction, the first optimal automated guided vehicle obtain third posture data by using the inertial navigation system, and drive to the initial position according to the third posture data and the third optimal path; the third path generating module 212 is connected to the querying module 205, and configured to, when only one automated guided vehicle in the working state exists within a preset range, generate a fourth optimal path by the scheduling server according to the second target position and the initial position of the current task of the automated guided vehicle in the working state; a fourth instruction sending module 213, connected to the third path generating module 212, configured to send, by the scheduling server, a fourth instruction including a fourth optimal path to the automated guided vehicle in the working state after the automated guided vehicle in the working state completes the current task; the fourth navigation module 214 is respectively connected with the fourth instruction sending module 213 and the first instruction sending module 203, and is used for responding to the fourth instruction by the unmanned transport vehicle which completes the current task, acquiring fourth posture data by using the inertial navigation system, and driving to the initial position according to the fourth posture data and a fourth optimal path; the second screening module 215 is connected to the query module 205, and configured to screen, by the scheduling server, a second optimal automated guided vehicle from the at least two automated guided vehicles in the working state according to the waiting time when only the at least two automated guided vehicles in the working state are within the preset range; a fifth instruction sending module 216, connected to the second screening module 215, configured to send a fifth instruction including a fifth optimal path to the second optimal automated guided vehicle after the second optimal automated guided vehicle completes the current task; and the fifth navigation module 217 is connected with the fifth instruction sending module 216 and the first instruction sending module 203 respectively, and is configured to, in response to the fifth instruction, acquire fifth pose data by using the inertial navigation system, and drive to the initial position according to the fifth pose data and the fifth optimal path.
In this optional embodiment, the preset range may be a circular area with the initial position as a center of a circle and the preset distance as a radius, where the preset distance is not a constant, and when the scheduling server cannot inquire the unmanned carrier vehicle within the preset range, the preset distance may be appropriately increased, so as to expand the preset range, and thus find a suitable unmanned carrier vehicle.
When there is one empty automated guided vehicle within the preset range, the dispatch server sends a transportation task to the empty automated guided vehicle regardless of whether there is an empty automated guided vehicle within the preset range that is closer to the initial position and is in a working state. And the dispatching server calculates the shortest path from the current position to the initial position, namely the second optimal path, and sends the shortest path to the unmanned transport vehicle through the wireless communication network. The automated guided vehicle can autonomously travel to the initial position, and the specific navigation principle is as described above, which is not described herein again.
When no unmanned transport vehicle in an idle state exists in the preset range, the unmanned transport vehicle in a working state is considered, and the working state refers to the current transport task. If only one automated guided vehicle exists in the preset range and the state of the automated guided vehicle is in a working state, the scheduling server sends a request for acquiring a second target position of the current task to the automated guided vehicle through the wireless communication network, the automated guided vehicle returns to the second target position, the scheduling server calculates and generates a fourth optimal path from the second target position to the initial position, and directly sends a fourth instruction to the automated guided vehicle after the automated guided vehicle completes the current task and requires the automated guided vehicle to travel from a place where the task is completed to the initial position, and the specific navigation principle is as described above, and is not repeated here.
If a plurality of automated guided vehicles exist in the preset range and the states of the automated guided vehicles are working states, the scheduling server selects which automated guided vehicle to send the fifth instruction according to the total time of each automated guided vehicle for completing the current task and driving to the initial state, namely the waiting time, and after the selected automated guided vehicle completes the current task, the scheduling server directly sends the fifth instruction to the selected automated guided vehicle to request the selected automated guided vehicle to drive from the place where the task is completed to the initial position.
In this alternative embodiment, when the automated guided vehicle travels to the home position, the dispatch server is notified that the automated guided vehicle has reached the home position, the dispatch server transmits a first command to the automated guided vehicle, and the automated guided vehicle starts a transport job in response to the first command.
Optionally, as shown in fig. 7, the first filtering module 209 includes: a fourth path generating module 2091, configured to respectively generate, by the scheduling server, a first selectable optimal path for each idle automated guided vehicle according to the current position and the initial position of at least two idle automated guided vehicles; the first time length prediction module 2092 is connected to the fourth path generation module 2091, and configured to obtain, by the scheduling server, a first average speed of each idle automated guided vehicle, and perform prediction according to the first average speed and the first optional optimal path, to obtain a first predicted time length of each idle automated guided vehicle; the first determining module 2093 is connected to the first time length predicting module 2092 and configured to determine, by the scheduling server, the automated guided vehicle corresponding to the smallest first predicted time length of the first predicted time lengths as the first optimal automated guided vehicle and determine the first selectable optimal path corresponding to the first optimal automated guided vehicle as the third optimal path.
The first predicted time length calculated by the first average speed and the first optional optimal path is the waiting time, and in order to start the carrying task as soon as possible, the automated guided vehicle which can arrive at the initial position as soon as possible needs to be found, so that the calculated first predicted time lengths are compared, the automated guided vehicle corresponding to the minimum first predicted time length meets the requirement, the first optional optimal path calculated by the fourth path generating module 2091 is the third optimal path, and the scheduling server does not need to recalculate.
Further, as shown in fig. 7, the first filtering module 209 further includes: a first speed acquisition 2094 to acquire a plurality of first speeds within a first preset time period for each of the automated guided vehicles in an idle state; a first average speed calculation module 2095, coupled to the first speed acquisition module 2094, to calculate a first average speed for each empty automated guided vehicle based on the plurality of first speeds; a first average speed sending module 2096, connected to the first average speed calculating module 2095 and the first time length predicting module 2092, respectively, is configured to send the first average speed to the scheduling server for each idle automated guided vehicle.
Alternatively, as shown in fig. 8, the first filtering module 209 further includes: a first speed acquisition module 2094 to acquire a plurality of first speeds for each of the automated guided vehicles in an idle state for a first preset time period; a first speed sending module 2097, connected to the first speed collecting module 2094, for sending a plurality of first speeds to the dispatch server for each empty automated guided vehicle; the second average speed calculation module 2098 is connected to the first speed transmission module 2097 and the first duration prediction module 2092, respectively, and is configured to calculate, by the dispatch server, a first average speed of each of the automated guided vehicles in the idle state according to the plurality of first speeds, respectively.
The first preset time period may be a time period during which the last transport job of the automated guided vehicle continues, may be a set of transport job continuation time periods of the latest transport job, or may be a certain time period within the last transport job continuation time period. The first speed can be collected through a speed measurement sensor, and the sampling frequency can be preset.
The scheduling server obtains the first average speed in two ways, one is that each automated guided vehicle calculates the first average speed of the automated guided vehicle and sends the first average speed to the scheduling server, and the other is that the scheduling server calculates a plurality of first speeds sent by the automated guided vehicle to obtain the first average speed.
Optionally, as shown in fig. 9, the second filtering module 215 includes: a fifth path generating module 2151, configured to respectively generate, by the scheduling server, a second optional optimal path for each automated guided vehicle in the working state according to the current positions of the at least two automated guided vehicles in the working state and a third target position of the current task; a sixth path generating module 2152, configured to respectively generate, by the scheduling server, a third selectable optimal path for each automated guided vehicle in the working state according to a third target position and an initial position of a current task of at least two automated guided vehicles in the working state; a second time duration prediction module 2153, connected to the fifth path generation module 2151 and the sixth path generation module 2152, respectively, and configured to obtain, by the scheduling server, a second average speed of each automated guided vehicle in the working state, and perform prediction according to the second average speed, the second optional optimal path, and the third optional optimal path, to obtain a second predicted time duration of each automated guided vehicle in the working state; the second determining module 2154 is connected to the second duration predicting module 2153, and is configured to determine, by the scheduling server, the automated guided vehicle corresponding to the second predicted duration that is the smallest in the second predicted duration as the second optimal automated guided vehicle, and determine the third selectable optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
For each automated guided vehicle in a working state, a second optional optimal path from the current position of the automated guided vehicle to a third target position of a currently executed task and a third optional optimal path from the third target position to an initial position are calculated, and then a second average speed is combined, so that a second predicted time length of each automated guided vehicle, namely waiting time, can be predicted, and in order to start a carrying task as soon as possible, an automated guided vehicle which can come to the initial position the fastest needs to be found, therefore, the automated guided vehicle with the smallest waiting time is selected to complete the carrying task, the third optional optimal path calculated by the sixth path generation module 2152 is the fifth optimal path, and the scheduling server does not need to calculate again.
Further, as shown in fig. 9, the second filtering module 215 further includes: a second speed acquisition module 2155, configured to acquire a plurality of second speeds within a second preset time period for each automated guided vehicle in a working state; a third average speed calculation module 2156 connected to the second speed acquisition module 2155, for calculating a second average speed for each of the unmanned vehicles in the working state according to the plurality of second speeds; a second average speed sending module 2157, connected to the third average speed calculating module 2156 and the second duration predicting module 2153, respectively, for sending the second average speed to the scheduling server by each unmanned transport vehicle in working state
Alternatively, as shown in fig. 10, the second filtering module 215 further includes: a second speed acquisition module 2155, configured to acquire a plurality of second speeds within a second preset time period for each automated guided vehicle in a working state; a second speed sending module 2158 connected to the second speed collecting module 2155, for sending a plurality of second speeds to the scheduling server by each automated guided vehicle in a working state; and a fourth average speed calculation module 2159 connected to the second speed transmission module 2158 and the second duration prediction module 2153, respectively, and the scheduling server calculates a second average speed of each of the automated guided vehicles in the working state according to the plurality of second speeds, respectively.
The second preset time period may be a time period during which the last transport job of the automated guided vehicle continues, may be a set of transport job continuation time periods of the latest transport job, or may be a certain time period within the last transport job continuation time period. The second speed can be collected by the speed measuring sensor, and the sampling frequency can be preset.
The scheduling server obtains the second average speed in two ways, one is that each automated guided vehicle calculates the second average speed of the automated guided vehicle and sends the second average speed to the scheduling server, and the other is that the scheduling server calculates a plurality of second speeds sent by the automated guided vehicle to obtain the second average speed.
It should be noted that the optimal path and the optional optimal path in this embodiment may be calculated by an a-x algorithm.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.

Claims (10)

1. A method for controlling navigation of an automated guided vehicle, comprising:
the method comprises the steps that a scheduling server obtains an initial position and a first target position of goods to be carried;
the scheduling server generates a first optimal path according to the initial position and the first target position;
the dispatch server sending a first instruction including the first optimal path to an automated guided vehicle in the initial position and in an idle state;
and the automated guided vehicle responds to the first instruction, acquires first position data by using an inertial navigation system, and conveys the goods to be conveyed to the first target position according to the first position data and the first optimal path.
2. The automated guided vehicle navigation control method of claim 1, further comprising, after the dispatch server obtains an initial position and a first target position of a load to be handled and before the dispatch server sends a first instruction including the first optimal path to the automated guided vehicle located at the initial position:
the scheduling server inquires the state of the unmanned transport vehicle within a preset range;
when one unmanned carrying vehicle in an idle state exists in the preset range, the scheduling server generates a second optimal path according to the current position and the initial position of the unmanned carrying vehicle in the idle state, and sends a second instruction containing the second optimal path to the unmanned carrying vehicle in the idle state; the unmanned transport vehicle in the idle state responds to the second instruction, acquires second position and posture data by using the inertial navigation system, and drives to the initial position according to the second optimal path according to the second position and posture data;
when at least two unmanned carrying vehicles in idle states exist in the preset range, the scheduling server screens out a first optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in idle states according to waiting time, and sends a third instruction containing a third optimal path to the first optimal unmanned carrying vehicle; the first optimal unmanned transport vehicle responds to the third instruction, acquires third posture data by using the inertial navigation system, and drives to the initial position according to the third posture data and the third optimal path;
when only one unmanned carrying vehicle in the working state exists in the preset range, the dispatching server generates a fourth optimal path according to a second target position of the current task of the unmanned carrying vehicle in the working state and the initial position; after the current task is completed by the automated guided vehicle in the working state, the scheduling server sends a fourth instruction containing the fourth optimal path to the automated guided vehicle in the working state; responding to the fourth instruction by the unmanned transport vehicle which finishes the current task, acquiring fourth posture data by using the inertial navigation system, and driving to the initial position according to the fourth posture data and the fourth optimal path;
when only at least two unmanned carrying vehicles in working states exist in the preset range, the scheduling server screens out a second optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in working states according to waiting time; after the second optimal automated guided vehicle completes the current task, the scheduling server sends a fifth instruction containing a fifth optimal path to the second optimal automated guided vehicle; the second optimal automated guided vehicle responds to the fifth instruction, acquires fifth pose data by using the inertial navigation system, and travels to the initial position according to the fifth optimal path according to the fifth pose data.
3. The automated guided vehicle navigation control method of claim 2, wherein the dispatch server screens out a first optimal automated guided vehicle from the at least two automated guided vehicles in an idle state based on a wait time, comprising:
the scheduling server respectively generates a first selectable optimal path of each unmanned carrier in the idle state according to the current positions and the initial positions of the at least two unmanned carriers in the idle state;
the scheduling server acquires a first average speed of each unmanned carrier in the idle state, and predicts according to the first average speed and the first optional optimal path to obtain a first predicted duration of each unmanned carrier in the idle state;
the scheduling server determines the automated guided vehicle corresponding to the first predicted time length with the minimum first predicted time length in the first predicted time length as the first optimal automated guided vehicle, and determines the first optional optimal path corresponding to the first optimal automated guided vehicle as the third optimal path; and/or
The scheduling server screens out a second optimal automated guided vehicle from the at least two automated guided vehicles in the working state according to the waiting time, and the method comprises the following steps:
the scheduling server respectively generates a second optional optimal path of each unmanned carrier in the working state according to the current positions of the at least two unmanned carriers in the working state and a third target position of the current task;
the scheduling server respectively generates a third optional optimal path of each unmanned carrier in the working state according to a third target position and the initial position of the current task of the at least two unmanned carriers in the working state;
the scheduling server acquires a second average speed of each unmanned carrier in the working state, and predicts according to the second average speed, the second optional optimal path and the third optional optimal path to obtain a second predicted duration of each unmanned carrier in the working state;
and the scheduling server determines the automated guided vehicle corresponding to the second predicted time length with the minimum second predicted time length in the second predicted time lengths as the second optimal automated guided vehicle, and determines a third optional optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
4. The automated guided vehicle navigation control method of claim 3, further comprising, before the dispatch server obtains the first average speed for each automated guided vehicle in the idle state:
each unmanned transport vehicle in an idle state collects a plurality of first speeds in a first preset time period, calculates a first average speed according to the first speeds, and sends the first average speed to the scheduling server; or
Each unmanned carrier in an idle state collects a plurality of first speeds in a first preset time period and sends the first speeds to the dispatching server; the scheduling server calculates a first average speed of each unmanned transport vehicle in an idle state according to the plurality of first speeds.
5. The automated guided vehicle navigation control method of claim 3, further comprising, before the dispatch server obtains the second average speed of each automated guided vehicle in the active state:
each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period, calculates a second average speed according to the second speeds, and sends the second average speed to the dispatching server; or
Each unmanned transport vehicle in the working state collects a plurality of second speeds in a second preset time period and sends the second speeds to the scheduling server; and the dispatching server respectively calculates a second average speed of each unmanned transport vehicle in the working state according to the plurality of second speeds.
6. An automated guided vehicle navigation control system, comprising:
the acquisition module is used for acquiring an initial position and a first target position of the goods to be transported by the dispatching server;
the first path generation module is connected with the acquisition module and used for generating a first optimal path by the scheduling server according to the initial position and the first target position;
a first instruction sending module, connected to the first path generating module, for sending a first instruction including the first optimal path to the automated guided vehicle located at the initial position and in an idle state by the dispatch server;
and the first navigation module is connected with the first instruction sending module and used for responding to the first instruction by the unmanned transport vehicle, acquiring first position and attitude data by using an inertial navigation system, and transporting the goods to be transported to the first target position according to the first position and attitude data and the first optimal path.
7. The automated guided vehicle navigation control system of claim 6, further comprising:
the query module is connected with the acquisition module and used for querying the state of the unmanned transport vehicle in a preset range after the scheduling server acquires the initial position and the first target position of the goods to be transported;
the second path generation module is connected with the query module and used for generating a second optimal path according to the current position of the unmanned carrying vehicle in the idle state and the initial position when the unmanned carrying vehicle in the idle state exists in the preset range;
a second instruction sending module, connected to the second path generating module, configured to send, by the scheduling server, a second instruction including the second optimal path to the automated guided vehicle in the idle state;
the second navigation module is respectively connected with the second instruction sending module and the first instruction sending module, and is used for responding to the second instruction by the unmanned transport vehicle in the idle state, acquiring second position and posture data by using the inertial navigation system, and driving to the initial position according to the second position and posture data and the second optimal path;
the first screening module is connected with the query module and used for screening out a first optimal unmanned carrying vehicle from the at least two unmanned carrying vehicles in the idle state according to waiting time when at least two unmanned carrying vehicles in the idle state exist in the preset range;
a third instruction sending module, connected to the first screening module, for sending, by the dispatch server, a third instruction including a third optimal path to the first optimal automated guided vehicle;
the third navigation module is respectively connected with the third instruction sending module and the first instruction sending module, and is used for responding to the third instruction by the first optimal unmanned transport vehicle, acquiring third posture data by using the inertial navigation system, and driving to the initial position according to the third posture data and the third optimal path;
the third path generating module is connected with the query module and used for generating a fourth optimal path according to the second target position and the initial position of the current task of the unmanned carrying vehicle in the working state when only one unmanned carrying vehicle in the working state exists in the preset range;
a fourth instruction sending module, connected to the third path generating module, configured to send, by the scheduling server, a fourth instruction including the fourth optimal path to the automated guided vehicle in the operating state after the automated guided vehicle in the operating state completes the current task;
the fourth navigation module is respectively connected with the fourth instruction sending module and the first instruction sending module, and is used for responding to the fourth instruction by the unmanned transport vehicle which completes the current task, acquiring fourth posture data by using the inertial navigation system, and driving to the initial position according to the fourth posture data and the fourth optimal path;
the second screening module is connected with the query module and used for screening out a second optimal unmanned transport vehicle from the at least two unmanned transport vehicles in the working state according to the waiting time when only the at least two unmanned transport vehicles in the working state exist in the preset range;
a fifth instruction sending module, connected to the second screening module, configured to send, by the scheduling server, a fifth instruction including a fifth optimal path to the second optimal automated guided vehicle after the second optimal automated guided vehicle completes the current task;
and the fifth navigation module is respectively connected with the fifth instruction sending module and the first instruction sending module, and is used for responding to the fifth instruction by the second optimal unmanned transport vehicle, acquiring fifth pose data by using the inertial navigation system, and driving to the initial position according to the fifth pose data and the fifth optimal path.
8. The automated guided vehicle navigation control system of claim 7, wherein the first screening module comprises:
a fourth path generating module, configured to generate, by the scheduling server, a first selectable optimal path for each idle automated guided vehicle according to the current positions and the initial positions of the at least two idle automated guided vehicles, respectively;
the first time length prediction module is connected with the fourth path generation module and used for the scheduling server to acquire a first average speed of each unmanned transport vehicle in the idle state, and to predict according to the first average speed and the first optional optimal path to obtain a first predicted time length of each unmanned transport vehicle in the idle state;
a first determining module, connected to the first time length predicting module, configured to determine, by the scheduling server, the automated guided vehicle corresponding to the smallest first predicted time length in the first predicted time lengths as the first optimal automated guided vehicle, and determine a first selectable optimal path corresponding to the first optimal automated guided vehicle as the third optimal path; and/or
The second screening module includes:
a fifth path generating module, configured to generate, by the scheduling server, a second optional optimal path for each automated guided vehicle in the working state according to the current positions of the at least two automated guided vehicles in the working state and a third target position of the current task, respectively;
a sixth path generating module, configured to generate, by the scheduling server, a third selectable optimal path for each automated guided vehicle in the working state according to a third target position and the initial position of the current task of the at least two automated guided vehicles in the working state, respectively;
a second duration prediction module, connected to the fifth path generation module and the sixth path generation module, respectively, for the scheduling server to obtain a second average speed of each automated guided vehicle in a working state, and perform prediction according to the second average speed, the second optional optimal path, and the third optional optimal path to obtain a second predicted duration of each automated guided vehicle in the working state;
a second determining module, connected to the second duration predicting module, configured to determine, by the scheduling server, the automated guided vehicle corresponding to the second predicted duration that is the smallest among the second predicted durations as the second optimal automated guided vehicle, and determine a third selectable optimal path corresponding to the second optimal automated guided vehicle as the fifth optimal path.
9. The automated guided vehicle navigation control system of claim 8, wherein the first screening module further comprises:
the first speed acquisition module is used for acquiring a plurality of first speeds in a first preset time period by each unmanned transport vehicle in an idle state;
a first average speed calculation module connected to the first speed acquisition module, for calculating the first average speed according to the plurality of first speeds for each empty automated guided vehicle;
the first average speed sending module is respectively connected with the first average speed calculating module and the first time length predicting module and is used for sending the first average speed to the dispatching server by each unmanned transport vehicle in an idle state; or
The first speed acquisition module is used for acquiring a plurality of first speeds in a first preset time period by each unmanned transport vehicle in an idle state;
the first speed sending module is connected with the first speed acquisition module and used for sending the plurality of first speeds to the dispatching server by each unmanned carrier in an idle state;
and the second average speed calculating module is respectively connected with the first speed sending module and the first time length predicting module and is used for the dispatching server to respectively calculate the first average speed of each unmanned transport vehicle in an idle state according to the plurality of first speeds.
10. The automated guided vehicle navigation control system of claim 8, wherein the second screening module further comprises:
the second speed acquisition module is used for acquiring a plurality of second speeds in a second preset time period by each unmanned transport vehicle in a working state;
the third average speed calculation module is connected with the second speed acquisition module and used for calculating the second average speed of each unmanned transport vehicle in the working state according to the plurality of second speeds;
the second average speed sending module is respectively connected with the third average speed calculating module and the second duration predicting module and is used for sending the second average speed to the dispatching server by each unmanned transport vehicle in a working state; or
The second speed acquisition module is used for acquiring a plurality of second speeds in a second preset time period by each unmanned transport vehicle in a working state;
the second speed sending module is connected with the second speed acquisition module and used for sending the plurality of second speeds to the dispatching server by each unmanned transport vehicle in a working state;
and the fourth average speed calculation module is respectively connected with the second speed sending module and the second duration prediction module, and the scheduling server respectively calculates a second average speed of each unmanned transport vehicle in a working state according to the plurality of second speeds.
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