CN116661467A - AGV robot walking path intelligent control system based on digital image processing - Google Patents
AGV robot walking path intelligent control system based on digital image processing Download PDFInfo
- Publication number
- CN116661467A CN116661467A CN202310953743.XA CN202310953743A CN116661467A CN 116661467 A CN116661467 A CN 116661467A CN 202310953743 A CN202310953743 A CN 202310953743A CN 116661467 A CN116661467 A CN 116661467A
- Authority
- CN
- China
- Prior art keywords
- agv robot
- target
- target agv
- intersection
- path
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012545 processing Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 47
- 238000012216 screening Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims description 59
- 239000000463 material Substances 0.000 claims description 43
- 238000005286 illumination Methods 0.000 claims description 18
- 238000012544 monitoring process Methods 0.000 claims description 18
- 238000009825 accumulation Methods 0.000 claims description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 8
- 238000010586 diagram Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000002352 surface water Substances 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 abstract description 7
- 230000032258 transport Effects 0.000 description 7
- 230000001105 regulatory effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Manipulator (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to the field of AGV robot walking path control, and particularly discloses an AGV robot walking path intelligent control system based on digital image processing. Acquiring road information of each feasible path of the target AGV robot, analyzing recommendation coefficients of each feasible path of the target AGV robot, obtaining the optimal feasible path of the target AGV robot, enriching diversification of path evaluation indexes, perfecting a path evaluation system, and facilitating screening out the optimal path; the method comprises the steps of obtaining the transportation safety coefficient of the target AGV robot for carrying cargoes currently, analyzing the speed regulation direction and the speed regulation quantity of the target AGV robot at a congestion intersection, realizing the regulation and control of the running speed of the AGV robot at the congestion intersection, and ensuring the cargo safety and the running safety of the AGV robot.
Description
Technical Field
The invention relates to the field of AGV robot walking path control, in particular to an AGV robot walking path intelligent control system based on digital image processing.
Background
The control of the AGV robot walking path has important significance for realizing efficient and safe logistics operation and improving production efficiency. Through reasonable planning route and the regulation and control to AGV robot running state, can ensure the quick, the accurate conveying of material, realize logistics operation's security and high efficiency.
The existing control method for the traveling path of the AGV robot has some defects: firstly, in the aspect of path planning, some paths do not directly pass through an obstacle area, but are very close to the obstacle area, because the AGV robot has a volume, the paths are defined as infeasible paths for the safety of the AGV robot in operation, however, the infeasible paths are not excluded in the conventional path planning, and even the infeasible paths are possibly selected as the optimal running paths of the AGV robot, so that potential safety hazards exist in the running paths of the AGV robot.
Secondly, in the aspect of path screening, indexes of the path analyzed by the existing method are not comprehensive enough, such as recommended coefficients of the path are evaluated only from the angles of the length of the route, the time required by traffic and road surface information, and the turning times, the turning radius, the gradient, the flatness, the illumination brightness and the like of the path are not deeply analyzed, so that a path evaluation system of the existing method is not perfect enough, an optimal path of the AGV robot cannot be screened well, and efficient operation of the AGV robot cannot be guaranteed.
Thirdly, in the aspect of driving control, especially when the AGV robot passes through the traffic intersection of a plurality of AGV robots, in order to prevent collision or friction with other AGV robots when the AGV robot transports goods, the driving speed of the AGV robot needs to be controlled, if for avoiding other AGV robots, the acceleration is passed or the deceleration is passed and the acceleration and deceleration force is regulated and controlled, meanwhile, the state of the AGV robot for carrying goods needs to be considered, and the goods safety and the driving safety of the AGV robot are ensured.
Disclosure of Invention
Aiming at the problems, the invention provides an AGV robot walking path intelligent control system based on digital image processing, which realizes the function of controlling the AGV robot walking path.
The technical scheme adopted for solving the technical problems is as follows: the invention provides an AGV robot walking path intelligent control system based on digital image processing, which comprises: and the AGV robot feasible path acquisition module is used for acquiring environmental information of the material warehouse, planning a traveling path of the target AGV robot according to the starting point position and the end point position of the target AGV robot, and obtaining all feasible paths of the target AGV robot.
The feasible path road information acquisition module is used for acquiring the road information of each feasible path of the target AGV robot, wherein the road information comprises a route length, a driving duration, a road condition hidden danger coefficient and a traffic technology difficulty coefficient.
AGV robot best route screening module: and the method is used for analyzing the recommended coefficient of each feasible path of the target AGV robot according to the road information of each feasible path of the target AGV robot to obtain the optimal feasible path of the target AGV robot.
The AGV robot traveling road junction congestion identification module is used for acquiring the position and traveling speed of each AGV robot waiting to pass at the road junction in front of the traveling path of the target AGV robot, judging whether the road junction in front of the traveling path of the target AGV robot is congested, and if so, acquiring the time required for each competing AGV robot at the road junction in front of the traveling path of the target AGV robot to reach the road junction.
The AGV robot current carrying cargo analysis module is used for acquiring basic information of the target AGV robot current carrying cargo, wherein the basic information comprises type, volume, weight, height and effective lifting area proportionality coefficient, and the transport safety coefficient of the target AGV robot current carrying cargo is obtained through analysis.
AGV robot blocks up crossing speed regulation and control module: the speed regulation and control direction and speed regulation quantity of the target AGV robot at the congestion crossing are obtained according to the transportation safety coefficient of the target AGV robot for carrying cargoes at present.
And the database is used for storing a relation function between the size information of the AGV robot and the proportional magnification of the obstacle area in the plan view of the material warehouse, and storing the reference running speeds corresponding to the road sections of all types and the fragile coefficients corresponding to the cargoes of all types.
Based on the above embodiment, the specific analysis process of the AGV robot viable path acquisition module is: and acquiring a panoramic image of the material warehouse through the high-definition camera, constructing a three-dimensional model of the material warehouse, acquiring a top view of the material warehouse, and marking each obstacle area in the top view of the material warehouse.
And acquiring size information of the target AGV robot, analyzing reference proportion magnification of the obstacle regions in the top view of the material warehouse, and carrying out proportion magnification on the obstacle regions in the top view of the material warehouse to obtain an expansion map of the obstacle regions in the top view of the material warehouse.
And marking the starting point position and the end point position of the target AGV robot in the plan view of the material warehouse, and planning the traveling path of the target AGV robot according to the expansion diagram of each obstacle area in the plan view of the material warehouse to obtain each feasible path of the target AGV robot.
On the basis of the above embodiment, the specific analysis process of the feasible path road information acquisition module includes: the route length of each feasible path of the target AGV robot is obtained and is recorded as，/>Indicate->The number of the possible paths is given,。
acquiring the driving time length of each feasible path of the target AGV robot and recording the driving time length as。
On the basis of the embodiment, the feasible path road information acquisition moduleThe concrete analysis process of the block further comprises: obtaining the maximum gradient of each road section in each feasible path of the target AGV robot, analyzing the gradient coefficient of each feasible path of the target AGV robot, and marking the gradient coefficient as。
Obtaining the average flatness of each road section in each feasible path of the target AGV robot, analyzing the flatness coefficient of each feasible path of the target AGV robot, and marking the flatness coefficient as。
Acquiring the pavement water accumulation area of each road section in each feasible path of the target AGV robot, analyzing the pavement water accumulation coefficient of each feasible path of the target AGV robot, and marking the pavement water accumulation coefficient as。
Acquiring illumination brightness of each road section in each feasible path of the target AGV robot, analyzing illumination brightness coefficients of each feasible path of the target AGV robot, and marking the illumination brightness coefficients as。
By analysis of formulasObtaining road condition hidden danger coefficients of each feasible path of target AGV robot>Wherein->Representing natural constant->、/>、/>、/>Respectively representing the weight factors of a preset gradient coefficient, a preset flatness coefficient, a preset road surface water accumulation coefficient and a preset illumination brightness coefficient.
On the basis of the above embodiment, the specific analysis process of the feasible path road information acquisition module further includes: acquiring the angles of each turning in each feasible path of the target AGV robot and recording the angles as，/>Indicate->Number of secondary turns, ++>。
By analysis of formulasObtaining the passing technical difficulty coefficient of each feasible path of the target AGV robot>Wherein->And representing the influence factors corresponding to the preset unit turning angles.
Based on the above embodiment, the specific analysis process of the optimal path screening module of the AGV robot is as follows: by analysis of formulasObtaining recommended coefficients of each feasible path of target AGV robot>Wherein->Represents the number of possible paths, +.>、/>、/>、/>Weights respectively representing preset route length, running duration, road condition hidden danger coefficient and traffic technology difficulty coefficient, ++>+/>+/>+=1。
And comparing the recommended coefficients of all the feasible paths of the target AGV robot, and taking the feasible path corresponding to the maximum recommended coefficient as the best feasible path of the target AGV robot.
Based on the above embodiment, the specific process of the AGV robot driving intersection congestion identification module is as follows:taking the intersection in front of the traveling path of the target AGV robot as a circle center, taking the set distance as a radius as a circle, obtaining the monitoring range of the intersection in front of the traveling path of the target AGV robot, and recording the monitoring range as the monitoring range of the intersection in front.
And acquiring the running directions of all AGV robots and all AGV robots in the monitoring range of the front intersection, analyzing to obtain all AGV robots waiting to pass at the front intersection, further acquiring the positions and the running speeds of all AGV robots waiting to pass at the front intersection, and analyzing to obtain the time of all AGV robots waiting to pass at the front intersection to reach the intersection.
And acquiring the time of the target AGV robot reaching the intersection.
Comparing the time of each AGV robot waiting to pass at the front intersection with the time of each target AGV robot waiting to pass at the front intersection, obtaining the interval duration between the time of each AGV robot waiting to pass at the front intersection and the time of each target AGV robot waiting to pass at the front intersection, and marking the interval duration as the vehicle meeting interval duration of each AGV robot waiting to pass at the front intersection.
Comparing the interval duration of the meeting of each AGV robot waiting to pass at the front intersection with a preset interval duration threshold, if the interval duration of the meeting of each AGV robot waiting to pass at the front intersection is greater than or equal to the preset interval duration threshold, the intersection in front of the traveling path of the target AGV robot is not congested, otherwise, the intersection in front of the traveling path of the target AGV robot is congested, and executing>。
: and marking the AGV robots with the meeting interval time length smaller than the preset meeting interval time length threshold as competing AGV robots of the intersection in front of the traveling path of the target AGV robot, counting to obtain each competing AGV robot of the intersection in front of the traveling path of the target AGV robot, and further obtaining the time length required by each competing AGV robot of the intersection in front of the traveling path of the target AGV robot to reach the intersection.
Based on the above embodiment, the specific analysis process of the current cargo carrying analysis module of the AGV robot is as follows: the method comprises the steps of obtaining the type of the goods currently carried by the target AGV robot, extracting the fragile coefficients corresponding to various types of goods stored in a database, and screening to obtain the goods currently carried by the target AGV robotThe damage coefficient is recorded as。
The volume, weight and height of the current carried goods of the target AGV robot are obtained and respectively recorded as、/>、/>。
The total area of the lifting surface of the target AGV robot for carrying the goods at present is obtained and is recorded asAnd the area of the current carrying cargo lifting surface of the target AGV robot, which is in contact with the target AGV robot, is obtained and is marked as +.>。
By analysis of formulasObtaining the effective lifting area proportion coefficient of the target AGV robot for carrying the goods at present>。
By analysis of formulasObtaining the transport safety factor of the target AGV robot currently carrying goods +.>Wherein->、/>、/>Representing the preset cargo volume, weight and height thresholds, respectively.
On the basis of the embodiment, the analysis process of the speed regulation module of the AGV robot congestion intersection is as follows:: comparing the transportation safety coefficient of the current transported goods of the target AGV robot with a preset transportation safety coefficient threshold value, if the transportation safety coefficient of the current transported goods of the target AGV robot is greater than or equal to the preset transportation safety coefficient threshold value, accelerating the speed regulation and control direction of the target AGV robot at a congestion crossing, and executing ++>Otherwise, the speed regulation direction of the target AGV robot at the congested intersection is deceleration, and +.>。
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->。
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the minimum time length required by the competing AGV robot to reach the intersection, and marking the minimum time length as。
By analysis of formulasObtaining
Speed adjustment quantity of target AGV robot at congested crossingWherein->Compensation amount representing speed adjustment amount of preset target AGV robot during acceleration, +.>。
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->。
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the maximum value of the required time length of each competing AGV robot at the intersection, and marking the maximum value as。
By analysis of formulasObtaining speed adjustment quantity +.>Wherein->Compensation amount representing speed adjustment amount at deceleration of preset target AGV robot, +.>。
Compared with the prior art, the intelligent control system for the travel path of the AGV robot based on digital image processing has the following beneficial effects: 1. according to the invention, the size information of the target AGV robot is acquired, the expansion map of each obstacle area in the top view of the material warehouse is analyzed, and each feasible path of the target AGV robot is planned by combining the starting point position and the end point position of the target AGV robot, so that the number range of the feasible paths of the AGV robot can be reduced, and the optimal path of the AGV robot can be screened efficiently and accurately.
2. According to the invention, the recommended coefficients of all feasible paths of the target AGV robot are analyzed by obtaining the route length, the driving time, the road condition hidden danger coefficient and the traffic technology difficulty coefficient of all feasible paths of the target AGV robot, so that the optimal feasible paths of the target AGV robot are obtained, the diversity of path evaluation indexes is enriched, a path evaluation system can be perfected, the optimal paths of the AGV robot are conveniently screened out, and the efficient operation of the AGV robot is ensured.
3. According to the invention, the basic information of the target AGV robot for carrying the goods at present is obtained, the transportation safety coefficient of the target AGV robot for carrying the goods at present is analyzed, the speed regulation direction and the speed regulation quantity of the target AGV robot at the congested crossing are further obtained, the running speed of the AGV robot at the congested crossing is regulated and controlled, and the goods safety and the running safety of the AGV robot are ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Fig. 2 is a top view of the material warehouse of the present invention.
Fig. 3 is a schematic diagram of a possible path of the present invention.
Fig. 4 is a schematic view of the cornering angle of the present invention.
Fig. 5 is a schematic diagram of the intersection congestion in front of the walking path according to the present invention.
FIG. 6 is a schematic diagram of a current cargo lift.
Reference numerals: 1. an obstacle region; 2. an obstacle region expansion map; 3. a starting point position; 4. an end position; 5. a feasible path; 6. turning angles; 7. a front intersection; 8. monitoring range of the front crossing; 9. a target AGV robot; 10. competing AGV robots; 11. carrying goods currently; 12. a lifting device in the target AGV robot; 13. a lifting surface; 14. the lifting surface is in contact with the target AGV robot.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an intelligent control system for an AGV robot walking path based on digital image processing, which comprises an AGV robot feasible path acquisition module, a feasible path information acquisition module, an AGV robot optimal path screening module, an AGV robot walking intersection congestion identification module, an AGV robot current carrying cargo analysis module, an AGV robot congestion intersection speed regulation module and a database.
The system comprises an AGV robot, an AGV robot optimal path screening module, an AGV robot traffic intersection congestion identification module, an AGV robot traffic intersection speed regulation module, an AGV robot current transport cargo analysis module, a database, an AGV robot feasible path acquisition module, a feasible path road information acquisition module and an AGV robot current transport cargo analysis module.
The AGV robot feasible path acquisition module is used for acquiring environmental information of the material warehouse, planning a traveling path of the target AGV robot according to the starting point position and the end point position of the target AGV robot, and obtaining all feasible paths of the target AGV robot.
Further, the specific analysis process of the AGV robot viable path acquisition module is as follows: referring to fig. 2, a panoramic image of a material warehouse is obtained through a high-definition camera, a three-dimensional model of the material warehouse is built, a top view of the material warehouse is obtained, and each obstacle area is marked in the top view of the material warehouse.
And acquiring size information of the target AGV robot, analyzing reference proportion magnification of the obstacle regions in the top view of the material warehouse, and carrying out proportion magnification on the obstacle regions in the top view of the material warehouse to obtain an expansion map of the obstacle regions in the top view of the material warehouse.
As a preferred scheme, the reference proportional magnification of the obstacle area in the top view of the material warehouse is analyzed, and the specific method is as follows: the method comprises the steps of obtaining size information of a target AGV robot, wherein the size information comprises length, width and height, extracting a relation function between the size information of the AGV robot stored in a database and the proportional magnification of an obstacle area in a top view of a material warehouse, screening and obtaining the proportional magnification of the obstacle area in the top view of the material warehouse corresponding to the size information of the target AGV robot, and recording the proportional magnification as a reference proportional magnification of the obstacle area in the top view of the material warehouse.
Referring to fig. 3, a starting point position and an end point position of the target AGV robot are marked in a top view of the material warehouse, and a travel path of the target AGV robot is planned according to an expansion map of each obstacle area in the top view of the material warehouse, so that each feasible path of the target AGV robot is obtained.
Preferably, the obstacle region in the plan view of the material warehouse comprises a material stacking region.
By acquiring the size information of the target AGV robot, the invention analyzes the expansion map of each obstacle area in the top view of the material warehouse, and plans each feasible path of the target AGV robot by combining the starting point position and the end point position of the target AGV robot, thereby being capable of reducing the number range of the feasible paths of the AGV robot and being beneficial to efficiently and accurately screening the optimal path of the AGV robot.
The feasible path road information acquisition module is used for acquiring road information of each feasible path of the target AGV robot, wherein the road information comprises a route length, a driving duration, a road condition hidden danger coefficient and a traffic technology difficulty coefficient.
Further, the specific analysis process of the feasible path road information acquisition module comprises the following steps: the route length of each feasible path of the target AGV robot is obtained and is recorded as，/>Indicate->Number of the feasible paths, +.>。
Acquiring the driving time length of each feasible path of the target AGV robot and recording the driving time length as。
As a preferable scheme, the driving time length of each feasible path of the target AGV robot is obtained, and the specific method comprises the following steps: each target AGV robot is feasible according to a preset principleDividing the paths to obtain each road section in each feasible path of the target AGV robot, obtaining the category of each road section in each feasible path of the target AGV robot, extracting the reference running speed corresponding to each road section stored in the database, screening to obtain the reference running speed of each road section in each feasible path of the target AGV robot, and marking the reference running speed as the reference running speed of each road section in each feasible path of the target AGV robot，/>Representing the +.>Number of each road section,/->The length of each road section in each feasible path of the target AGV robot is obtained and is recorded as +.>。
By analysis of formulasObtaining the driving time length of each feasible path of the target AGV robotWherein->And a correction amount indicating a preset feasible path travel time period.
In one particular embodiment, the categories of road segments in the target AGV robot's viable path include high traffic, moderate traffic, and low traffic segments.
Further, the specific analysis process of the feasible path road information acquisition module further comprises: obtaining the maximum gradient of each road section in each feasible path of the target AGV robot, analyzing the gradient coefficient of each feasible path of the target AGV robot, and marking the gradient coefficient as。
As a preferable scheme, gradient coefficients of all feasible paths of the target AGV robot are analyzed, and the specific method comprises the following steps: obtaining the maximum gradient of each road section in each feasible path of the target AGV robot, and marking the maximum gradient asBy analysis of the formulaObtaining gradient coefficient of each feasible path of target AGV robot>Wherein->Representing the number of segments in a feasible path, +.>Indicating a preset grade threshold.
Obtaining the average flatness of each road section in each feasible path of the target AGV robot, analyzing the flatness coefficient of each feasible path of the target AGV robot, and marking the flatness coefficient as。
As a preferable scheme, the flatness coefficients of all feasible paths of the target AGV robot are analyzed, and the specific method comprises the following steps: obtaining the average flatness of each road section in each feasible path of the target AGV robot, and marking the average flatness asBy analysis formula->Obtaining flatness coefficient of each feasible path of target AGV robot>Wherein->Representing a preset flatness threshold.
Acquiring the pavement water accumulation area of each road section in each feasible path of the target AGV robot, analyzing the pavement water accumulation coefficient of each feasible path of the target AGV robot, and marking the pavement water accumulation coefficient as。
As a preferable scheme, the pavement water accumulation coefficients of each feasible path of the target AGV robot are analyzed, and the specific method comprises the following steps: acquiring the accumulated water area of the road surface of each road section in each feasible path of the target AGV robot, and recording the accumulated water area asBy analysis formula->Obtaining pavement water accumulation coefficient of each feasible path of target AGV robot>Wherein->Representing natural constant->And representing the influence factor corresponding to the preset pavement unit ponding area.
Acquiring illumination brightness of each road section in each feasible path of the target AGV robot, analyzing illumination brightness coefficients of each feasible path of the target AGV robot, and marking the illumination brightness coefficients as。
As a preferable scheme, the illumination brightness coefficients of each feasible path of the target AGV robot are analyzed, and the specific method comprises the following steps: acquiring the illumination brightness of each road section in each feasible path of the target AGV robot and marking the illumination brightness asBy analysis of the formulaObtaining illumination brightness coefficients of all feasible paths of target AGV robotWherein->Indicating the target AGV robot +>The (th) of the feasible paths>Lighting intensity of road section->Representing a preset illumination brightness threshold of the road segment.
By analysis of formulasObtaining road condition hidden danger coefficients of each feasible path of target AGV robot>Wherein->Representing natural constant->、/>、/>、/>Respectively represent the preset gradient systemNumber, flatness coefficient, road surface water accumulation coefficient and weight factor of illumination brightness coefficient.
Further, the specific analysis process of the feasible path road information acquisition module further comprises: referring to FIG. 4, the angles of each turn in each feasible path of the target AGV robot are obtained and recorded as，/>Indicate->Number of secondary turns, ++>。
By analysis of formulasObtaining the passing technical difficulty coefficient of each feasible path of the target AGV robot>Wherein->And representing the influence factors corresponding to the preset unit turning angles.
As a preferable mode, the range of the turning angle is。
The AGV robot optimal path screening module is used for analyzing recommendation coefficients of all feasible paths of the target AGV robot according to road information of all feasible paths of the target AGV robot to obtain the optimal feasible paths of the target AGV robot.
Further, the specific analysis process of the AGV robot optimal path screening module is as follows: by analysis of formulasObtaining recommended coefficients of each feasible path of target AGV robot>Wherein->Represents the number of possible paths, +.>、/>、/>、/>Weights respectively representing preset route length, running duration, road condition hidden danger coefficient and traffic technology difficulty coefficient, ++>+/>+/>+/>=1。
And comparing the recommended coefficients of all the feasible paths of the target AGV robot, and taking the feasible path corresponding to the maximum recommended coefficient as the best feasible path of the target AGV robot.
The method and the system have the advantages that the recommended coefficients of all feasible paths of the target AGV robot are analyzed by obtaining the route length, the running duration, the road condition hidden danger coefficients and the traffic technology difficulty coefficients of all the feasible paths of the target AGV robot, so that the optimal feasible paths of the target AGV robot are obtained, the diversity of path evaluation indexes is enriched, a path evaluation system can be perfected, the optimal paths of the AGV robot can be conveniently screened, and the efficient operation of the AGV robot is guaranteed.
The AGV robot driving intersection congestion identification module is used for acquiring the position and driving speed of each AGV robot waiting to pass at the intersection in front of the target AGV robot driving path, judging whether the intersection in front of the target AGV robot driving path is congested, and if so, acquiring the time length required for each competing AGV robot at the intersection in front of the target AGV robot driving path to reach the intersection.
Further, the specific process of the AGV robot driving intersection congestion identification module is as follows:referring to FIG. 5, a monitoring range of the intersection in front of the traveling path of the target AGV robot is obtained by taking the intersection in front of the traveling path of the target AGV robot as a circle center and taking the set distance as a radius, and the monitoring range is recorded as a monitoring range of the intersection in front.
And acquiring the running directions of all AGV robots and all AGV robots in the monitoring range of the front intersection, analyzing to obtain all AGV robots waiting to pass at the front intersection, further acquiring the positions and the running speeds of all AGV robots waiting to pass at the front intersection, and analyzing to obtain the time of all AGV robots waiting to pass at the front intersection to reach the intersection.
As a preferable mode, when the target AGV robot travels within the front intersection monitoring range, monitoring analysis is started for each AGV robot within the front intersection monitoring range.
As a preferable scheme, each AGV robot waiting to pass at the front intersection is obtained, and the specific method is as follows: and acquiring each AGV robot and the running direction of each AGV robot in the monitoring range of the front intersection, if the running direction of a certain AGV robot in the monitoring range of the front intersection passes through the front intersection, marking the AGV robot as the AGV robot waiting for passing at the front intersection, and counting to obtain each AGV robot waiting for passing at the front intersection.
And acquiring the time of the target AGV robot reaching the intersection.
Comparing the time of each AGV robot waiting to pass at the front intersection with the time of each target AGV robot waiting to pass at the front intersection, obtaining the interval duration between the time of each AGV robot waiting to pass at the front intersection and the time of each target AGV robot waiting to pass at the front intersection, and marking the interval duration as the vehicle meeting interval duration of each AGV robot waiting to pass at the front intersection.
Comparing the interval duration of the meeting of each AGV robot waiting to pass at the front intersection with a preset interval duration threshold, if the interval duration of the meeting of each AGV robot waiting to pass at the front intersection is greater than or equal to the preset interval duration threshold, the intersection in front of the traveling path of the target AGV robot is not congested, otherwise, the intersection in front of the traveling path of the target AGV robot is congested, and executing>。
: and marking the AGV robots with the meeting interval time length smaller than the preset meeting interval time length threshold as competing AGV robots of the intersection in front of the traveling path of the target AGV robot, counting to obtain each competing AGV robot of the intersection in front of the traveling path of the target AGV robot, and further obtaining the time length required by each competing AGV robot of the intersection in front of the traveling path of the target AGV robot to reach the intersection.
As a preferable scheme, the time for each AGV robot waiting to pass at the front intersection to reach the intersection is analyzed, and the specific method is as follows: according to the position of each AGV robot waiting to pass at the front intersection, the required running distance of each AGV robot waiting to pass at the front intersection is obtained, the required running distance of each AGV robot waiting to pass at the front intersection is divided by the corresponding running speed, the required time of each AGV robot waiting to pass at the front intersection is obtained, the required time of each AGV robot waiting to pass at the front intersection is added with the current time, and the time of each AGV robot waiting to pass at the front intersection is obtained.
As a preferable scheme, the method for obtaining the time of the target AGV robot to reach the intersection is the same as the method for analyzing the time of each AGV robot waiting to pass at the front intersection, and the principle is the same.
As a preferable scheme, the method for acquiring the time length required by each competing AGV robot at the intersection in front of the traveling path of the target AGV robot comprises the following steps: and screening and obtaining the time required by the arrival intersection of each competing AGV robot of the intersection in front of the traveling path of the target AGV robot according to the time required by the arrival intersection of each AGV robot waiting to pass at the intersection in front.
The AGV robot current carrying cargo analysis module is used for acquiring basic information of the target AGV robot current carrying cargo, wherein the basic information comprises a type, a volume, a weight, a height and an effective lifting area proportionality coefficient, and the transport safety coefficient of the target AGV robot current carrying cargo is obtained through analysis.
Further, the specific analysis process of the current carrying cargo analysis module of the AGV robot is as follows: the method comprises the steps of obtaining the type of the goods currently carried by the target AGV robot, extracting the fragile coefficients corresponding to the goods of various types stored in a database, screening to obtain the fragile coefficients of the goods currently carried by the target AGV robot, and marking the fragile coefficients as。
The volume, weight and height of the current carried goods of the target AGV robot are obtained and respectively recorded as、/>、/>。
Referring to FIG. 6, the total area of the lifting surface of the target AGV robot currently carrying the load is obtained and is recorded asAnd the area of the current carrying cargo lifting surface of the target AGV robot, which is in contact with the target AGV robot, is obtained and is marked as +.>。
By analysis of formulasObtaining the effective lifting area proportion coefficient of the target AGV robot for carrying the goods at present>。
By analysis of formulasObtaining the transport safety factor of the target AGV robot currently carrying goods +.>Wherein->、/>、/>Representing the preset cargo volume, weight and height thresholds, respectively.
In one embodiment, the lifting surface of the target AGV robot that is currently carrying the load is the bottom surface of the current carrying load.
The speed regulation and control module of the AGV robot at the congested intersection is used for obtaining the speed regulation and control direction and the speed regulation and control quantity of the target AGV robot at the congested intersection according to the transportation safety coefficient of the target AGV robot for carrying cargoes at present.
Further, the analysis process of the AGV robot congestion intersection speed regulation module is as follows:: comparing the transportation safety coefficient of the current transported goods of the target AGV robot with a preset transportation safety coefficient threshold value, if the transportation safety coefficient of the current transported goods of the target AGV robot is greater than or equal to the preset transportation safety coefficient threshold value, accelerating the speed regulation and control direction of the target AGV robot at a congestion crossing, and executing ++>Otherwise, the speed regulation direction of the target AGV robot at the congested intersection is deceleration, and +.>。
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->。
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the minimum time length required by the competing AGV robot to reach the intersection, and marking the minimum time length as。
By analysis of formulasObtaining
Speed adjustment quantity of target AGV robot at congested crossingWherein->Compensation amount representing speed adjustment amount of preset target AGV robot during acceleration, +.>。
As a preferred embodiment of the present invention,。
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->。
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the maximum value of the required time length of each competing AGV robot at the intersection, and marking the maximum value as。
By analysis of formulasObtaining speed adjustment quantity +.>Wherein->Representing pre-emphasisThe compensation quantity of speed regulation quantity when the target AGV robot is decelerating is set up, and the compensation quantity is +.>。
As a preferred embodiment of the present invention,。
as a preferred scheme, the front intersection of the traveling path of the target AGV robot is congested, which indicates that the target AGV robot and each competing AGV robot pass through the front intersection in a short period of time, when the target AGV robot selects to accelerate to pass through the front intersection, the target AGV robot passes through the front intersection earlier than the competing AGV robot that passes through the front intersection earliest, and when the target AGV robot selects to decelerate to pass through the front intersection, the target AGV robot passes through the front intersection later than the competing AGV robot that passes through the front intersection latest.
The method and the device can be used for further obtaining the speed regulation direction and the speed regulation quantity of the target AGV robot at the congested crossing by acquiring the basic information of the target AGV robot for carrying the goods at present and analyzing the transportation safety coefficient of the target AGV robot for carrying the goods at present, so that the running speed of the AGV robot at the congested crossing is regulated and controlled, and the goods safety and the running safety of the AGV robot are ensured.
The database is used for storing a relation function between size information of the AGV robot and proportional amplification factors of obstacle areas in a top view of the material warehouse, and storing reference running speeds corresponding to road sections of various types and damage coefficients corresponding to goods of various types.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (9)
1. AGV robot walking route intelligent control system based on digital image processing, its characterized in that includes:
the AGV robot feasible path acquisition module is used for acquiring environmental information of a material warehouse, planning a traveling path of the target AGV robot according to a starting point position and an end point position of the target AGV robot, and obtaining all feasible paths of the target AGV robot;
the feasible path road information acquisition module is used for acquiring the road information of each feasible path of the target AGV robot, wherein the road information comprises a route length, a driving duration, a road condition hidden danger coefficient and a traffic technology difficulty coefficient;
AGV robot best route screening module: the method comprises the steps of analyzing recommendation coefficients of all feasible paths of a target AGV robot according to road information of all the feasible paths of the target AGV robot to obtain an optimal feasible path of the target AGV robot;
the AGV robot traveling road junction congestion identification module is used for acquiring the position and traveling speed of each AGV robot waiting to pass at the road junction in front of the traveling path of the target AGV robot, judging whether the road junction in front of the traveling path of the target AGV robot is congested, and if so, acquiring the time required by each competing AGV robot at the road junction in front of the traveling path of the target AGV robot to reach the road junction;
the AGV robot current carrying cargo analysis module is used for acquiring basic information of the target AGV robot current carrying cargo, wherein the basic information comprises type, volume, weight, height and effective lifting area proportionality coefficient, and analyzing to obtain a transport safety coefficient of the target AGV robot current carrying cargo;
AGV robot blocks up crossing speed regulation and control module: the speed regulation and control direction and speed regulation quantity of the target AGV robot at the congestion intersection are obtained according to the transportation safety coefficient of the target AGV robot for carrying cargoes at present;
and the database is used for storing a relation function between the size information of the AGV robot and the proportional magnification of the obstacle area in the plan view of the material warehouse, and storing the reference running speeds corresponding to the road sections of all types and the fragile coefficients corresponding to the cargoes of all types.
2. The digital image processing-based intelligent control system for the travel path of an AGV robot according to claim 1, wherein: the specific analysis process of the AGV robot feasible path acquisition module is as follows:
obtaining a panoramic image of the material warehouse through a high-definition camera, constructing a three-dimensional model of the material warehouse, obtaining a top view of the material warehouse, and marking each obstacle area in the top view of the material warehouse;
acquiring size information of a target AGV robot, analyzing reference proportion magnification of obstacle regions in a top view of a material warehouse, and carrying out proportion magnification on each obstacle region in the top view of the material warehouse to obtain an expansion map of each obstacle region in the top view of the material warehouse;
and marking the starting point position and the end point position of the target AGV robot in the plan view of the material warehouse, and planning the traveling path of the target AGV robot according to the expansion diagram of each obstacle area in the plan view of the material warehouse to obtain each feasible path of the target AGV robot.
3. The digital image processing-based intelligent control system for the travel path of an AGV robot according to claim 1, wherein: the specific analysis process of the feasible path road information acquisition module comprises the following steps:
the route length of each feasible path of the target AGV robot is obtained and is recorded as，/>Indicate->Number of the feasible paths, +.>；
The running time of each feasible path of the target AGV robot is obtained,it is noted as。
4. The digital image processing based intelligent control system for the travel path of an AGV robot according to claim 3, wherein: the specific analysis process of the feasible path road information acquisition module further comprises the following steps:
obtaining the maximum gradient of each road section in each feasible path of the target AGV robot, analyzing the gradient coefficient of each feasible path of the target AGV robot, and marking the gradient coefficient as；
Obtaining the average flatness of each road section in each feasible path of the target AGV robot, analyzing the flatness coefficient of each feasible path of the target AGV robot, and marking the flatness coefficient as；
Acquiring the pavement water accumulation area of each road section in each feasible path of the target AGV robot, analyzing the pavement water accumulation coefficient of each feasible path of the target AGV robot, and marking the pavement water accumulation coefficient as；
Acquiring illumination brightness of each road section in each feasible path of the target AGV robot, analyzing illumination brightness coefficients of each feasible path of the target AGV robot, and marking the illumination brightness coefficients as；
By analysis of formulasObtaining road condition hidden danger coefficients of each feasible path of target AGV robot>Wherein->Representing natural constant->、/>、/>、/>Respectively representing the weight factors of a preset gradient coefficient, a preset flatness coefficient, a preset road surface water accumulation coefficient and a preset illumination brightness coefficient.
5. The digital image processing based intelligent control system for the travel path of an AGV robot according to claim 4, wherein: the specific analysis process of the feasible path road information acquisition module further comprises the following steps:
acquiring the angles of each turning in each feasible path of the target AGV robot and recording the angles as，/>Indicate->Number of secondary turns, ++>；
By analysis of formulasObtaining the passing of each feasible path of the target AGV robotTechnical difficulty coefficient->Wherein->And representing the influence factors corresponding to the preset unit turning angles.
6. The digital image processing based intelligent control system for the travel path of an AGV robot of claim 5, wherein: the specific analysis process of the AGV robot optimal path screening module is as follows:
by analysis of formulasObtaining recommended coefficients of each feasible path of target AGV robot>Wherein->Represents the number of possible paths, +.>、/>、/>、/>Weights respectively representing preset route length, running duration, road condition hidden danger coefficient and traffic technology difficulty coefficient, ++>+/>+/>+/>=1；
And comparing the recommended coefficients of all the feasible paths of the target AGV robot, and taking the feasible path corresponding to the maximum recommended coefficient as the best feasible path of the target AGV robot.
7. The digital image processing-based intelligent control system for the travel path of an AGV robot according to claim 1, wherein: the specific process of the AGV robot driving intersection congestion identification module is as follows:
taking the intersection in front of the traveling path of the target AGV robot as a circle center, taking the set distance as a radius as a circle, obtaining the monitoring range of the intersection in front of the traveling path of the target AGV robot, and recording the monitoring range as the monitoring range of the intersection in front;
acquiring each AGV robot and the running direction of each AGV robot in a monitoring range of a front intersection, analyzing to obtain each AGV robot waiting to pass at the front intersection, further acquiring the position and the running speed of each AGV robot waiting to pass at the front intersection, and analyzing to obtain the time of each AGV robot waiting to pass at the front intersection to reach the intersection;
acquiring the time of the target AGV robot reaching the intersection;
comparing the time of each AGV robot waiting to pass at the front intersection with the time of each target AGV robot waiting to pass at the front intersection to obtain the interval duration between the time of each AGV robot waiting to pass at the front intersection and the time of each target AGV robot waiting to pass at the front intersection, and marking the interval duration as the vehicle meeting interval duration of each AGV robot waiting to pass at the front intersection;
comparing the interval duration of the meeting of each AGV robot waiting to pass at the front intersection with a preset interval duration threshold, if the interval duration of the meeting of each AGV robot waiting to pass at the front intersection is greater than or equal to the preset interval duration threshold, the intersection in front of the traveling path of the target AGV robot is not congested, otherwise, the intersection in front of the traveling path of the target AGV robot is congested, and executing>；
: and marking the AGV robots with the meeting interval time length smaller than the preset meeting interval time length threshold as competing AGV robots of the intersection in front of the traveling path of the target AGV robot, counting to obtain each competing AGV robot of the intersection in front of the traveling path of the target AGV robot, and further obtaining the time length required by each competing AGV robot of the intersection in front of the traveling path of the target AGV robot to reach the intersection.
8. The digital image processing-based intelligent control system for the travel path of an AGV robot according to claim 1, wherein: the specific analysis process of the current transport cargo analysis module of the AGV robot is as follows:
the method comprises the steps of obtaining the type of the goods currently carried by the target AGV robot, extracting the fragile coefficients corresponding to the goods of various types stored in a database, screening to obtain the fragile coefficients of the goods currently carried by the target AGV robot, and marking the fragile coefficients as；
The volume, weight and height of the current carried goods of the target AGV robot are obtained and respectively recorded as、/>、/>；
The total area of the lifting surface of the target AGV robot for carrying the goods at present is obtained and is recorded asAnd the area of the current carrying cargo lifting surface of the target AGV robot, which is in contact with the target AGV robot, is obtained and is marked as +.>；
By analysis of formulasObtaining the effective lifting area proportion coefficient of the target AGV robot for carrying the goods at present>；
By analysis of formulasObtaining the transport safety factor of the target AGV robot currently carrying goods +.>Wherein->、/>、/>Representing the preset cargo volume, weight and height thresholds, respectively.
9. The digital image processing based intelligent control system for the travel path of an AGV robot of claim 7, wherein: the analysis process of the AGV robot congestion intersection speed regulation module is as follows:
: comparing the transportation safety coefficient of the current transported goods of the target AGV robot with a preset transportation safety coefficient threshold value, if the transportation safety coefficient of the current transported goods of the target AGV robot is greater than or equal to the preset transportation safety coefficient threshold value, accelerating the speed regulation and control direction of the target AGV robot at a congestion crossing, and executing ++>Otherwise, the speed regulation direction of the target AGV robot at the congested intersection is deceleration, and +.>；
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->；
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the minimum time length required by the competing AGV robot to reach the intersection, and marking the minimum time length as；
By analysis of formulasObtaining
Speed adjustment quantity of target AGV robot at congested crossingWherein->Compensation amount representing speed adjustment amount of preset target AGV robot during acceleration, +.>；
: acquiring the time length, the current speed and the distance between the target AGV robot and the front intersection, and respectively marking the time length, the current speed and the distance as +.>、/>And->；
Comparing the required time length of each competing AGV robot at the intersection in front of the traveling path of the target AGV robot to obtain the maximum value of the required time length of each competing AGV robot at the intersection, and marking the maximum value as；
By analysis of formulasObtaining speed adjustment quantity +.>Wherein->Compensation amount representing speed adjustment amount at deceleration of preset target AGV robot, +.>。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310953743.XA CN116661467B (en) | 2023-08-01 | 2023-08-01 | AGV robot walking path intelligent control system based on digital image processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310953743.XA CN116661467B (en) | 2023-08-01 | 2023-08-01 | AGV robot walking path intelligent control system based on digital image processing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116661467A true CN116661467A (en) | 2023-08-29 |
| CN116661467B CN116661467B (en) | 2023-10-13 |
Family
ID=87710183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310953743.XA Active CN116661467B (en) | 2023-08-01 | 2023-08-01 | AGV robot walking path intelligent control system based on digital image processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116661467B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190240834A1 (en) * | 2016-10-13 | 2019-08-08 | Beijing Jingdong Shangke Information Technology Co, Ltd. | Dispatching method and device, and non-transitory readable storage medium |
| CN112859847A (en) * | 2021-01-06 | 2021-05-28 | 大连理工大学 | Multi-robot collaborative path planning method under traffic direction limitation |
| CN113554885A (en) * | 2021-06-23 | 2021-10-26 | 北京汽车股份有限公司 | Method and system for guiding vehicle steering at congested intersection |
| CN115273474A (en) * | 2022-08-02 | 2022-11-01 | 浙江安易信科技有限公司 | RPA patrols and examines robot and patrols and examines management system based on artificial intelligence |
| CN115375130A (en) * | 2022-08-17 | 2022-11-22 | 南京元屏风科技有限公司 | AGV robot intelligence transport control analytic system based on wisdom logistics storage management |
| CN116030654A (en) * | 2023-02-13 | 2023-04-28 | 中电信数字城市科技有限公司 | Traffic jam adjusting method and device, electronic equipment and medium |
| CN116184944A (en) * | 2022-12-05 | 2023-05-30 | 武汉易特兰瑞科技有限公司 | Intelligent factory intelligent robot control management method and system |
-
2023
- 2023-08-01 CN CN202310953743.XA patent/CN116661467B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190240834A1 (en) * | 2016-10-13 | 2019-08-08 | Beijing Jingdong Shangke Information Technology Co, Ltd. | Dispatching method and device, and non-transitory readable storage medium |
| CN112859847A (en) * | 2021-01-06 | 2021-05-28 | 大连理工大学 | Multi-robot collaborative path planning method under traffic direction limitation |
| CN113554885A (en) * | 2021-06-23 | 2021-10-26 | 北京汽车股份有限公司 | Method and system for guiding vehicle steering at congested intersection |
| CN115273474A (en) * | 2022-08-02 | 2022-11-01 | 浙江安易信科技有限公司 | RPA patrols and examines robot and patrols and examines management system based on artificial intelligence |
| CN115375130A (en) * | 2022-08-17 | 2022-11-22 | 南京元屏风科技有限公司 | AGV robot intelligence transport control analytic system based on wisdom logistics storage management |
| CN116184944A (en) * | 2022-12-05 | 2023-05-30 | 武汉易特兰瑞科技有限公司 | Intelligent factory intelligent robot control management method and system |
| CN116030654A (en) * | 2023-02-13 | 2023-04-28 | 中电信数字城市科技有限公司 | Traffic jam adjusting method and device, electronic equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116661467B (en) | 2023-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11120688B2 (en) | Orientation-adjust actions for autonomous vehicle operational management | |
| US10796574B2 (en) | Driving assistance method and device | |
| CN110614992B (en) | Method and system for avoiding obstacle during automatic driving of vehicle and vehicle | |
| CN107683234A (en) | Surrounding enviroment identification device and computer program product | |
| DE102020006337A1 (en) | Autonomous industrial truck | |
| CN109477725A (en) | For generating the method and system of the cartographic information in emergency region | |
| EP3822945B1 (en) | Driving environment information generation method, driving control method, driving environment information generation device | |
| EP3407328A1 (en) | Driving assistance method and device | |
| CN111583678A (en) | Unmanned truck port horizontal transportation system and method | |
| CN113538937B (en) | Port mixed traffic control system | |
| CN108898866A (en) | A kind of effective intelligent vehicle control system | |
| CN111627228A (en) | Expressway confluence point lane lamp control system and method | |
| CN116013101B (en) | System and method for suggesting speed of signal-free intersection based on network environment | |
| Duinkerken et al. | Comparison of routing strategies for AGV systems using simulation | |
| CN116661467B (en) | AGV robot walking path intelligent control system based on digital image processing | |
| CN113781839A (en) | Crossroad efficient passing method and traffic system based on vehicle-road cooperation | |
| CN114239897A (en) | Automatic vehicle distribution system and automatic vehicle distribution method | |
| EP4246486A1 (en) | Non-selfish traffic lights passing advisory systems | |
| JP6759141B2 (en) | Container terminal and its operation method | |
| CN115547054A (en) | Traffic guidance system based on big data | |
| CN109164798B (en) | Intelligent traffic control regulation and control system in AGV dolly transportation process | |
| CN116529141A (en) | Motion planning in autonomous vehicle curve coordinate system | |
| CN114237209A (en) | Travel time prediction device and travel time prediction method | |
| CN108829108B (en) | AGV trolley running traffic control and control method based on information normalization | |
| JP6863546B2 (en) | Container terminal and its operation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |