Control method and device of unmanned transport vehicle, storage medium and electronic equipment

Abstract

The invention relates to an unmanned transport vehicle control method and device, a storage medium and electronic equipment, and relates to the technical field of automatic driving, and the method comprises the steps: receiving a current operation region and a current loading position of mineral mining equipment sent by a vehicle scheduling system, and carrying out the grid processing of the current operation region, obtaining a plurality of regional grids; determining region nodes to be searched based on the region grids, and determining an initial search node corresponding to the drive-in waiting point and a termination search node corresponding to the current loading position from the region nodes to be searched; determining a current global path of a candidate transport vehicle based on the initial search node, the termination search node and a historical global path of the candidate transport vehicle; and controlling the candidate transport vehicle to run from a drive-in waiting point where the current operation area is located to a current loading position based on the current global path. According to the invention, the planning efficiency of the driving path is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of autonomous driving technology, and more specifically, to a control method for an unmanned transport vehicle, a control device for an unmanned transport vehicle, a computer-readable storage medium, and an electronic device. Background Art

[0002] In open-pit mining operations, the paths of unmanned transport systems are usually collected and produced manually; therefore, if the transport vehicle is to stop at the loading area, it is necessary to follow the pre-collected stop trajectory to complete the stop process.

[0003] However, the above scheme has the following problems: since the parking trajectory is collected and produced by manual participation, when there is a large deviation between the produced reference path and the actual operation route of the mining equipment, the reference path needs to be re-planned manually, which makes the path planning efficiency low.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a control method for an unmanned transport vehicle, a control device for an unmanned transport vehicle, a computer-readable storage medium, and an electronic device, thereby at least to a certain extent overcoming the problem of low path planning efficiency caused by the limitations and defects of related technologies.

[0006] According to one aspect of the present disclosure, a control method for an unmanned transport vehicle is provided, comprising:

[0007] Receiving the current operation area and the current loading position of the mining equipment sent by the vehicle dispatching system, and performing grid processing on the current operation area to obtain a plurality of area grids;

[0008] Determine the regional nodes to be searched based on each of the regional grids, and determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched;

[0009] Determine a current global path of the candidate transport vehicle based on the starting search node, the ending search node, and the historical global path of the candidate transport vehicle;

[0010] The candidate transport vehicle is controlled to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0011] In an exemplary embodiment of the present disclosure, determining the current global path of the candidate transport vehicle based on the starting search node, the ending search node, and the historical global path of the candidate transport vehicle includes:

[0012] Subnode expansion step: putting the start node as the first parent node into an open list, and expanding multiple subnodes corresponding to the first parent node in the open list;

[0013] The current child node determination step: putting the first parent node into a closed list, and determining the first current child node corresponding to the first parent node in each of the child nodes based on the principle of minimum total cost value; wherein the total cost value includes a cumulative cost value and an estimated cost value;

[0014] Current child node storage step: when it is determined that the accumulated cost value of the first current child node is less than a preset cost value, storing the first current child node in a target child node list, and replacing the preset cost value with the accumulated cost value of the first current child node;

[0015] Looping step: looping the sub-node expansion step, the current sub-node determination step and the current sub-node storage step in sequence until the current sub-node is the terminating search node;

[0016] Trajectory planning step: determining the current global path of the candidate transport vehicle according to each of the current sub-nodes in the target sub-node list and the historical global path of the candidate transport vehicle.

[0017] In an exemplary embodiment of the present disclosure, determining the first current child node corresponding to the first parent node based on the principle of minimizing the total cost value among the child nodes includes:

[0018] Calculate the cumulative cost value from the first parent node to each of the child nodes, and calculate the estimated cost value from each of the child nodes to the terminating search node;

[0019] According to each of the accumulated cost values ​​and each of the estimated cost values, a total cost value of each of the child nodes is obtained, and the child node with the smallest total cost value is used as the first current child node.

[0020] In an exemplary embodiment of the present disclosure, calculating the estimated cost value from each of the child nodes to the terminating search node includes:

[0021] Configuring weight parameters of each of the subnodes and the terminating search node on the time T axis;

[0022] The estimated cost value is obtained according to the weight parameter, the Manhattan distance between each of the child nodes and the terminating search node.

[0023] In an exemplary embodiment of the present disclosure, controlling the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path includes:

[0024] receiving first instruction information sent by the vehicle dispatching system, and when determining that the first instruction information is a driving instruction, controlling the candidate transport vehicle to drive from the entry waiting point where the current operation area is located to the current loading position based on the current global path;

[0025] When it is determined that the first instruction information is a waiting instruction, controlling the candidate transport vehicle to wait at the entry waiting point, and receiving the second instruction information sent by the vehicle dispatching system;

[0026] When it is determined that the second instruction information is a driving instruction, the candidate transport vehicle is controlled to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0027] In an exemplary embodiment of the present disclosure, the driving instruction and the waiting instruction are generated by the vehicle dispatching system in the following manner:

[0028] Based on the real-time vehicle position information reported by the candidate transport vehicles, determining whether there is a target transport vehicle to be loaded and / or being loaded in the current operation area;

[0029] If the target transport vehicle does not exist, the driving instruction is generated; if the target transport vehicle exists, the waiting instruction is generated.

[0030] In an exemplary embodiment of the present disclosure, the second instruction information is generated by the vehicle dispatching system in the following manner:

[0031] If there is a target transport vehicle to be loaded in the current operation area and the target transport vehicle is in the process of reversing from the current pre-parking position to the current loading position, then when it is detected that the target transport vehicle to be loaded is already in the current loading position, the second instruction information is generated; wherein the second instruction information is used to instruct the candidate transport vehicle to travel from the entry waiting point to the current pre-parking position;

[0032] If there is a target transport vehicle to be loaded and the target transport vehicle is in the process of being loaded, the second instruction information is generated after detecting that the loading of the target transport vehicle is completed and the target transport vehicle passes the vehicle projection point on the exit path; wherein the second instruction information is used to instruct the candidate transport vehicle to travel from the current pre-parking position to the current loading position.

[0033] According to one aspect of the present disclosure, a control device for an unmanned transport vehicle is provided, comprising:

[0034] An operation area gridding module is used to receive the current operation area and current loading position of the mining equipment sent by the vehicle dispatching system, and perform gridding processing on the current operation area to obtain multiple area grids;

[0035] A search node determination module, used to determine the regional nodes to be searched based on each of the regional grids, and determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched;

[0036] A global path determination module, configured to determine a current global path of the candidate transport vehicle based on the start search node, the end search node, and the historical global path of the candidate transport vehicle;

[0037] The vehicle control module is used to control the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0038] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the control method of the unmanned transport vehicle described in any one of the above is implemented.

[0039] According to one aspect of the present disclosure, there is provided an electronic device, including:

[0040] Processor; and

[0041] A memory, configured to store executable instructions of the processor;

[0042] Wherein, the processor is configured to execute any one of the above-mentioned control methods for the unmanned transport vehicle by executing the executable instructions.

[0043] A control method for an unmanned transport vehicle provided by an embodiment of the present disclosure comprises the following steps: on the one hand, receiving the current operating area and the current loading position of the mineral mining equipment sent by the vehicle dispatching system, and gridding the current operating area to obtain a plurality of regional grids; then determining the regional nodes to be searched based on each regional grid, and determining the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched; and then determining the current global path of the candidate transport vehicle based on the starting search node, the ending search node and the historical global path of the candidate transport vehicle; and finally controlling the candidate transport vehicle to travel from the entry waiting point where the current operating area is located to the current loading position based on the current global path, thereby realizing automatic planning of the vehicle's travel path, solving the problem in the prior art that the reference path needs to be manually replanned, resulting in low planning efficiency of the path, and improving the planning efficiency of the current global path; on the other hand, solving the problem in the prior art that the reference path needs to be manually replanned, resulting in low accuracy of the travel path, and improving the accuracy of the current global path obtained.

[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0046] Figure 1 A diagram schematically shows an example scenario of vehicle transportation in a related technical solution.

[0047] Figure 2 The following is a flowchart schematically showing a method for controlling an unmanned transport vehicle according to an exemplary embodiment of the present disclosure.

[0048] Figure 3 A structural example diagram of a control system of an unmanned transport vehicle according to an example embodiment of the present disclosure is schematically shown.

[0049] Figure 4 A diagram schematically illustrates an example scenario of a reporting process of updated current device location information according to an example embodiment of the present disclosure.

[0050] Figure 5 A scene example diagram of an obtained area grid according to an example embodiment of the present disclosure is schematically shown.

[0051] Figure 6 A diagram schematically shows an example scenario of dynamically planning a current global path of a transport vehicle according to an example embodiment of the present disclosure.

[0052] Figure 7 A block diagram schematically illustrates a control device for an unmanned transport vehicle according to an exemplary embodiment of the present disclosure.

[0053] Figure 8 An electronic device for implementing a control method for an unmanned transport vehicle according to an exemplary embodiment of the present disclosure is schematically illustrated. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, the known technical solutions are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0055] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0056] In open-pit mining operations, the paths of unmanned transport systems are usually collected and produced manually. Therefore, the docking plan of the transport vehicle in the loading area is to follow the pre-collected docking trajectory to complete the docking process of the transport vehicle; among them, reference Figure 1 As shown, the reference operation area involved in the existing scheme can be as follows Figure 1 As shown in 101, the loading area boundary can refer to Figure 1 As shown in 102; at the same time, Figure 1In the operation scene shown, a reference loading area entry path 103 and a reverse reference path 104 are also set. However, the scheme has the following problems: on the one hand, the frequency of manual participation is high, and the path collection and production time is long, which affects the operation efficiency; that is, since the parking trajectory is collected and produced by manual participation, when the produced path deviates greatly from the actual operation route of the excavator, manual participation is required to re-produce it, so the path planning efficiency is low; on the other hand, the coordinated operation of the unmanned transport vehicle and the excavator cannot be realized, and the accurate posture of the stop point cannot be provided; therefore, when the actual operation route of the excavator deviates greatly from the expected operation route, the stop posture will be inaccurate, and thus normal loading cannot be performed; on the other hand, the parking process of the transport vehicle cannot be dynamically planned; that is, on the basis that the excavator can provide the stop point in real time, it is impossible to further autonomously plan the parking trajectory of the transport vehicle and control the unmanned transport vehicle to complete accurate parking based on the parking trajectory.

[0057] Based on this, this exemplary embodiment first provides a control method for an unmanned transport vehicle, which can be run on a vehicle-mounted device; of course, those skilled in the art can also run the method disclosed in this disclosure on other platforms as required, and this exemplary embodiment does not specifically limit this. Figure 2 As shown, the control method of the unmanned transport vehicle may include the following steps:

[0058] Step S210. Receive the current operation area and current loading position of the mining equipment sent by the vehicle dispatching system, and perform grid processing on the current operation area to obtain multiple area grids;

[0059] Step S220. Determine the regional nodes to be searched based on each of the regional grids, and determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched;

[0060] Step S230. Determine the current global path of the candidate transport vehicle based on the starting search node, the ending search node and the historical global path of the candidate transport vehicle;

[0061] Step S240: Control the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0062] In the control method of the unmanned transport vehicle recorded above, on the one hand, by receiving the current operating area and the current loading position of the mineral mining equipment sent by the vehicle dispatching system, and gridding the current operating area, a plurality of regional grids are obtained; then, based on each regional grid, the nodes of the area to be searched are determined, and the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position are determined from the nodes of the area to be searched; then, based on the starting search node, the ending search node and the historical global path of the candidate transport vehicle, the current global path of the candidate transport vehicle is determined; finally, the candidate transport vehicle is controlled to travel from the entry waiting point where the current operating area is located to the current loading position based on the current global path, thereby realizing automatic planning of the vehicle's driving path, solving the problem in the prior art that the reference path needs to be manually replanned, resulting in low planning efficiency of the path, and improving the planning efficiency of the current global path; on the other hand, it also solves the problem in the prior art that the reference path needs to be manually replanned, resulting in low accuracy of the driving path, and improving the accuracy of the current global path obtained.

[0063] Hereinafter, the control method of the unmanned transport vehicle described in the exemplary embodiment of the present disclosure will be explained and illustrated in detail with reference to the accompanying drawings.

[0064] First, the technical implementation principle of the exemplary embodiment of the present disclosure is explained and illustrated. Specifically, the control method of the unmanned transport vehicle recorded in the exemplary embodiment of the present disclosure can be applied to the unmanned transport system in the open-pit mining area; specifically, in the process of actual application, based on the control method of the unmanned transport vehicle recorded in the exemplary embodiment of the present disclosure, when the position of the excavator in the loading area changes or shifts, it can autonomously plan and update the entry path and exit path for the unmanned transport vehicle, realize dynamic adjustment and optimization, and ensure the efficiency and accuracy of the transportation operation. Therefore, the technical problem that the exemplary embodiment of the present disclosure actually needs to solve is: on the basis of the current manual point collection and mapping to realize the operation route of the designated loading area, provide a dynamic path planning function, so as to achieve the purpose of improving the operation efficiency and accuracy by adjusting the trajectory in a small range, dynamically adapting to the dynamic change of the position of the excavator and optimizing the three-dimensional collaborative operation path of the transport vehicle and the excavator of the unmanned transport system; at the same time, it can also solve the problems of long path acquisition and production time, insufficient coordination between the unmanned transport vehicle and the excavator, low parking accuracy and lack of dynamic parking planning in the prior art.

[0065] The following will explain and illustrate the control system of the unmanned transport vehicle involved in the exemplary embodiment of the present disclosure. Figure 3As shown, the control system of the unmanned transport vehicle includes an on-board device terminal 310 in the transport vehicle, a vehicle dispatching system 320, an HMI (Human Machine Interface) module 330 in the mineral excavation equipment, and a background server 340 corresponding to the HMI module. Among them, the on-board terminal device can be connected to the vehicle dispatching system through a wired network or a wireless network, and the HMI module can also be connected to the background server through a wired network or a wireless network; the background server is connected to the vehicle dispatching system; in the actual application process, the HMI module can report the current operating area and the current loading position to the background server; then, the background server will send the current operating area and the current loading position to the vehicle dispatching system; when the vehicle dispatching system receives the current operating area and the current loading position, it can send the current operating area and the current loading position to the candidate transport vehicle; then, the candidate transport vehicle can dynamically plan the current global path according to the received current operating area and the current loading position. That is, in the actual application process, the real-time position and heading information of the vehicle can be obtained through the GPS (Global Positioning System) and inertial navigation module of the transport vehicle; the dynamic planning algorithm module in the on-board equipment terminal generates an optimized parking path based on the current position of the vehicle, the stop point position provided by the excavator in real time, and the boundary constraints in the work area; the communication and interaction module in the on-board equipment terminal is used to exchange data between the vehicle and the excavator and the scheduling system, including the stop point posture, dynamic planning path and other information; the HMI module in the mineral excavation equipment (i.e., the excavator) is used to provide the excavator driver (i.e., the user) with an interface for real-time adjustment of the stop point posture, supporting fine-tuning of the position and heading.

[0066] The following will be combined Figure 3 right Figure 2 The control method of the unmanned transport vehicle shown in the figure is further explained and illustrated. Specifically:

[0067] In step S210, the current operating area and the current loading position of the mining equipment sent by the vehicle dispatching system are received, and the current operating area is gridded to obtain a plurality of area grids.

[0068] In this example embodiment, first, the current operating area and current loading position of the mining equipment sent by the vehicle dispatching system are received; specifically, the current equipment position information recorded here is triggered and reported by the user (excavator driver) based on the human-computer interaction interface of the mining equipment (i.e., the excavator) after the position of the mining equipment changes; that is, in the actual application process, if the operating position of the excavator changes, the user will click on the interactive control in the human-computer interaction interface (such as one-click reporting of the current position, and the specific reporting scenario example diagram can be referred to Figure 4 As shown), when the human-computer interaction interface receives a click event acting on the interactive control, it can obtain the current position information of the excavator based on the corresponding positioning system, and determine the current working area and the current loading position according to the current position information; then, the current working area and the current loading position are reported to the background server; after the background server receives it, it is also necessary to send the current working area and the current loading position to the vehicle terminal device via the vehicle dispatch system.

[0069] Secondly, when the vehicle-mounted terminal device receives the current operating area and the current loading position, dynamic path planning can be performed. Specifically, in the process of dynamic path planning, first, the current operating area needs to be gridded to obtain multiple regional grids; wherein, in the process of gridding, the number of grids required to be divided can be determined according to the area size of the current operating area, and then multiple regional grids can be obtained by evenly dividing based on the number of grids. The specific regional grids obtained can be referred to Figure 5 shown.

[0070] In step S220, the regional nodes to be searched are determined based on each of the regional grids, and the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position are determined from the regional nodes to be searched.

[0071] In this example embodiment, first, the node of the area to be searched is determined; specifically, the specific determination process of the node of the area to be searched can be implemented in the following manner: first, the longitude and latitude coordinate information of the four vertices of the area grid is determined; then, the center point of the area grid is determined based on the longitude and latitude coordinate information of the four vertices; finally, the center point is used as the node of the area to be searched; further, after obtaining the node of the area to be searched, the starting search node corresponding to the entry waiting point and the terminating search node corresponding to the current loading position can be determined from the node of the area to be searched; that is, the node of the area to be searched corresponding to the area grid corresponding to the entry waiting point can be used as the starting search node, and the node of the area to be searched corresponding to the area grid corresponding to the current loading position can be used as the terminating search node.

[0072] In step S230, the current global path of the candidate transport vehicle is determined based on the start search node, the end search node, and the historical global path of the candidate transport vehicle.

[0073] Specifically, the specific determination process of the current global path can be implemented in the following manner: child node expansion step: put the starting node as the first parent node into an open list, and expand multiple child nodes corresponding to the first parent node in the open list; current child node determination step: put the first parent node into a closed list, and determine the first current child node corresponding to the first parent node in each of the child nodes based on the principle of minimum total cost value; wherein the total cost value includes a cumulative cost value and an estimated cost value; current child node storage step: when it is determined that the cumulative cost value of the first current child node is less than a preset cost value, the first current child node is stored in a target child node list, and the preset cost value is replaced by the cumulative cost value of the first current child node; loop step: loop the child node expansion step, the current child node determination step and the current child node storage step in sequence until the current child node is the termination search node; trajectory planning step: determine the current global path of the candidate transport vehicle according to each of the current child nodes in the target child node list and the historical global path of the candidate transport vehicle.

[0074] In an exemplary embodiment, determining the first current child node corresponding to the first parent node in each of the child nodes based on the principle of minimizing the total cost value can be achieved in the following manner: calculating the cumulative cost value from the first parent node to each of the child nodes, and calculating the estimated cost value from each of the child nodes to the terminating search node; obtaining the total cost value of each of the child nodes based on each of the cumulative cost values ​​and each of the estimated cost values, and taking the child node with the smallest total cost value as the first current child node. Calculating the estimated cost value from each of the child nodes to the terminating search node can be achieved in the following manner: configuring the weight parameters of each of the child nodes and the terminating search node on the time T axis; obtaining the estimated cost value based on the weight parameters, the Manhattan distance between each of the child nodes and the terminating search node. Furthermore, the specific calculation process of the total cost value can be shown by the following formula (1):

[0075] Node k .f=Node k .g+Node k .h; (1)

[0076] Among them, Node k .f represents the total generation value; Node k .g represents the expansion from the starting node to the current node Nodek The cumulative cost value of Node k .h represents the estimated cost of expanding from the current node to the terminal node. And, since the A* algorithm maintains a dynamic collection called OpenList, which stores the index of the nodes to be explored, if the node Node k At a certain moment, it is saved in OpenList, then the state Node k .is_opened should be set to 1; once a node in the OpenList is explored, it is removed from the OpenList and set to Node k .is_opened = 0, Node k .is_closed=1.

[0077] The specific determination process of the current global path will be further explained and illustrated below. Specifically, the current global path recorded here may include a forward driving path from the entry waiting point to the current pre-parking position and a reverse driving path from the current pre-parking position to the current loading point. Figure 6 As shown, the current pre-parking position recorded here can be Figure 6 As shown in 601 in FIG. 1 and the current loading position can be as shown in FIG. Figure 6 At the same time, the area boundary of the current operation area can be referred to Figure 6 As shown in 603, the current entry waiting point can be referred to Figure 6 As shown in 604, the exit point can be referred to Figure 6 605; that is, in the process of current global path planning, after obtaining the target subnode list, the current pre-stop position can be determined from the current subnode according to each current subnode in the target subnode list and the historical pre-stop positions included in the historical global path of the candidate transport vehicle; then, the forward driving path from the entry waiting point to the current pre-stop position and the reverse driving path from the current pre-stop position to the current loading position are planned. Further, continue to refer to Figure 6 As shown, the planned forward driving path can refer to 606, and the reverse driving path can refer to 607. It should be noted here that if the path planning fails due to boundary constraints or multi-vehicle dynamic planning conflicts, the background needs to report the fault information so that the final path planning can be performed manually.

[0078] In step S140, the candidate transport vehicle is controlled to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0079] Specifically, it can be achieved in the following manner: receiving first instruction information sent by the vehicle dispatching system, and when determining that the first instruction information is a driving instruction, controlling the candidate transport vehicle to travel from the entry waiting point where the current working area is located to the current loading position based on the current global path; when determining that the first instruction information is a waiting instruction, controlling the candidate transport vehicle to wait at the entry waiting point, and receiving second instruction information sent by the vehicle dispatching system; when determining that the second instruction information is a driving instruction, controlling the candidate transport vehicle to travel from the entry waiting point where the current working area is located to the current loading position based on the current global path.

[0080] In an exemplary embodiment, the driving instructions and waiting instructions recorded above are generated by the vehicle dispatching system in the following manner: based on the real-time vehicle position information reported by the candidate transport vehicle, determine whether there is a target transport vehicle to be loaded and / or being loaded in the current operation area; if the target transport vehicle does not exist, generate the driving instructions; if the target transport vehicle exists, generate the waiting instructions.

[0081] In an exemplary embodiment, the second instruction information recorded above is generated by the vehicle dispatching system in the following manner: if there is a target transport vehicle to be loaded in the current operation area and the target transport vehicle is in the process of reversing from the current pre-parking position into the current loading position, then upon detecting that the target transport vehicle to be loaded is already in the current loading position, the second instruction information is generated; wherein, the second instruction information is used to instruct the candidate transport vehicle to travel from the entry waiting point to the current pre-parking position; if there is a target transport vehicle to be loaded and the target transport vehicle is in the process of being loaded, then upon detecting that the target transport vehicle has been loaded and has passed the vehicle projection point on the exit path, the second instruction information is generated; wherein, the second instruction information is used to instruct the candidate transport vehicle to travel from the current pre-parking position to the current loading position. Figure 6 As shown, the configured exit path can refer to 608, the vehicle projection point can refer to 609; the target transport vehicle can refer to Figure 6 The candidate transport vehicles can refer to Figure 6 As shown in vehicle B; the mining equipment can refer to 610.

[0082] The specific control process of the candidate transport vehicle will be further explained and illustrated below. Specifically, in the actual application process, since the operation scene of the open-pit mine is mainly single-shovel operation, the exemplary embodiment of the present disclosure uses single-shovel operation as the background to explain and illustrate the specific driving process of the candidate transport vehicle.

[0083] Furthermore, in the single-shovel operation scenario, in order to improve the overall operation efficiency, a current pre-parking position is set in the current operation area (the current pre-parking position can be defined as: when the target transport vehicle A is in a loading state, the candidate transport vehicle B can drive to the current pre-parking position and stop, and wait for the target transport vehicle A to be loaded and drive out, and then the candidate transport vehicle B can reverse from the current pre-parking position into the current loading position); wherein, the switching point position of the forward parking path and the reverse parking path corresponding to the transport vehicle at the current pre-parking position can be dynamically generated; in the actual application process, if there are no other vehicles in the current operation area (that is, there are no target transport vehicles to be loaded and / or being loaded), the candidate transport vehicle can drive into the operation area based on the parking path generated by dynamic planning (that is, the forward driving path and the reverse driving path) to complete the entire parking process; when there is a target transport vehicle A in the operation area, if the target transport vehicle A is in the parking driving process, If the target transport vehicle A has completed parking, the candidate transport vehicle B will drive to the current pre-parking position based on the parking path generated by dynamic programming and stop to wait. Furthermore, if the target transport vehicle A has completed loading, the exit path of the target transport vehicle A will be generated by dynamic programming in combination with the parking positions of other vehicles in the current operating area (that is, the candidate transport vehicles) (without collision with the positions of other candidate transport vehicles), and the vehicle projection points of the parking positions of other candidate transport vehicles in the current operating area on the exit path will be calculated. When it is determined that the target transport vehicle has exceeded the vehicle projection point, the candidate transport vehicle B can be notified to resume driving from the current pre-parking position to the current loading position. Furthermore, if the number of vehicles in the current operating area is ≥2, in order to ensure safety, the remaining vehicles need to remain at the entry waiting point to wait.

[0084] So far, the control method of the unmanned transport vehicle described in the exemplary embodiments of the present disclosure has been fully implemented. Based on the above-mentioned contents, it can be known that the control method of the unmanned transport vehicle recorded in the exemplary embodiment of the present disclosure can, on the one hand, respond to the change of the excavator position in real time; that is, when the position of the excavator is offset or changed, the system can adjust the path in a small range to ensure that the autonomous driving vehicle can accurately enter and exit the loading area, thereby optimizing the vehicle-excavation collaborative operation process; at the same time, it can also ensure that the unmanned transport vehicle can accurately follow the new trajectory to achieve automated parking and loading operations; on the other hand, the exemplary embodiment of the present disclosure provides real-time feedback information on the excavator operation route through real-time data exchange with the excavator and the transport vehicle, and guides the transport vehicle to accurately park through the information; and, using the dynamic programming algorithm, the system can predict and adjust the vehicle parking position to ensure the accuracy and stability during the parking process, solving the problem of inaccurate parking posture in the existing solution; on the other hand, the exemplary embodiment of the present disclosure implements the path update and adjustment mechanism in an automated manner, which greatly reduces the frequency of manual intervention; and, during the operation process, there is no need to manually re-collect the path or adjust the trajectory, and the system can automatically adapt to changes in the actual operating environment, thereby improving operating efficiency and reducing labor costs. In summary, the example embodiments of the present disclosure optimize the vehicle-excavation collaborative operation path of the unmanned transportation system in open-pit mining operations through a dynamic planning method, which can cope with real-time changes in the operating environment and ensure the accurate parking and efficient operation of the unmanned transportation vehicles.

[0085] The following are embodiments of the device disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the device embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0086] The exemplary embodiment of the present disclosure also provides a control device for an unmanned transport vehicle. Figure 7 As shown, the vehicle driving path planning device may include an operation area gridding module 710, a search node determination module 720, a global path determination module 730 and a vehicle control module 740. Among them:

[0087] The operation area gridding module 710 is used to receive the current operation area and the current loading position of the mining equipment sent by the vehicle dispatching system, and perform gridding processing on the current operation area to obtain multiple area grids;

[0088] A search node determination module 720 is used to determine the regional nodes to be searched based on each of the regional grids, and determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched;

[0089] A global path determination module 730, for determining a current global path of the candidate transport vehicle based on the start search node, the end search node and the historical global path of the candidate transport vehicle;

[0090] The vehicle control module 740 is used to control the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

[0091] In an exemplary embodiment of the present disclosure, based on the starting search node, the terminating search node and the historical global path of the candidate transport vehicle, the current global path of the candidate transport vehicle is determined, including: a child node expansion step: putting the starting node as the first parent node into an open list, and expanding multiple child nodes corresponding to the first parent node in the open list; a current child node determination step: putting the first parent node into a closed list, and determining the first current child node corresponding to the first parent node in each of the child nodes based on the principle of minimum total cost value; wherein the total cost value includes a cumulative cost value and an estimated cost value; a current child node storage step: when it is determined that the cumulative cost value of the first current child node is less than a preset cost value, the first current child node is stored in a target child node list, and the preset cost value is replaced by the cumulative cost value of the first current child node; a loop step: looping the child node expansion step, the current child node determination step and the current child node storage step in sequence until the current child node is the terminating search node; a trajectory planning step: determining the current global path of the candidate transport vehicle according to each of the current child nodes in the target child node list and the historical global path of the candidate transport vehicle.

[0092] In an exemplary embodiment of the present disclosure, the first current child node corresponding to the first parent node is determined in each of the child nodes based on the principle of minimizing the total cost value, including: calculating the cumulative cost value from the first parent node to each of the child nodes, and calculating the estimated cost value from each of the child nodes to the termination search node; obtaining the total cost value of each of the child nodes based on each of the cumulative cost values ​​and each of the estimated cost values, and taking the child node with the smallest total cost value as the first current child node.

[0093] In an exemplary embodiment of the present disclosure, the estimated cost value from each of the child nodes to the terminating search node is calculated, including: configuring weight parameters of each of the child nodes and the terminating search node on the time T axis; and obtaining the estimated cost value based on the weight parameters and the Manhattan distance between each of the child nodes and the terminating search node.

[0094] In an exemplary embodiment of the present disclosure, controlling the candidate transport vehicle to travel from the entry waiting point where the current work area is located to the current loading position based on the current global path includes: receiving first instruction information sent by a vehicle scheduling system, and when determining that the first instruction information is a driving instruction, controlling the candidate transport vehicle to travel from the entry waiting point where the current work area is located to the current loading position based on the current global path; when determining that the first instruction information is a waiting instruction, controlling the candidate transport vehicle to wait at the entry waiting point, and receiving second instruction information sent by the vehicle scheduling system; when determining that the second instruction information is a driving instruction, controlling the candidate transport vehicle to travel from the entry waiting point where the current work area is located to the current loading position based on the current global path.

[0095] In an exemplary embodiment of the present disclosure, the driving instruction and the waiting instruction are generated by the vehicle dispatching system in the following manner: based on the real-time vehicle position information reported by the candidate transport vehicle, determine whether there is a target transport vehicle to be loaded and / or being loaded in the current operation area; if the target transport vehicle does not exist, generate the driving instruction; if the target transport vehicle exists, generate the waiting instruction.

[0096] In an exemplary embodiment of the present disclosure, the second instruction information is generated by the vehicle scheduling system in the following manner: if there is a target transport vehicle to be loaded in the current operating area and the target transport vehicle is in the process of reversing from the current pre-parking position into the current loading position, then when it is detected that the target transport vehicle to be loaded is already in the current loading position, the second instruction information is generated; wherein, the second instruction information is used to instruct the candidate transport vehicle to travel from the entry waiting point to the current pre-parking position; if there is a target transport vehicle to be loaded and the target transport vehicle is in the process of being loaded, then after it is detected that the loading of the target transport vehicle is completed and the target transport vehicle passes the vehicle projection point on the exit path, the second instruction information is generated; wherein, the second instruction information is used to instruct the candidate transport vehicle to travel from the current pre-parking position to the current loading position.

[0097] The specific details of each module in the above-mentioned vehicle driving path planning device have been described in detail in the corresponding vehicle driving path planning method, so they will not be repeated here.

[0098] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0099] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0100] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. A person skilled in the art will appreciate that various aspects of the present disclosure may be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which may be collectively referred to herein as a circuit, module, or system.

[0101] Refer to the following Figure 8 The electronic device 800 according to this embodiment of the present disclosure is described. Figure 8 The electronic device 800 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0102] like Figure 8 As shown, the electronic device 800 is in the form of a general computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.

[0103] The storage unit stores program codes, which can be executed by the processing unit 810, so that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 810 can perform the following steps: Figure 1Step S110 shown in: receiving the current operating area and current loading position of the mineral mining equipment sent by the vehicle dispatching system, and gridding the current operating area to obtain multiple regional grids; step S120: determining the regional nodes to be searched based on each of the regional grids, and determining the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched; step S130: determining the current global path of the candidate transport vehicle based on the starting search node, the ending search node and the historical global path of the candidate transport vehicle; step S140: controlling the candidate transport vehicle to travel from the entry waiting point where the current operating area is located to the current loading position based on the current global path.

[0104] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0105] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0106] Bus 830 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0107] The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 800, and / or communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 850. Furthermore, the electronic device 800 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 860. As shown, the network adapter 860 communicates with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0108] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0109] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0110] According to the program product for implementing the above method in the embodiment of the present disclosure, it can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0111] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0112] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0113] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0114] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0115] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0116] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the inventions invented herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not invented by the present disclosure. The specification and examples are to be considered merely exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A control method for an unmanned transport vehicle, characterized in that: include: Receiving the current operation area and the current loading position of the mining equipment sent by the vehicle dispatching system, and performing grid processing on the current operation area to obtain a plurality of area grids; Determine the regional nodes to be searched based on each of the regional grids, and determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched; Determine a current global path of the candidate transport vehicle based on the starting search node, the ending search node, and the historical global path of the candidate transport vehicle; The candidate transport vehicle is controlled to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

2. The control method of the unmanned transport vehicle according to claim 1, characterized in that: Determining a current global path of the candidate transport vehicle based on the start search node, the end search node, and the historical global path of the candidate transport vehicle includes: Subnode expansion step: putting the start node as the first parent node into an open list, and expanding multiple subnodes corresponding to the first parent node in the open list; The current child node determination step: putting the first parent node into a closed list, and determining the first current child node corresponding to the first parent node in each of the child nodes based on the principle of minimum total cost value; wherein the total cost value includes a cumulative cost value and an estimated cost value; Current child node storage step: when it is determined that the accumulated cost value of the first current child node is less than a preset cost value, storing the first current child node in a target child node list, and replacing the preset cost value with the accumulated cost value of the first current child node; Looping step: looping the sub-node expansion step, the current sub-node determination step and the current sub-node storage step in sequence until the current sub-node is the terminating search node; Trajectory planning step: determining the current global path of the candidate transport vehicle according to each of the current sub-nodes in the target sub-node list and the historical global path of the candidate transport vehicle.

3. The control method of the unmanned transport vehicle according to claim 2, characterized in that: Determining the first current child node corresponding to the first parent node based on the principle of minimizing the total cost value among the child nodes includes: Calculate the cumulative cost value from the first parent node to each of the child nodes, and calculate the estimated cost value from each of the child nodes to the terminating search node; According to each of the accumulated cost values ​​and each of the estimated cost values, a total cost value of each of the child nodes is obtained, and the child node with the smallest total cost value is used as the first current child node.

4. The control method of the unmanned transport vehicle according to claim 3, characterized in that: Calculating the estimated cost value from each of the child nodes to the terminating search node, including: Configuring weight parameters of each of the subnodes and the terminating search node on the time T axis; The estimated cost value is obtained according to the weight parameter, the Manhattan distance between each of the child nodes and the terminating search node.

5. The control method of the unmanned transport vehicle according to claim 1, characterized in that: Controlling the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path includes: receiving first instruction information sent by the vehicle dispatching system, and when determining that the first instruction information is a driving instruction, controlling the candidate transport vehicle to drive from the entry waiting point where the current operation area is located to the current loading position based on the current global path; When it is determined that the first instruction information is a waiting instruction, controlling the candidate transport vehicle to wait at the entry waiting point, and receiving the second instruction information sent by the vehicle dispatching system; When it is determined that the second instruction information is a driving instruction, the candidate transport vehicle is controlled to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

6. The control method of the unmanned transport vehicle according to claim 5, characterized in that: The driving instruction and the waiting instruction are generated by the vehicle dispatching system in the following manner: Based on the real-time vehicle position information reported by the candidate transport vehicles, determining whether there is a target transport vehicle to be loaded and / or being loaded in the current operation area; If the target transport vehicle does not exist, the driving instruction is generated; if the target transport vehicle exists, the waiting instruction is generated.

7. The control method of the unmanned transport vehicle according to claim 6, characterized in that: The second instruction information is generated by the vehicle dispatching system in the following manner: If there is a target transport vehicle to be loaded in the current operation area and the target transport vehicle is in the process of reversing from the current pre-parking position to the current loading position, then when it is detected that the target transport vehicle to be loaded is already in the current loading position, the second instruction information is generated; wherein the second instruction information is used to instruct the candidate transport vehicle to travel from the entry waiting point to the current pre-parking position; If there is a target transport vehicle to be loaded and the target transport vehicle is in the process of being loaded, the second instruction information is generated after detecting that the loading of the target transport vehicle is completed and the target transport vehicle passes the vehicle projection point on the exit path; wherein the second instruction information is used to instruct the candidate transport vehicle to travel from the current pre-parking position to the current loading position.

8. A control device for an unmanned transport vehicle, characterized in that: include: An operation area gridding module is used to receive the current operation area and current loading position of the mining equipment sent by the vehicle dispatching system, and perform gridding processing on the current operation area to obtain multiple area grids; A search node determination module is used to determine the regional nodes to be searched based on each of the regional grids, and to determine the starting search node corresponding to the entry waiting point and the ending search node corresponding to the current loading position from the regional nodes to be searched; A global path determination module, configured to determine a current global path of the candidate transport vehicle based on the start search node, the end search node, and the historical global path of the candidate transport vehicle; The vehicle control module is used to control the candidate transport vehicle to travel from the entry waiting point where the current operation area is located to the current loading position based on the current global path.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the control method of the unmanned transport vehicle according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the control method of the unmanned transport vehicle according to any one of claims 1 to 7 by executing the executable instructions.

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