A multi-AGV traffic control method based on time window

Abstract

The invention discloses a multi-AGV traffic control method based on a time window, comprising: for a transportation system with multiple adjacent paths in an area; a dispatching system loads a path map and vehicle information of vehicles, the vehicle information including vehicle number and vehicle size; generates a vehicle contour curve of vehicles moving on all paths; digitizes AGV working environment information to obtain an AGV working environment electronic map; plans a path with the shortest time for the AGV on the AGV working environment electronic map; the invention aims at the conflict and deadlock problems arising in AGV traffic control, proposes a strategy for solving the conflict and deadlock based on a time window algorithm, and proposes a method for reserving a reverse path for a dead-end path; classifies and predicts conflict types, and rearranges the conflict time windows; and for deadlock conflicts, finds an avoidance point through a breadth search algorithm to solve the deadlock.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV traffic control, and in particular to a multi-AGV traffic control method based on a time window. Background Art

[0002] With the rapid development of artificial intelligence, the promotion of Industry 4.0 related policies has accelerated the transformation of artificial intelligence. The strategy of combining traditional manufacturing with artificial intelligence has promoted the development of the country and enterprises. AGV is an indispensable part of the intelligent manufacturing system and an important part of smart logistics. After entering the 21st century, intelligent mobile robots have developed rapidly and played an important role in daily life and production. AGV has been applied in many fields such as logistics sorting, intelligent warehousing, and safe manufacturing. The intelligent revolution has gradually transformed traditional manual workshops into smart workshops. Enterprises have higher and higher requirements for production efficiency, and the original single AGV has gradually increased to multiple AGVs to meet the needs of complex production environments. In the intelligent manufacturing scenario, the traffic control of multiple AGVs has a great impact on transportation efficiency.

[0003] In order to avoid collisions between vehicles on the same path, traffic control is implemented on AGVs in the entire system to ensure that AGVs that need to perform tasks on the same path do not collide. However, as AGVs are more widely used, the number of paths set up in production sites has increased, and the distance between two adjacent paths is too small, resulting in collisions between multiple AGVs on multiple adjacent paths in a certain area. The existing traffic control methods are unable to cope with the above situation. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a multi-AGV traffic control method based on a time window, which solves the conflict and deadlock problems of multi-AGV paths and ensures the stable operation of AGVs.

[0005] To achieve the above effects, the technical solution of the present invention is as follows:

[0006] A multi-AGV traffic control method based on a time window comprises the following steps:

[0007] Step 1: For a transportation system with multiple adjacent paths in an area, the dispatching system loads the path map and the vehicle information of the vehicle, the vehicle information including the vehicle number and the vehicle size, and generates a vehicle profile curve of the vehicle moving on all the paths;

[0008] Step 2: Digitize the AGV working environment information to obtain an electronic map of the AGV working environment; plan the shortest path for the AGV on the electronic map of the AGV working environment; the AGV receives the task information issued by the scheduling system, and the task includes the task starting point, the task end point and the AGV that performs the task;

[0009] The dispatching system queries the path that the AGV needs to use to perform the task based on the path map and obtains the corresponding task path list;

[0010] Step 3: The scheduling system obtains the path from the current point to the task starting point and the path from the task starting point to the task end point through the preset multi-AGV path planning algorithm. The path from the AGV current point to the task starting point is used as the main path, and the path from the task starting point to the task end point is used as the reverse reservation path.

[0011] Step 4: The scheduling system allocates time windows to AGVs and uses time windows to detect conflicts between AGVs in advance;

[0012] Step 5: The scheduling system determines whether there is a conflict between the paths of the AGVs based on the task path list, and avoids the conflict by waiting or changing the path;

[0013] If there is no conflict, the task is executed according to the current path time window; if there is a conflict, the conflict type is determined; the conflict types are divided into: opposite conflict, same-direction conflict and intersection conflict;

[0014] If the conflict type is an intersection conflict or a same-direction conflict, the time windows are rearranged by waiting to resolve the conflict, thereby successfully receiving the task information. The time windows are updated after each arrival at the station.

[0015] If the conflict type is a head-on conflict, a breadth-based search algorithm is used to find an avoidance point so that the AGV can temporarily calculate the route to this site for avoidance. If no avoidance point is found, this task is canceled and the AGV returns to the parking point along the original path.

[0016] Furthermore, in step 1, the scheduling system generates a vehicle profile curve of the vehicle moving at a set moving speed on the path according to the vehicle size and the path map.

[0017] Furthermore, step 1 uses a topological method to construct a bidirectional single-path AGV environment electronic map:

[0018] Set the corresponding nodes according to the docking points where the AGV needs to stop, and use the coordinates (id, x, y) to represent them, where id represents the node number, and (x, y) represents the time required for the AGV to reach the node's horizontal and vertical coordinates from the origin; set the intermediate nodes for the AGV to go back and forth between the work points; after the nodes are set, connect the adjacent nodes with edges to form a mesh structure diagram.

[0019] Furthermore, the paths along which the AGVs travel are all bidirectional multi-AGV travel channels.

[0020] Furthermore, the ground on which the AGV travels is provided with track marks that are different from the ground color. The AGV acquires ground images during travel and identifies the track marks on the ground through visual processing.

[0021] Furthermore, step 4 allocates time windows to AGVs, including:

[0022] After AGV performs path planning, it calculates the time information of the planned route. The time window of the path is [t start ,t end ], that is, the AGV occupies this path from t start From time t end At the end of time, if at t start The point where the AGV is located at the moment is the starting point of the route, then t start is the zero time; if at t start The point where the AGV is located at the moment is not the starting point of the route, then t start is the t of the previous path segment end , the calculation method is as follows:

[0023]

[0024] Among them, L represents the length of the path between adjacent nodes, and v represents the speed of the AGV;

[0025] The set of AGVs is R = {r1, r2, ..., r n}, r i represents the i-th AGV, and the path set of AGV is P = {l1,l2,...,l n}, the set of sites in the path is Vex = {v1,v2,...,v n}, the path of the i-th AGV is P i ={l1,l2,...,l n}, where the path information of the kth segment is na and nb are the two endpoints of the k-th path.

[0026] Furthermore, the steps of the breadth search algorithm described in step 5 are as follows:

[0027] (1) Initialize the set S and the set U;

[0028] (2) Put the current node into the set S as the point that has been searched;

[0029] (3) Search the neighboring nodes of the current point in sequence. If the neighboring node is within the associated path range of the current station of the conflicting AGV, the neighboring node is regarded as an obstacle point and added to the U set. The node that can only be reached by AGV is no longer explored.

[0030] If the adjacent node is not within the associated path range of the conflicting AGV's current station, add the adjacent node to the set S;

[0031] (4) If the adjacent node is no longer within the associated path range of the remaining path of the conflicting AGV, a site that can be avoided is found, and the breadth search algorithm ends the search;

[0032] (5) If the adjacent node is still within the associated path range where the remaining path of the conflicting AGV is located, continue to explore the next adjacent node of the current node. If the adjacent nodes are exhausted, select one adjacent node from these adjacent nodes as the next node, and repeat this cycle until the avoidance point is found.

[0033] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0034] The present invention aims at the conflict and deadlock problems in AGV traffic control, and proposes a strategy to solve the conflict and deadlock based on the time window algorithm. Due to the influence of AGV volume on the path, the associated path is introduced to ensure the safety between AGVs. For dead-end paths, a method of reverse reservation path is proposed; conflict types are classified and predicted, and the time window of the conflict is rearranged; for deadlock conflicts, the deadlock is solved by finding an avoidance point through a breadth search algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flow chart of the method of the present invention;

[0036] Figure 2 This is a schematic diagram of intersection conflicts of the present invention;

[0037] Figure 3 This is a schematic diagram of the pursuit conflict of the present invention;

[0038] Figure 4 This is a schematic diagram of the conflict of the present invention;

[0039] Figure 5 It is a schematic diagram of the operation flow of the present invention;

[0040] Figure 6 It is a schematic diagram of a dead end in the scenario of the present invention;

[0041] Figure 7 This is a schematic diagram of the AGV of the present invention as a particle operation;

[0042] Figure 8It is a schematic diagram of the actual volume operation of the AGV of the present invention;

[0043] Fig. 9 It is a schematic diagram of the association path of the present invention;

[0044] Fig.10 It is a conflict schematic diagram of the present invention;

[0045] Fig.11 is a schematic diagram of the associated path of point S95 of the present invention;

[0046] Fig.12 It is a schematic diagram of the intersecting conflicting time windows of the present invention;

[0047] Fig.13 This is a schematic diagram of the same-direction operation of the present invention;

[0048] Fig.14 is the associated path of point S96 of the present invention;

[0049] Fig.15 This is a schematic diagram of the same-direction conflict time window of the present invention;

[0050] Fig.16 This is a schematic diagram of the conflict of the present invention;

[0051] Fig.17 It is a schematic diagram of finding an avoidance point according to the present invention;

[0052] Fig.18 This is a schematic diagram of the opposite conflict time window of the present invention;

[0053] Fig.19 It is a schematic diagram of the flow of the breadth search algorithm of the present invention;

[0054] Fig. 20 This is a topological diagram of the test environment of Case 1 of the present invention;

[0055] Fig.21 This is a topological diagram of the test environment of Case 2 of the present invention. DETAILED DESCRIPTION

[0056] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0057] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0058] Example

[0059] This embodiment proposes a multi-AGV traffic control method based on time window. Figure 1 , including the following steps:

[0060] Step 1: For a transportation system with multiple adjacent paths in an area, the dispatching system loads the path map and the vehicle information of the vehicle, the vehicle information including the vehicle number and the vehicle size, and generates a vehicle profile curve of the vehicle moving on all the paths;

[0061] Step 2: Digitize the AGV working environment information to obtain an electronic map of the AGV working environment; plan the shortest path for the AGV on the electronic map of the AGV working environment; the AGV receives the task information issued by the scheduling system, and the task includes the task starting point, the task end point and the AGV that performs the task;

[0062] The dispatching system queries the path that the AGV needs to use to perform the task based on the path map and obtains the corresponding task path list;

[0063] Step 3: The scheduling system obtains the path from the current point to the task starting point and the path from the task starting point to the task end point through the preset multi-AGV path planning algorithm. The path from the AGV current point to the task starting point is used as the main path, and the path from the task starting point to the task end point is used as the reverse reservation path.

[0064] Step 4: The scheduling system allocates time windows to AGVs and uses time windows to detect conflicts between AGVs in advance;

[0065] Step 5: The scheduling system determines whether there is a conflict between the paths of the AGVs based on the task path list, and avoids the conflict by waiting or changing the path;

[0066] If there is no conflict, the task is executed according to the current path time window; if there is a conflict, the conflict type is determined; the conflict types are divided into: opposite conflict, same-direction conflict and intersection conflict;

[0067] If the conflict type is an intersection conflict or a same-direction conflict, the time windows are rearranged by waiting to resolve the conflict, thereby successfully receiving the task information. The time windows are updated after each arrival at the station.

[0068] If the conflict type is a head-on conflict, a breadth-based search algorithm is used to find an avoidance point so that the AGV can temporarily calculate the route to this site for avoidance. If no avoidance point is found, this task is canceled and the AGV returns to the parking point along the original path.

[0069] It is understandable that a conflict usually only occurs when the AGV has completed the previous task and is about to return to the parking point but suddenly receives a new task. The route calculated from the current station to the starting point of the new task causes a conflict. You can try to recalculate the route, but the calculated route result may be consistent with the original result.

[0070] As a preferred technical solution, in this embodiment, in step 1, the scheduling system generates a vehicle contour curve of the vehicle moving at a set moving speed on the path according to the vehicle size and the path map.

[0071] As a preferred technical solution, in this embodiment, step 1 uses a topological method to construct a bidirectional single-path AGV environment electronic map:

[0072] Set the corresponding nodes according to the docking points where the AGV needs to stop, and use the coordinates (id, x, y) to represent them, where id represents the node number, and (x, y) represents the time required for the AGV to reach the node's horizontal and vertical coordinates from the origin; set the intermediate nodes for the AGV to go back and forth between the work points; after the nodes are set, connect the adjacent nodes with edges to form a mesh structure diagram.

[0073] As a preferred technical solution, in this embodiment, the paths along which the AGVs travel are all bidirectional multi-AGV travel channels.

[0074] As a preferred technical solution, in this embodiment, the ground on which the AGV travels is provided with track marks that are different from the ground color. The AGV obtains ground images during travel and identifies the track marks on the ground through visual processing.

[0075] In this embodiment, the AGV is equipped with a camera or other visual sensor to capture images of the garage floor. These images are transmitted to the robot's visual processing system for analysis. The visual processing system uses computer vision technology to identify track markings on the garage floor. Computer vision technology includes methods such as color recognition, shape detection, and pattern matching to determine the location and type of the markings. Once the track markings are identified, the AGV can adjust its navigation path in real time based on these markings, that is, it can follow markings of specific colors or shapes to follow the correct route, avoid obstacles, or find the correct parking space. The use of track markings can improve the positioning accuracy of the AGV. This is essential for high-precision navigation in complex environments such as double-decker garages. The use of track markings also helps to improve the safety of handling tasks because it can help robots avoid obstacles, follow the correct route, and reduce the occurrence of accidents.

[0076] As a preferred technical solution, in this embodiment, the intersection conflicts of the present invention are divided into three types. The first type is as follows: Figure 2 As shown in (a), (1) intersection conflict, where the vehicles intersect at the intersection and drive in different directions; Figure 2 As shown in (b), they intersect at the intersection at the crossroads and then drive in the same direction. The third type is Figure 2 As shown in (c), the AGVs intersect not only at a point of intersection, but also at a section of route. However, at this time, the two AGVs are not on each other's route, which is considered an intersection conflict. The fourth type is Figure 2 The situation shown in (d) is affected by the association path.

[0077] (2) Chasing Conflict

[0078] A chasing conflict refers to two AGVs running in the same direction, one in front of the other. Under normal circumstances, the AGVs are set to run at the same speed. However, the front vehicle may detect an obstacle and slow down or stop. At this time, the speed of the rear AGV is higher than that of the front vehicle. Figure 3 As shown in (a), if the second AGV slows down, the first AGV is controlled and a chasing conflict occurs. Figure 3 (b) Also due to the relationship between the associated paths, a chasing conflict also occurs.

[0079] (3) Opposing Conflicts

[0080] A head-on collision is when two AGVs are moving towards each other. Figure 4 As shown in (a), the difference from the third type of intersection conflict is that the two AGVs are currently on each other's route and the conflict cannot be resolved by waiting, e.g. Figure 4 (b) also shows a situation of opposite conflict.

[0081] like Figure 5 As shown, in the actual AGV operation scenario, the AGV runs from parking point 1 to task starting point 2 to carry materials, then runs from task starting point 2 to task end point 3 to drop the materials, and then runs from task end point 3 back to parking point 1 or the starting point of the new task.

[0082] There are dead ends in some scenarios, such as Figure 6 As shown in , if we only consider the route of AGV entering and not the route of AGV exiting, a deadlock will occur. Figure 6 As shown in (a), when the first AGV arrives at task point 2, the second AGV follows closely to task point 3, but after the first AGV completes the task, it returns from task point 2 to point 4. Figure 6 As shown in (b), at this time, the second AGV has blocked the first AGV's route out, causing a deadlock. To avoid this situation, the concept of reverse reservation path is introduced; under normal circumstances, the AGV only calculates the path from the current point to the starting point of the task, and records this path as path S1, and the path from the starting point of the task to the end of the task is recorded as path S2. The reverse reservation path is that the AGV calculates path S1 and path S2, and adds path S1 and path S2 as the total path of the current AGV. Every time a task point is reached, the path from the next task point to the next task point is filled in again.

[0083] Considering AGV as a point mass, collisions between AGVs can only occur on the same route. The present invention takes into account the volume of AGVs and the possibility of collisions with AGVs on other routes during transportation, so the concept of associated paths is proposed. Figure 7 As shown, the AGV is regarded as a particle.

[0084] like Figure 8 As shown, the volume of the AGV is restored, and two AGVs will collide. The route where the AGV currently collides with the AGV on other routes is called the associated path of the current AGV's route, and the route is given the same attributes as the route where the current AGV is located.

[0085] like Fig. 9 As shown, when the AGV is in the path S706 - S702 , the outline of the AGV may be in the range marked by the blue box in the figure, and the associated path is the path segment marked in brown in the figure.

[0086] The present invention focuses on the traffic control method, so the path planning in step three can be various path planning algorithms.

[0087] Step 4 allocates time windows to AGVs, including:

[0088] After AGV performs path planning, it calculates the time information of the planned route. The time window of the path is [t start ,tend ], that is, the AGV occupies this path from t start From time t end At the end of time, if at t start The point where the AGV is located at the moment is the starting point of the route, then t start is the zero time; if at t start The point where the AGV is located at the moment is not the starting point of the route, then t start is the t of the previous path segment end , the calculation method is as follows:

[0089]

[0090] Among them, L represents the length of the path between adjacent nodes, and v represents the speed of the AGV. The body length of the AGV is l. The body length of the AGV is not added to the path length L to calculate the total running time on the path. This is because by introducing the associated path, the time window information of the associated path and the time window information of the path itself are consistent. The role is to expand the scope of the route currently occupied by the AGV, so the size of the AGV can be ignored.

[0091] The set of AGVs is R = {r1, r2, ..., r n}, r i represents the i-th AGV, and the path set of AGV is P = {l1,l2,...,l n}, the set of sites in the path is Vex = {v1,v2,...,v n}, the path of the i-th AGV is P i ={l1,l2,...,l n}, where the path information of the kth segment is na and nb are the two endpoints of the k-th path.

[0092] The present invention rearranges the conflicting time windows; due to centralized scheduling, only one AGV calculates the path at the same time. After the current AGV calculates the path and allocates the time window, the AGV that calculates the path and allocates the time window can only change its own path and time window on this basis, and cannot change the path and time window of the previous AGV; according to Fig.10 For analysis, the route of the first AGV is S82, S83, S84, S85, S86, and the route of the second AGV is S95, S94, S84, S91, S92. Assuming that the first AGV has calculated the path and allocated the time window, the second AGV starts calculating immediately afterwards. The calculation time is negligible compared to the AGV running time; the length of each path segment is the same, given by Fig.10It can be seen that there may be a conflict at S84. Considering the problem of associated paths, such as Fig.11 As shown in FIG. 1 , when the second AGV is at the S95 station, it just does not affect the S84 station. Fig.11 Shown is the associated path of point S95;

[0093]

[0094] in, Indicates the station numbered i of the first AGV, GR AGV2 represents the path set and associated path set of the second AGV, represents the starting time window of the associated path set of the second AGV station i, represents the starting time window of the associated path set of the first AGV station i, and They represent the cutoff time windows of the associated path sets of the second AGV and the first AGV’s station i, respectively. When equation (2) is satisfied, there is an intersection conflict between the AGVs;

[0095] The time windows of the two AGVs are sorted as follows: Fig.12 As shown in (a), the first AGV corresponds to the blue time window, the second AGV corresponds to the red time window, the solid line is the current occupied path, and the dotted line is the associated path; Fig.12 (a) It can be clearly seen that there are conflicts at paths 84-91, 94-84, 95-94, 84-85, 83-84, and 82-83. The second AGV can resolve the conflict by waiting for 8 units of time at the station. The time window arrangement after resolving the conflict is as follows: Fig.12 (b)

[0096] according to Fig.13 According to the analysis, the route of the first AGV is S96, S98, S99, S100, S101, and the route of the second AGV is S105, S106, S107;

[0097] Although the two AGVs are not on the same route, the paths of the AGVs can be related; Fig.14 As shown in FIG. 1 , it is the associated path of station S96; at this time, if the second AGV detects an obstacle and slows down, the first AGV catches up with the second AGV, resulting in a same-direction chasing conflict; under normal circumstances, there is no same-direction conflict between AGVs, and the time window of the first AGV is behind the time window of the second AGV. At this time, the second AGV slows down and stops. When the time window is updated, the second AGV is not moved downward. The time window of the first AGV moves downward because the path is released, thereby satisfying formula (3), resulting in the detection of a time window conflict;

[0098]

[0099] At this time, the time windows of the two AGVs are arranged as follows: Fig.15 As shown in (a), the first AGV can resolve the conflict by waiting for 6 units of time at point S98. Fig.15 (b)

[0100] according to Fig.16 According to the analysis, the path of the first AGV is S110, S111, S112, S113, S114, S109, and the path of the second AGV is S115, S121, S122, S123;

[0101] Under normal circumstances, the two AGVs do not collide with each other. At this time, the second AGV receives a new task, and the path is S115, S121, S120, S119, S116, S117, and a collision occurs.

[0102]

[0103]

[0104]

[0105]

[0106] Among them C AGV1 Indicates the current station of the first AGV, C AGV2 It indicates the current site of the second AGV. It means the first AGV is at the i-th station. It means that the second AGV is at the jth station. If the above equations (4), (5), (6), and (7) are met, the conditions for the conflict are met. At this time, a deadlock occurs, and the waiting strategy cannot solve this situation.

[0107] like Fig.17 As shown, the second AGV must avoid the obstacle by recalculating the route or finding an avoidance point.

[0108] The time windows of the two AGVs are sorted as follows: Fig.18 As shown in the figure, the blue solid line represents the time window actually corresponding to the first AGV body, the blue dotted line represents the time window of the first AGV associated path, the red solid line represents the time window actually corresponding to the second AGV body, and the red dotted line represents the time window of the second AGV associated path; at the beginning, the second AGV did not receive a new task, and the time window was allocated as follows Fig.18 As shown in (a), when the second AGV receives a new task, it recalculates the route and calculates the time window to obtain the following results: Fig.18As shown in (b), the red solid line and the blue dotted line overlap, indicating that the second AGV enters the associated path of the first AGV and will collide; avoidance is achieved by finding an avoidance point, such as Fig.18 (c) shows the time window allocation diagram obtained after the second AGV finds the avoidance point S122, which effectively solves the deadlock conflict in the opposite direction.

[0109] As a preferred technical solution, in this embodiment, in order to solve the deadlock conflict, the present invention compares the breadth search algorithm, the depth search algorithm, the A* search algorithm and the Dijkstra algorithm, and combines the method of solving the deadlock problem to find an avoidance point. No clear end point is given, so the A* algorithm and the Dijkstra algorithm are not applicable. The characteristic of the depth search algorithm is to search in one direction, so the point found may not be the best. The present invention chooses to use the breadth search algorithm to find the avoidance point. Fig.19 As shown, the steps of the breadth search algorithm described in step 5 are as follows:

[0110] (1) Initialize the set S and the set U;

[0111] (2) Put the current node into the set S as the point that has been searched;

[0112] (3) Search the neighboring nodes of the current point in sequence. If the neighboring node is within the associated path range of the current station of the conflicting AGV, the neighboring node is regarded as an obstacle point and added to the U set. The node that can only be reached by AGV is no longer explored.

[0113] If the adjacent node is not within the associated path range of the conflicting AGV's current station, add the adjacent node to the set S;

[0114] (4) If the adjacent node is no longer within the associated path range of the remaining path of the conflicting AGV, a site that can be avoided is found, and the breadth search algorithm ends the search;

[0115] (5) If the adjacent node is still within the associated path range where the remaining path of the conflicting AGV is located, continue to explore the next adjacent node of the current node. If the adjacent nodes are exhausted, select one adjacent node from these adjacent nodes as the next node, and repeat this cycle until the avoidance point is found.

[0116] It should be noted that the present invention sets case 1: Fig. 20As shown, the map includes production area, raw material area, parking point, and charging point. Production area: AGV transports various raw materials here, and the machine processes these raw materials into tires. Raw material area: responsible for the production of raw materials, AGV transports raw materials from here to the production area, and transports empty material racks loaded with raw materials from the production area back to the raw material area. Parking point: AGV stops here when there is no task. Charging point: When the AGV power is lower than a certain threshold, the AGV comes to the charging point for charging. Test the AGV deadlock release function in this environment.

[0117] For implementation case 1, the first AGV and the second AGV start at parking points S57 and S48 respectively. The task is to move from point S61 to point S25. The task generation time is 0.5 minutes. When the task is generated, the first AGV receives the task first. The path information of the AGV is shown in Table 1.

[0118] Table 1 AGV path information

[0119]

[0120]

[0121] At this time, the second AGV has not received the task, and the path information is empty. 0.5 minutes later, the second task is generated, the second AGV receives the task, and the path information is shown in Table 2.

[0122] Table 2 AGV path information

[0123]

[0124] After the first AGV arrives at point S61, both the main path and the reverse reserved path change, as shown in Table 3.

[0125] Table 3 AGV path information

[0126]

[0127] When the second AGV arrives at point S49, an intersection conflict occurs. The second AGV waits here. After the first AGV arrives at point S22, the conflict is resolved and the second AGV continues to walk. After the first AGV walks to point S25 and completes the task, the reverse reservation path becomes the main path. The first AGV is ready to return to the parking point, but it receives a new task at this time. The new path information is shown in Table 4.

[0128] Table 4 AGV path information

[0129]

[0130]

[0131] After conflict detection, the first AGV and the second AGV have a deadlock of conflicting in opposite directions. The first AGV solves the deadlock by finding an avoidance point, avoids at point S49, and recalculates the path to obtain path information as shown in Table 5. The deadlock is resolved.

[0132] Table 5 AGV path information

[0133]

[0134] The present invention sets case 2 as Fig.21 As shown in Table 6, there are 7 production machines, 6 raw material stations, 7 parking spots and 2 charging spots, and there are fire passages and dead-end paths. The stability of the AGV traffic control algorithm is tested in this environment. For the second test environment, as shown in Table 6, it is the station where the material type is located. As shown in Table 7, it is the production area.

[0135] Table 6 Material Type Station

[0136]

[0137] Table 7 Production area

[0138]

[0139] The raw materials in the tire manufacturing workshop include sidewalls, inner liners, steel belts, carcasses, and treads. Each raw material station produces one raw material, and each production area station produces the above five raw materials. Each production area station returns empty pallets to the raw material area, with a total of six task types. According to the rhythm of a certain tire production workshop, basically one material is produced every 10 minutes. Therefore, the raw material task generation and release interval is 10 minutes, and the return task release time is 2 minutes. This implementation case tests the effectiveness and stability of the traffic control algorithm in this article. After two hours of running tests, a total of 17 deadlock situations occurred, and all were resolved.

[0140] Table 8 Operation results

[0141]

[0142]

[0143] Implementing the embodiments of the present invention has the following beneficial effects:

[0144] After the control system where the AGV is located generates a task, it assigns the task to the AGV. The AGVs that can be assigned tasks include AGVs that are idle at the parking point, AGVs that have completed the task and are returning to the parking point or charging point, and AGVs that are charging at the charging point and have reached a high power level. After each AGV receives the task, it starts to calculate from the current site to the starting point of the task, and then calculates from the starting point of the task to the end point of the task. The latter section of the path is used as the reverse reservation path of the AGV, the purpose is to reduce deadlock conflicts on dead ends or two-way routes. After the calculation is successful, the time window is allocated and the conflict type is determined. For conflicts that can be resolved by adjusting the time window and conflicts that cannot be resolved by adjusting the time window, different solutions are used to solve the problem. Enable the AGVs in the entire system to run smoothly without deadlock.

[0145] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation methods of the present invention. For those skilled in the art, different forms of changes or modifications of the AGV can be made based on the above description. It is not necessary and impossible to list all implementation methods here. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A multi-AGV traffic control method based on time window, characterized in that: The following steps are involved: Step 1: For a transportation system with multiple adjacent paths in the region; The dispatching system loads the route map and the vehicle information of the vehicle, the vehicle information including the vehicle number and the vehicle size; generates the vehicle profile curve of the vehicle moving on all the routes; Step 2: Digitize the AGV working environment information to obtain an electronic map of the AGV working environment; plan the shortest path for the AGV on the electronic map of the AGV working environment; the AGV receives the task information issued by the scheduling system, and the task includes the task starting point, the task end point and the AGV that performs the task; The dispatching system queries the path that the AGV needs to use to perform the task based on the path map and obtains the corresponding task path list; Step 3: The scheduling system obtains the path from the current point to the task starting point and the path from the task starting point to the task end point through the preset multi-AGV path planning algorithm. The path from the AGV current point to the task starting point is used as the main path, and the path from the task starting point to the task end point is used as the reverse reservation path. Step 4: The scheduling system allocates time windows to AGVs and uses time windows to detect conflicts between AGVs in advance; Step 5: The scheduling system determines whether there is a conflict between the paths of the AGVs based on the task path list, and avoids the conflict by waiting or changing the path; If there is no conflict, the task is executed according to the current path time window; if there is a conflict, the conflict type is determined; the conflict types are divided into: opposite conflict, same-direction conflict and intersection conflict; If the conflict type is an intersection conflict or a same-direction conflict, the time windows are rearranged by waiting to resolve the conflict, thereby successfully receiving the task information. The time windows are updated after each arrival at the station. If the conflict type is a directional conflict, a avoidance point is found according to the breadth search algorithm so that the AGV temporarily calculates the route to this site for avoidance. If no avoidance point is found, this task is canceled and the AGV returns to the parking point along the original path. The steps of the breadth search algorithm are as follows: Step 1, initialize the set S and the set U; Step 2, put the current node into the set S as a point that has been searched; Step 3, search the adjacent nodes of the current point in sequence. If the adjacent node is within the associated path range of the current site of the conflicting AGV, the adjacent node is regarded as an obstacle point and added to the U set, and the node that can only be reached by the AGV is no longer explored; if the adjacent node is not within the associated path range of the current site of the conflicting AGV, the adjacent node is added to the set S; Step 4, if the adjacent node is no longer in the conflicting A If the remaining path of the GV is within the associated path range, a site that can be avoided is found, and the breadth search algorithm ends the search; Step 5, if the adjacent node is still within the associated path range of the remaining path of the conflicting AGV, continue to explore the next adjacent node of the current node. If the adjacent nodes are explored, select one adjacent node from these adjacent nodes as the next node, and repeat this cycle until the avoidance point is found; the associated path refers to treating the AGV as a particle. The collision between AGVs can only occur on the same route. Considering that the AGV has its own volume, if the AGV collides with AGVs on other routes during transportation on the route, the route where the AGV currently collides with AGVs on other routes is called the associated path of the route where the current AGV is located, and the route is given the same attributes as the route where the current AGV is located.

2. The method according to claim 1, characterized in that Step 1: The dispatching system generates a vehicle profile curve of the vehicle moving at a set moving speed on the path according to the vehicle size and path map.

3. The method according to claim 1, characterized in that Step 1: Use the topology method to construct a bidirectional single-path AGV environment electronic map: Set the corresponding node according to the stop point where the AGV needs to stop, and use the coordinates (id, x, y) to represent it, where id represents the node number and (x, y) represents the time required for the AGV to reach the node's horizontal and vertical coordinates from the origin; Set up the intermediate nodes for the AGV to go back and forth between the work points; after the nodes are set up, connect the adjacent nodes with edges to form a mesh structure diagram.

4. The method according to claim 1, characterized in that: The paths along which the AGVs travel are all bidirectional multi-AGV travel channels.

5. The method according to claim 1, characterized in that The ground on which the AGV travels is provided with track marks that are different from the ground color. The AGV acquires ground images during travel and identifies the track marks on the ground through visual processing.

6. The method according to claim 1, characterized in that Step 4 allocates time windows to AGVs, including: After AGV performs path planning, it calculates the time information of the planned route. The time window of the path is , that is, the AGV occupies the path from Time starts to The time ends, if The point where the AGV is located at the moment is the starting point of the route, then is the zero moment; if At this moment, the point where the AGV is located is not the starting point of the route, then is the previous path segment , the calculation method is as follows: in, represents the length of the path between adjacent nodes, Indicates the speed of the AGV; The collection of AGVs is , Representative There are AGVs, and the path set of AGV is , the set of sites in the path is , No. The path of the AGV is , among which The path information of the segment is , and For the The two endpoints of a segment's path.

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