Path planning system and method based on AGV variable-speed driving strategy

Abstract

The invention relates to the technical field of path planning, in particular to a path planning system and method based on an AGV variable-speed driving strategy, and the method comprises the steps: obtaining workshop comprehensive layout information, extracting nodes and paths, dividing the node types and path attributes of the nodes and the paths, and building a workshop electronic map, the path types comprise a low-speed path, a medium-speed path and a high-speed path, and path speeds corresponding to different path types are also set in the workshop electronic map; the method comprises the following steps: acquiring a carrying task of the AGV, calculating a current load coefficient KL of the AGV, solving an optimal path of the AGV, and searching a preset path speed and multiplying the current load coefficient KL of the AGV to obtain the driving speed of the AGV in each path. According to benefit optimization brought by AGV variable-speed driving, the optimal path planned based on the path environment and the load weight is safer and more efficient in combination with consideration of the actual path and the load condition.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a path planning system and method based on an AGV speed-changing driving strategy. Background Art

[0002] With the rapid development of the logistics industry, AGV systems have been widely used in warehouses, factories and other places due to their high efficiency and flexibility. Path planning, as one of the core technologies of the AGV system, is responsible for planning the optimal driving path based on the AGV's current position, target position, operating environment and task requirements.

[0003] At present, most of the path planning methods are based on the uniform speed of the Automated Guided Vehicle (AGV), and the optimal path is found through path optimization algorithms such as A-star and Djkstra, and the selection index is generally the shortest distance or the shortest time. However, this type of path planning method only regards the AGV driving process as a uniform speed, which is not in line with reality and ignores the benefit optimization that can be brought by the variable speed driving of AGV. Summary of the invention

[0004] The purpose of the present invention is to provide a path planning method based on the AGV speed-changing driving strategy. In view of the shortcomings of the prior art, according to the benefit optimization brought by the AGV speed-changing driving, combined with the consideration of the actual path and load conditions, the optimal path planned based on the path environment and load weight is safer and more efficient.

[0005] A path planning method based on an AGV variable speed driving strategy comprises the following steps:

[0006] Step 1: Obtain comprehensive workshop layout information, extract nodes and paths, and classify their node types and path attributes to establish an electronic map of the workshop, where node types include common nodes and complex nodes, and path types include low-speed paths, medium-speed paths, and high-speed paths. The electronic map of the workshop also sets path speeds corresponding to different path types;

[0007] Step 2: According to the workshop map obtained in step 1, obtain undirected map data G = (N, E), where N represents the set of all nodes in the workshop, and E represents the set of all feasible paths between nodes;

[0008] Step 3: Get the AGV's handling task and calculate the AGV's current load factor K L as follows:

[0009]

[0010] Among them, W C is the load weight of the previous task, WE is the maximum load capacity of AGV, b is the performance coefficient of AGV, 0<b≤1;

[0011] Step 4: Take the walking time of AGV on the path as the path weight and use Dijkstra algorithm to solve the optimal path of AGV;

[0012] According to the optimal path of the AGV, find the path speed of the pre-set path to be walked multiplied by the current load factor K of the AGV L Get the driving speed of AGV on each path.

[0013] Furthermore, step 1 includes:

[0014] Step 1.1: Extract the loading and unloading stations of warehouses and workstations, AGV charging stations, AGV parking stations, and path intersections as complex nodes, define the nodes for auxiliary driving on the remaining paths as ordinary nodes, and extract the roads between two passable nodes as paths;

[0015] Step 1.2: Define the path with a complex node at either end as a low-speed path, and divide the path with ordinary nodes at both ends into medium-speed paths and high-speed paths according to the size of the flow of people. Define the path with a flow of people greater than the threshold as a medium-speed path, and define the path with a flow of people less than the threshold as a high-speed path. The formula for calculating the flow of people on the path is as follows:

[0016]

[0017] Where D represents the flow of people on the path, m represents the flow correction coefficient, 0<m≤1, T represents the average time each person stays on the path per hour (in seconds), N represents the average number of people passing through the path per hour, and L represents the length of the path in meters;

[0018] If D≥1, the path is a medium-speed path; if D<1, the path is a high-speed path;

[0019] Step 1.3: Calculate the path speeds corresponding to the low-speed path, medium-speed path, and high-speed path:

[0020] Define the low speed path speed V L It is the maximum speed actually measured on the workshop positioning path when the AGV is running without load and can ensure precise positioning with the QR code; the medium-speed path speed is calculated based on the walking speed of the person, the acceleration of the AGV, and the safe distance between the person and the vehicle, and is generated by the following formula:

[0021]

[0022] Among them, V M Indicates the medium-speed path speed, V Prepresents the walking speed of a person, which is 1.2 m / s for adults, a represents the acceleration of the AGV, and D S Indicates the safe distance between people and vehicles, with a value range of 0<D S ≤AGV body length;

[0023] Define the high-speed path speed V H It is the maximum speed within the AGV performance range that is actually measured on the workshop high-speed path when the AGV runs empty through a node and can ensure normal communication and navigation.

[0024] Furthermore, the path weight calculation formula is:

[0025]

[0026] Among them, W(N i ,N j ) represents node N i and N j The path composed of E ij The weight of (X i ,Y i ),(X j ,Y j ) represents node N i and N i Coordinates of V ij is the node N calculated in step 1 i and N j The speed of the path type to which the path belongs, K L is the AGV load factor calculated in step 3;

[0027] Furthermore, the steps of using Dijkstra algorithm to solve the optimal path include:

[0028] Step 4.2.1: Initial set S and set U, set S only contains the starting node N S , let S = {N S}, the set U contains all the S Other nodes except U, that is, U = {other nodes};

[0029] Step 4.2.2: If N S The nodes adjacent to the vertex u in U are selected so that W(N S ,u) the smallest node N K , put N K Add to the set S, if u is not N S The adjacent nodes of W(N S ,u) is infinite;

[0030] Step 4.2.3: Take N K is the new midpoint. If it passes through node NK The weight of node u in the set is less than that of node N. K If is small, then update the weight of u in the U set, otherwise do nothing:

[0031] Step 4.2.4: Repeat steps 4.2.3 and 4.2.4 until the task ends N E Has been added to S, deriving the optimal path node sequence [N S ,…,N K ,…,N E ].

[0032] Furthermore, the process of obtaining the driving speed of the AGV on each path includes:

[0033] According to the optimal path node sequence, find the speed information of the path to be walked and derive the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ], where V S,S+1 Indicates the starting point of the task N S To the optimal path node sequence N S Next node N S+1 The AGV travel speed of the path, V K,K+1 Represents node N in the optimal path node sequence K With N K Next node N K+1 The AGV travel speed of the path, V E-1,E Indicates the task end point N E and the optimal path node sequence N E Previous node N E-1 The AGV driving speed of the path composed of the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ] multiplied by the current AGV load factor K to obtain the final AGV driving speed sequence [V' S,S+1 ,…,V' K,K+1 ,…,V' E-1,E ].

[0034] The present invention also discloses a path planning system based on an AGV speed-changing driving strategy, including an AGV hardware system, a map management system, a communication system and an AGV scheduling system;

[0035] The AGV hardware system includes the AGV body, AGV navigation system, cargo weighing system and AGV onboard control system; the AGV navigation system is used to realize AGV automatic guidance;

[0036] The cargo weighing system is used to obtain the weight of the cargo currently loaded on the AGV;

[0037] The AGV vehicle control system is used to control the navigation, walking, loading and unloading operations of the AGV.

[0038] The map management system is used to store workshop map information;

[0039] The communication system is used for information transmission between the AGV dispatching system and the AGV;

[0040] The AGV scheduling system includes an initial module, an input module, a coefficient calculation module, an algorithm module, and an output module. The initial module is used to initialize the map; the input module is used to receive AGV transportation tasks; the coefficient calculation module is used to calculate the parameters required in the algorithm module according to the aforementioned method; the algorithm module is used to solve the optimal path from the task start point to the task end point and the AGV driving speed corresponding to each path according to the aforementioned method; the output module records the results solved by the algorithm module.

[0041] Furthermore, the navigation method includes a laser navigation method or a QR code navigation method;

[0042] Furthermore, the workshop map information includes node information and path information;

[0043] The node information includes node number, node x-coordinate, node y-coordinate and node type;

[0044] The path information includes a path index, a path start point, a path end point, a path type, a path length, and a path speed.

[0045] Furthermore, the communication mode of the communication system is a communication screen or WiFi.

[0046] Furthermore, the AGV hardware system also includes an AGV obstacle avoidance system, which is used for the AGV to perform parking operations within a set obstacle avoidance distance.

[0047] The beneficial effects of the present invention are:

[0048] (1) The present invention proposes an AGV driving speed optimization strategy. Different AGV driving speeds are set according to the different needs and characteristics of each road, maximizing the AGV transportation efficiency while ensuring accurate positioning and safe driving of the AGV. At the same time, the load factor is calculated according to the AGV load condition to ensure the safe delivery of parts when the load is large.

[0049] (2) The AGV speed optimization strategy is integrated into the Dijkstra path planning algorithm. The path weight is the estimated driving time of the AGV on the path. The calculation formula takes into account the path length, the AGV driving speed on the path, and the AGV load factor. The path planning algorithm combines the actual path and load conditions, and the optimal path planned is safer and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic structural diagram of a path planning system based on an AGV speed-changing driving strategy provided in an embodiment of the present invention.

[0051] Figure 2 A workshop topology map is provided in an embodiment of the present invention.

[0052] Figure 3 The figure is a flow chart of a path planning method based on an AGV speed-changing driving strategy provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] like Figure 1 As shown, the path planning system for AGV speed-changing driving strategy based on path environment and load weight provided by an embodiment of the present invention includes an AGV hardware system, a map management system, a communication system and AGV scheduling software.

[0054] The AGV hardware system includes the AGV body, AGV navigation system, AGV obstacle avoidance system, cargo weighing system and AGV on-board control system.

[0055] The AGV navigation system is used to realize AGV automatic guidance. The navigation methods include laser navigation and QR code navigation. Laser navigation is used for AGV to travel on the predetermined path, and reflective columns need to be placed at certain intervals in the workshop. QR code navigation is used to realize the precise positioning of AGV in special scenarios. The QR code needs to be fixed in the area where precise positioning is required, such as loading and unloading stations in warehouses and workstations, AGV charging stations, AGV parking spaces, and path intersections.

[0056] The AGV obstacle avoidance system is used to enable the AGV to park within the set obstacle avoidance distance.

[0057] The cargo weighing system is used to obtain the weight of the cargo currently loaded on the AGV.

[0058] The AGV on-board control system is used to control the navigation, travel, loading and unloading operations of a single AGV.

[0059] The map management system is used to store workshop map information, including node information and path information as shown in Table 1:

[0060] Table 1 Node information and path information table

[0061]

[0062] The communication system is responsible for information transmission between the path planning software and the AGV. The communication method can be a communication screen or WiFi.

[0063] The AGV scheduling software includes an initial module, an input module, a coefficient calculation module, an algorithm module, and an output module.

[0064] The initial module is used to initialize the map;

[0065] The input module is used to receive AGV transportation tasks;

[0066] The coefficient calculation module is used to calculate the parameters required in the algorithm module;

[0067] The algorithm module is used to solve the optimal path from the task start point to the task end point and the AGV driving speed corresponding to each path; the output module records the results solved by the algorithm module.

[0068] like Figure 2 As shown, the path planning method implemented based on the above system in this example includes the following steps:

[0069] Step 1: Extract the comprehensive layout information of the workshop, extract the nodes and paths and classify them into their types, establish an electronic map of the workshop, and store it in the map management system;

[0070] Step 1.1: Extract the loading and unloading stations, AGV charging stations, AGV parking stations, and path intersections of warehouses and workstations as complex nodes, define the nodes for auxiliary driving on the remaining paths as ordinary nodes, and extract the roads between two passable nodes as paths. Establish a topological map such as Figure 3 The workshop topology map consists of nodes and edges. The solid dots such as 1 are ordinary nodes, and the hollow dots such as 2 are complex nodes.

[0071] Step 1.2: Define the path with a complex node at either end as a low-speed path, and the path with ordinary nodes at both ends as a medium-speed path and a high-speed path according to the flow of people. Define the path with a flow of people greater than the threshold as a medium-speed path, and define the path with a flow of people less than the threshold as a high-speed path. The formula for calculating the flow of people on a path is as follows:

[0072]

[0073] Wherein, D represents the flow of people on the path, m represents the flow correction coefficient, 0<m≤1, T represents the average time per person staying on the path per hour, in seconds; N represents the average number of people passing through the path per hour, L represents the length of the path, in meters; in this embodiment, m=0.5, if D≥1, the path is a medium-speed path; if D<1, the path is a high-speed path. According to the calculation, the path allocation in this embodiment is shown in Table 2:

[0074] Table 2 Path type allocation table

[0075]

[0076] Step 1.3: Calculate the path speeds corresponding to the low-speed path, medium-speed path, and high-speed path. Define the low-speed path speed V L is the maximum speed actually measured on the workshop positioning path when the AGV is running without load and can ensure the completion of precise positioning with the QR code. In this embodiment, V L =0.1m / s; The medium-speed path speed is calculated based on the walking speed of the person, the acceleration of the AGV, and the safe distance between the person and the vehicle, and is generated by the following formula:

[0077]

[0078] Among them, V M Indicates the medium-speed path speed, V P represents the walking speed of a person, which is 1.2 m / s for adults, a represents the acceleration of the AGV, and D S Indicates the safe distance between people and vehicles, with a value range of 0<D S ≤AGV body length. In this embodiment, a=1m / s 2 ,D S =0.5m, calculated V S =0.36m / s.

[0079] Define the high-speed path speed V H is the maximum speed within the AGV performance range that can ensure normal communication and navigation when the AGV passes through the node without load, which is actually measured on the workshop high-speed path. In this embodiment, V H =0.5m / s.

[0080] Step 1.4: Store the node information and path information obtained in steps 1.1-1.3 in the map management system in XML format.

[0081] Step 2: AGV dispatch software initializes the map. Read the map file obtained in step 1 to obtain the undirected map data

[0082] G=(N,E), where N represents the set of all nodes in the workshop, and E represents the set of all feasible paths between nodes.

[0083] Step 3: The AGV scheduling software receives the AGV handling task and calculates the current load factor of the AGV.

[0084] Step 3.1: In this embodiment, the task start point and task end point [a6, a16] of the transport task are obtained.

[0085] Step 3.2: Calculate the load factor, generated by the following formula:

[0086]

[0087] Among them, WC is the load weight of the previous task, W E is the maximum load capacity of the AGV, b is the performance coefficient of the AGV, 0<b≤1. In this embodiment, the current task load weight W C =1kg, AGV maximum load capacity W E =10kg, b=1, calculate K L =0.9.

[0088] Step 4: Solve the optimal path of the AGV and the driving speed of the AGV on each path.

[0089] Step 4.1: Calculate the path weight. Use the walking time of the AGV on the path as the path weight. The path weight calculation formula is:

[0090]

[0091] Among them, W(N i ,N j ) represents node N i and N j The path composed of E ij The weight of (X i ,Y i ),(X j ,Y j ) represents node N i and N i Coordinates of V ij is the node N calculated in step 1 i and N j The speed of the path type to which the path belongs, K L is the AGV load factor calculated in step 3.

[0092] Step 4.2: Use Dijkstra algorithm to solve the optimal path. In this embodiment, the starting point and end point of the transport task are obtained as [a6, a16].

[0093] Step 4.2.1: Initial set S and set U, set S only contains the starting node a6, let S = {a6}, set U contains other nodes except a6, that is, U = {other nodes};

[0094] Step 4.2.2: If a6 is adjacent to vertex u in U, select a node that makes W(N S ,u) the smallest node N K , put N K Add to the set S, if u is not N S The adjacent nodes of W(N S ,u) is infinite;

[0095] Step 4.2.3: Take NK is the new midpoint. If it passes through node N K The weight of node u in the set is less than that of node N. K If is small, then update the weight of u in the U set, otherwise do nothing:

[0096] Step 4.2.4: Repeat steps 4.2.3 and 4.2.4 until the task endpoint a16 has been added to S and the optimal path node sequence [N S ,…,N K ,…,N E ].

[0097] In this embodiment, the optimal path node sequence [a6, a2, b2, b1, a1, b9, b21, b13, b49, b22, a14, a16] is derived.

[0098] Step 4.3: Find the speed information of the corresponding walking path in the map file according to the optimal path node sequence, and derive the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ], where V S,S+1 Indicates the starting point of the task N S To the optimal path node sequence N S Next node N S+1 The AGV travel speed of the path, V K,K+1 Represents node N in the optimal path node sequence K With N K Next node N K+1 The AGV travel speed of the path, V E-1,E Indicates the task end point N E and the optimal path node sequence N E Previous node N E-1 The AGV driving speed of the path composed of the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ] multiplied by the current load factor of the AGV to obtain the final AGV driving speed sequence [V' S,S+1 ,…,V' K,K+1 ,…,V' E-1,E In this embodiment, the optimal path results are shown in Table 3, and the final AGV driving speed sequence is derived as [0.09, 0.09, 0.32 0.09, 0.09, 0.45, 0.09, 0.09, 0.32, 0.09, 0.09].

[0099] Table 2 Optimal path results

[0100] Serial number path Path Type Path speed (m / s) Load Factor Final AGV driving speed (m / s) 1 a6-a2 Low speed path 0.1 0.9 0.09 2 a2-b2 Low speed path 0.1 0.9 0.09 3 b2-b1 Medium speed path 0.36 0.9 0.32 4 b1-a1 Low speed path 0.1 0.9 0.09 5 a1-b9 Low speed path 0.1 0.9 0.09 6 b9-b21 High-speed path 0.5 0.9 0.45 7 b21-a13 Low speed path 0.1 0.9 0.09 8 a13-b49 Low speed path 0.1 0.9 0.09 9 b49-b22 Medium speed path 0.36 0.9 0.32 10 b22-a14 Low speed path 0.1 0.9 0.09 11 a14-a16 Low speed path 0.1 0.9 0.09

[0101] Step 5: The communication system transmits the optimal path node sequence and AGV driving speed sequence obtained in step 4 to the AGV.

[0102] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by the present invention shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A path planning method based on AGV speed-changing driving strategy, characterized in that: The following steps are involved: Step 1: Obtain comprehensive workshop layout information, extract nodes and paths, and classify their node types and path attributes to establish an electronic map of the workshop, where node types include common nodes and complex nodes, and path types include low-speed paths, medium-speed paths, and high-speed paths. The electronic map of the workshop also sets path speeds corresponding to different path types; Step 2: According to the workshop map obtained in step 1, obtain undirected map data G = (N, E), where N represents the set of all nodes in the workshop, and E represents the set of all feasible paths between nodes; Step 3: Get the AGV's handling task and calculate the AGV's current load factor K L as follows: Among them, W C is the current task load weight, W E is the maximum load capacity of AGV, b is the performance coefficient of AGV, 0<b≤1; Step 4: Take the walking time of AGV on the path as the path weight and use Dijkstra algorithm to solve the optimal path of AGV; According to the optimal path of the AGV, find the path speed of the pre-set path to be walked multiplied by the current load factor K of the AGV L Get the driving speed of AGV on each path.

2. The method according to claim 1, characterized in that Step 1 includes: Step 1.1: Extract the loading and unloading stations of warehouses and workstations, AGV charging stations, AGV parking stations, and path intersections as complex nodes, define the nodes for auxiliary driving on the remaining paths as ordinary nodes, and extract the roads between two passable nodes as paths; Step 1.2: Define the path with a complex node at either end as a low-speed path, and divide the path with ordinary nodes at both ends into medium-speed paths and high-speed paths according to the size of the flow of people. Define the path with a flow of people greater than the threshold as a medium-speed path, and define the path with a flow of people less than the threshold as a high-speed path. The formula for calculating the flow of people on the path is as follows: Where D represents the flow of people on the path, m represents the flow correction coefficient, 0<m≤1, T represents the average time each person stays on the path per hour (in seconds), N represents the average number of people passing through the path per hour, and L represents the length of the path in meters; If D≥1, the path is a medium-speed path; if D<1, the path is a high-speed path; Step 1.3: Calculate the path speeds corresponding to the low-speed path, medium-speed path, and high-speed path: Define the low speed path speed V L It is the maximum speed actually measured on the workshop positioning path when the AGV is running without load and can ensure precise positioning with the QR code; the medium-speed path speed is calculated based on the walking speed of the person, the acceleration of the AGV, and the safe distance between the person and the vehicle, and is generated by the following formula: Among them, V M Indicates the medium-speed path speed, V P represents the walking speed of a person, which is 1.2 m / s for adults, a represents the acceleration of the AGV, and D S Indicates the safe distance between people and vehicles, with a value range of 0<D S ≤AGV body length; Define the high-speed path speed V H It is the maximum speed within the AGV performance range that is actually measured on the workshop high-speed path when the AGV runs empty through a node and can ensure normal communication and navigation.

3. The method according to claim 1, characterized in that The formula for calculating path weight is: Among them, W(N i ,N j ) represents node N i and N j The path composed of E ij The weight of (X i ,Y i ),(X j ,Y j ) represents node N i and N i Coordinates of V ij is the node N set in step 1 i and N j The speed of the path type to which the path belongs, K L is the AGV load factor calculated in step 3; 4. The method according to claim 3, characterized in that The steps to solve the optimal path using Dijkstra's algorithm include: Step 4.2.1: Initial set S and set U, set S only contains the starting node N S , let S = {N S }, the set U contains all the S Other nodes except U, that is, U = {other nodes}; Step 4.2.2: If N S The nodes adjacent to the vertex u in U are selected so that W(N S ,u) the smallest node N K , put N K Add to the set S, if u is not N S The adjacent nodes of W(N S ,u) is infinite; Step 4.2.3: Take N K is the new midpoint. If it passes through node N K The weight of node u in the set is less than that of node N. K If is small, then update the weight of u in the U set, otherwise do nothing: Step 4.2.4: Repeat steps 4.2.3 and 4.2.4 until the task ends N E Has been added to S, and the optimal path node sequence [N S ,…,N K ,…,N E ].

5. The method according to claim 1, characterized in that The process of obtaining the driving speed of the AGV on each path includes: According to the optimal path node sequence, find the speed information of the path to be walked and derive the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ], where V S,S+1 Indicates the starting point of the task N S To the optimal path node sequence N S Next node N S+1 The AGV travel speed of the path, V K,K+1 Represents node N in the optimal path node sequence K With N K Next node N K+1 The AGV travel speed of the path, V E-1,E Indicates the task end point N E and the optimal path node sequence N E Previous node N E-1 The AGV driving speed of the path composed of the original path speed sequence [V S,S+1 ,…,V K,K+1 ,…,V E-1,E ] multiplied by the current AGV load factor K to obtain the final AGV driving speed sequence [V' S,S+1 ,…,V' K,K+1 ,…,V' E-1,E ].

6. A path planning system based on AGV variable speed driving strategy, characterized in that: Including AGV hardware system, map management system, communication system and AGV dispatching system; The AGV hardware system includes the AGV body, AGV navigation system, cargo weighing system and AGV onboard control system; AGV navigation system is used to realize AGV automatic guidance; The cargo weighing system is used to obtain the weight of the cargo currently loaded on the AGV; The AGV vehicle control system is used to control the navigation, walking, loading and unloading operations of the AGV. The map management system is used to store workshop map information; The communication system is used for information transmission between the AGV dispatching system and the AGV; The AGV scheduling system includes an initial module, an input module, a coefficient calculation module, an algorithm module, and an output module. The initial module is used to initialize the map; the input module is used to receive AGV transportation tasks; the coefficient calculation module is used to calculate the parameters required in the algorithm module according to any method described in claims 1-5; the algorithm module is used to solve the optimal path from the task start point to the task end point and the AGV driving speed corresponding to each path according to the method described in claims 1-5; the output module records the results solved by the algorithm module.

7. The system according to claim 6, characterized in that The navigation methods of the AGV navigation system include laser navigation or QR code navigation.

8. The system according to claim 6, characterized in that The workshop map information includes node information and path information; The node information includes node number, node x-coordinate, node y-coordinate and node type; The path information includes a path index, a path start point, a path end point, a path type, a path length, and a path speed.

9. The system according to claim 6, characterized in that The communication mode of the communication system is a communication screen or WiFi.

10. The system according to claim 6, characterized in that The AGV hardware system also includes an AGV obstacle avoidance system, which is used for the AGV to perform parking operations within a set obstacle avoidance distance.

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