AGV operation library AGV operation path planning and obstacle avoidance method and system

Abstract

The application relates to an AGV running path planning and obstacle avoidance method and system. The on-site environment of a motor train transport depot is complex, which leads to difficulties in AGV navigation and obstacle avoidance. The method scans a motor train transport depot channel through a laser radar to obtain a three-dimensional model of the motor train transport depot channel; a virtual navigation boundary of the AGV is set in the three-dimensional model; an initial running path of the AGV is planned in the virtual navigation boundary; the in-and-out depot time of vehicles on each track is obtained from a vehicle scheduling system, the time period of the AGV running path passing through the track is predicted, if there is overlap, the path waiting time of the AGV is set; and the final running path is determined. The application provides a precise depot environment map for AGV running through laser radar scanning modeling, so that the path planning is more accurate; the system calculation amount is reduced through virtual route planning; and the safety risk of AGV collision with in-and-out depot vehicles is avoided through communication with the depot vehicle scheduling system.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance technology in EMU depots, specifically to a method and system for AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance in EMU depots. Background Technology

[0002] Currently, the distribution of tools and materials in high-speed train depots is mainly done manually, which presents several problems. First, manual operation is limited by the number and skill level of personnel, making it unable to meet the demands of large-scale, high-frequency distribution. Second, manual operation is prone to fatigue and errors, leading to low efficiency and mistakes. Third, manual operation poses certain safety hazards, such as injuries and damage to goods.

[0003] Despite the aforementioned problems with manual delivery of tools and materials, manual retrieval and delivery remain the primary methods of distribution during EMU maintenance. With the increasing workload of EMU maintenance, existing manual delivery methods are no longer sufficient to meet the growing demands of maintenance tasks, necessitating automated delivery to improve the efficiency of tool and material distribution. AGVs (Automated Guided Vehicles) can automatically travel along pre-set routes or tow cargo trolleys to designated locations, enabling automated delivery of maintenance tools and materials. However, on-site investigations revealed that the complex environment of EMU transport depots makes AGV navigation and obstacle avoidance difficult, thus limiting their application in EMU depots.

[0004] Therefore, it is necessary to propose new methods to achieve autonomous navigation and obstacle avoidance of AGVs in EMU maintenance depots. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for AGV operation path planning and obstacle avoidance in EMU depots, so as to solve the problem of AGV navigation and obstacle avoidance difficulties caused by the complex on-site environment of EMU depots.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance in a high-speed train depot, the method comprising:

[0008] A three-dimensional model of the EMU depot passage was obtained by scanning the passage with lidar.

[0009] Set the virtual navigation boundaries of the AGV in the 3D model;

[0010] Plan the initial running path for the AGV within the virtual navigation boundary;

[0011] The system obtains the entry and exit times of vehicles on each track from the vehicle dispatching system, predicts the time period that the AGV will pass through the track on its running path, and sets a path waiting time for the AGV if there is an overlap.

[0012] Determine the final running path.

[0013] Furthermore, the virtual navigation boundaries of the AGV are set in the 3D model, including:

[0014] Set the AGV's travel route in the 3D model;

[0015] Using the walking route as the center line, set up symmetrical virtual walking boundary lines on both sides as virtual navigation boundaries.

[0016] Furthermore, within the virtual navigation boundary, an initial running path is planned for the AGV, including:

[0017] Receive pickup point location information and delivery point location information for delivery tasks;

[0018] Based on the pickup point location information and the delivery point location information, plan the first initial running path of the AGV from the pickup point location to the delivery point location;

[0019] Obtain the current location information of all AGVs and plan the second initial running path for all AGVs from their current locations to the pickup point;

[0020] The path with the shortest total time between the first and second initial running paths is the initial running path for the corresponding AGV.

[0021] Furthermore, based on the pickup point location information and the delivery point location information, the first initial running path of the AGV from the pickup point location to the delivery point location is planned, including:

[0022] Based on the location information of the pickup point and the delivery point, plan N feasible operating routes within the EMU depot;

[0023] Based on the AGV's operating speed, determine the running time t1-tN for N running paths;

[0024] The running path with the shortest running time is selected as the first initial running path, and the corresponding running time is t'.

[0025] Furthermore, obtain the current location information of all AGVs, and plan a second initial running path for all AGVs from their current locations to the pickup point, including:

[0026] Based on the current location information of n AGVs, plan the running path for the n AGVs from their current location to the pickup point within the EMU depot;

[0027] Based on the AGV's operating speed, calculate the travel time T1-Tn for n AGVs to travel from their current location to the pickup point.

[0028] The running path with the shortest running time is selected as the second initial running path, and the corresponding running time is T'.

[0029] Furthermore, the entry and exit times of vehicles on each track are obtained from the vehicle scheduling system to predict the time periods during which the AGV will pass through the tracks. If there is overlap, a path waiting time is set for the AGV, including:

[0030] Obtain the entry and exit times of vehicles on each track from the vehicle dispatching system and mark them on the timeline;

[0031] Predict the time period of the AGV's initial running path on each track on the time axis;

[0032] Compare the timelines of vehicle entry and exit from the warehouse with the timelines of delivery AGVs on each track to see if there is any overlap.

[0033] If so, set a path waiting time T for the delivery AGV.

[0034] Further, the final execution path is determined, including:

[0035] Obtain the status information of all AGVs, which are either resting or delivering.

[0036] Calculate the delivery time T of the AGV during rest periods. 休 ,T=t'+T'+T”;

[0037] Get the remaining delivery time T for the AGV to complete the current delivery task. 余 Calculate the delivery time T of the AGV during delivery. 配 T = t' + T' + T” + T 余 ;

[0038] Comparison T 休 and T 配 The AGV with the shortest running time is selected as the AGV for this delivery task, and the initial running path is updated with the corresponding running path as the final running path.

[0039] Furthermore, the method also includes:

[0040] Feature points are pre-set inside the EMU depot, including travel feature points, turning feature points, and maintenance work station feature points;

[0041] During operation, the AGV determines its current position by identifying feature points.

[0042] Furthermore, the method also includes:

[0043] During AGV operation, if an obstacle is detected within the virtual navigation boundary, the remaining running path within the virtual navigation boundary is calculated to determine whether passage is possible.

[0044] If the passage is permitted, the AGV will automatically plan its route to avoid obstacles; if the passage is not permitted, the AGV will stop and sound an alarm.

[0045] On the other hand, a system for planning the operating path and avoiding obstacles for AGVs in a high-speed train depot is provided. This system is used to implement the method, including:

[0046] The model building module is used to obtain a three-dimensional model of the EMU depot channel by scanning the EMU depot channel with LiDAR;

[0047] The boundary setting module is used to set the virtual navigation boundaries of the AGV in the 3D model;

[0048] The initial running path planning module is used to plan the initial running path for the AGV within the virtual navigation boundary;

[0049] The path waiting time setting module is used to obtain the entry and exit times of vehicles on each track from the vehicle scheduling system, predict the time period of the AGV running path passing through the track, and set the path waiting time for the AGV if there is an overlap.

[0050] The final execution path module is used to determine the final execution path.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention provides a method and system for AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance in a high-speed train depot. By using LiDAR scanning and modeling, a precise depot environment map is provided for AGV operation, resulting in more accurate path planning and reduced AGV delivery delays. Virtual route planning reduces system computation and lowers hardware requirements. Communication with the depot's vehicle scheduling system avoids the safety risk of AGV collisions with vehicles entering and leaving the depot. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0054] Figure 1 This is a flowchart of the method of the present invention.

[0055] Figure 2 This is a system composition diagram of the present invention.

[0056] Figure 3 This is a schematic diagram of AGVs in the EMU transport depot. Detailed Implementation

[0057] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0058] It should be noted that similar reference numerals and letters indicate similar items; therefore, once an item is defined in one embodiment, it does not need to be further defined and explained in subsequent embodiments. Furthermore, the terms "comprising" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] Furthermore, in the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Of course, such terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than those illustrated or described herein.

[0060] It should also be noted that although the order of steps is mentioned in the method description, in some cases, steps may be performed in a different order than that described here, and this should not be interpreted as a restriction on the order of steps.

[0061] This invention provides a method for AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance in a high-speed train depot. By setting virtual boundaries for the operation route in the scene map, a minimum path range for AGV movement is obtained. By establishing data communication with the vehicle scheduling system in the depot, the accurate time periods for vehicle entry and exit are obtained, effectively improving AGV delivery efficiency.

[0062] like Figure 1 The method includes:

[0063] S1: Obtain a three-dimensional model of the EMU depot channel by scanning the channel with lidar.

[0064] The AGV involved in this method has its own onboard LiDAR, which can scan the scene inside the warehouse, build a 3D scene map, and obtain a 3D model.

[0065] S2: Set the virtual navigation boundaries of the AGV in the 3D model. This includes:

[0066] S201: Set the AGV's travel route in the 3D model.

[0067] S202: Using the walking route as the center line, set up virtual walking boundary lines on both sides as virtual navigation boundaries.

[0068] The size of the virtual navigation boundary is 1.5 times the width of the AGV, that is, if the vehicle width is 1m, the width of the virtual navigation boundary is 1.5m.

[0069] S3: Plan the initial running path for the AGV within the virtual navigation boundary. This includes:

[0070] S301: Receive pickup point location information and delivery point location information for the delivery task.

[0071] S302: Based on the pickup point location information and delivery point location information, plan the first initial running path for the AGV from the pickup point location to the delivery point location. This includes:

[0072] S30201: Based on the location information of the pickup point and the delivery point, plan N feasible running routes within the EMU depot;

[0073] S30202: Determine the running time t1-tN for N running paths based on the running speed of the AGV;

[0074] S30203: Select the running path with the shortest running time as the first initial running path, with a corresponding running time of t'.

[0075] S303: Obtain the current position information of all AGVs and plan the second initial running path for all AGVs from their current positions to the pickup point. This includes:

[0076] S30301: Based on the current location information of n AGVs, plan the running path for the n AGVs from their current location to the pickup point within the EMU depot;

[0077] S30302: Based on the AGV's running speed, calculate the running time T1-Tn for n AGVs to travel from their current location to the pickup point.

[0078] S30303: Select the running path with the shortest running time as the second initial running path, with a corresponding running time of T'.

[0079] S304: The path with the shortest total time between the first and second initial running paths is the initial running path of the corresponding AGV.

[0080] S4: Obtain the entry and exit times of vehicles on each track from the vehicle dispatching system, predict the time period of the AGV's running path through the track, and set the path waiting time for the AGV if there is an overlap.

[0081] As vehicles enter the various tracks, they may collide with AGVs during operation. This step helps prevent collisions, including:

[0082] S401: Obtain the entry and exit times of vehicles on each track from the vehicle dispatching system and mark them on the timeline.

[0083] S402: Predict the time period of the AGV's initial running path on each track on the time axis.

[0084] S403: Compare whether there is any overlap between the vehicle's entry and exit timeline and the delivery AGV's timeline on each track.

[0085] S404: If present, set the path waiting time T for the delivery AGV.

[0086] The AGV needs to wait before the timelines overlap and then run again after the overlap time.

[0087] S5: Determine the final execution path. This includes:

[0088] S501: Obtain the status information of all AGVs, which are either resting or delivering.

[0089] S502: Calculate the delivery time T of the AGV during rest. 休 , T = t' + T' + T".

[0090] S503: Obtain the remaining delivery time T for the AGV to complete the current delivery task. 余 Calculate the delivery time T of the AGV during delivery. 配 T = t' + T' + T” + T 余 .

[0091] S504: Compared to T 休 and T 配 The AGV with the shortest running time is selected as the AGV for this delivery task, and the initial running path is updated with the corresponding running path as the final running path.

[0092] The remaining AGVs either complete their existing tasks or continue to wait.

[0093] In addition, the method further includes the following steps:

[0094] Feature points are pre-set within the EMU depot, including travel feature points, turning feature points, and maintenance station feature points. During operation, the AGV determines its current position by identifying these feature points.

[0095] During AGV operation, if an obstacle is detected within the virtual navigation boundary, the system calculates whether the remaining path within the virtual navigation boundary is passable. If passable, the AGV automatically plans a path to avoid the obstacle; otherwise, the AGV stops and triggers an alarm.

[0096] This method uses an AGV-mounted LiDAR to scan the scene within the depot, creating a 3D scene map of the maintenance depot. Based on this map, initial path planning is performed. Simultaneously, a symmetrical virtual navigation boundary is established, using the original path as the center line. This boundary restricts the AGV's operating route to within the virtual boundary, and feature positioning points are selected along this virtual boundary, corresponding to the actual workshop scene. By setting this virtual boundary, the computational load is reduced, and the AGV's operational stability is improved. This method enables automated delivery within the complex environment of a high-speed train maintenance depot.

[0097] In practice:

[0098] Once the selected AGV receives a delivery instruction, it first scans the surrounding environment using LiDAR to create a map. By identifying feature points, the AGV's current position is determined, and then automatic path planning is performed. The AGV moves along the planned path, continuously scanning the boundaries of its operating area to ensure it stays within a reasonable range. It also scans and confirms feature points at preset intervals along the planned path. Because the AGV has already performed a comprehensive scan and feature point determination of the maintenance depot, its planned path is determined by these feature points, including travel feature points, turning feature points, and maintenance station feature points. The AGV moves along the planned path, specifically according to the selected travel, turning, and maintenance station feature points. These selected feature points are located within the maintenance depot and remain unchanged by default, ensuring the accuracy of the AGV's movement. This chosen solution eliminates the need for omnidirectional radar deployment in the maintenance depot, resulting in a smaller amount of data acquisition and processing.

[0099] This method communicates with the vehicle dispatching system of the maintenance depot (maintaining the departure and arrival of maintenance vehicles). The implementation of the method also requires the determination of track feature points, which are the feature points on both sides of the track. During the AGV transportation process, when a vehicle leaves or enters the depot, the AGV will determine its current position. When it is at one end of the track, it will determine whether it is the position of a vehicle leaving or entering. If not, it will continue to move. If so, it will determine the track feature points on the running path. When it moves to the position where a vehicle leaves or enters the depot, the AGV will stop moving until the vehicle completes the departure or arrival before starting the unfinished delivery task to prevent the AGV from colliding with the vehicle.

[0100] When the AGV detects an obstacle within the virtual boundary of its forward movement, it calculates whether the remaining width of the virtual channel is sufficient for passage. If it is sufficient, the AGV automatically plans a route to avoid the obstacle. If it is not sufficient, the AGV stops, and a voice prompt is given to remove the obstacle. At the same time, an alarm is triggered on the control system interface.

[0101] On the other hand, the present invention provides an AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance system for high-speed train depots, used to implement the above-mentioned method, including:

[0102] The model building module is used to scan the EMU depot channel with LiDAR to obtain a three-dimensional model of the EMU depot channel, corresponding to S1 of the above method;

[0103] The boundary setting module is used to set the virtual navigation boundary of the AGV in the 3D model, corresponding to S2 of the above method;

[0104] The initial running path planning module is used to plan the initial running path for the AGV within the virtual navigation boundary, corresponding to S3 of the above method;

[0105] The path waiting time setting module is used to obtain the entry and exit times of vehicles on each track from the vehicle scheduling system, predict the time period of the AGV running path passing through the track, and set the path waiting time for the AGV if there is an overlap, which corresponds to S4 of the above method.

[0106] The final execution path module is used to determine the final execution path, corresponding to S5 of the above method.

[0107] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for AGV (Automated Guided Vehicle) operation path planning and obstacle avoidance in a high-speed train depot, characterized by: The method includes: A three-dimensional model of the EMU depot passage was obtained by scanning the passage with lidar. Set the virtual navigation boundaries of the AGV in the 3D model; Plan the initial running path for the AGV within the virtual navigation boundary, including: Receive pickup point location information and delivery point location information for delivery tasks; Based on the location information of the pickup point and the delivery point, plan the first initial running path of the AGV from the pickup point to the delivery point, including: planning N feasible running paths within the EMU depot based on the location information of the pickup point and the delivery point; determining the running time t1-tN of the N running paths based on the running speed of the AGV; selecting the running path with the shortest running time as the first initial running path, with the corresponding running time being t'; Obtain the current location information of all AGVs, and plan the second initial running path for all AGVs from their current location to the pickup point. This includes: based on the current location information of n AGVs, planning the running path for n AGVs from their current location to the pickup point within the EMU depot; calculating the running time T1-Tn for the n AGVs from their current location to the pickup point based on their running speed; and selecting the running path with the shortest running time as the second initial running path, with the corresponding running time being T'. The path with the shortest total time between the first and second initial running paths is the initial running path for the corresponding AGV. The system obtains the entry and exit times of vehicles on each track from the vehicle dispatching system, predicts the time periods during which the AGV will traverse the tracks, and sets path waiting times for the AGV if there are overlaps. Obtain the entry and exit times of vehicles on each track from the vehicle dispatching system and mark them on the timeline; Predict the time period of the AGV's initial running path on each track on the time axis; Compare the timelines of vehicle entry and exit from the warehouse with the timelines of delivery AGVs on each track to see if there is any overlap. If so, set a path waiting time T” for the delivery AGV; Determine the final execution path, including: Obtain the status information of all AGVs, which are either resting or delivering. Calculating the delivery time T of the AGV in rest 休 , T 休 = t' + T' + T" Acquiring the remaining delivery time T of the AGV in delivery completing the current delivery task 余 , calculating the delivery time T of the AGV in delivery 配 , T 配 =t’+ T’ + T”+T 余 ; Compare T 休 and T 配 , select the AGV with the shortest time as the AGV for this distribution task, and update the initial running path with the corresponding running path as the final running path.

2. The method for AGV operation path planning and obstacle avoidance in a high-speed train depot according to claim 1, characterized in that: Setting the virtual navigation boundaries of the AGV in the 3D model includes: Set the AGV's travel route in the 3D model; Using the walking route as the center line, set up symmetrical virtual walking boundary lines on both sides as virtual navigation boundaries.

3. The method for AGV operation path planning and obstacle avoidance in a high-speed train depot according to claim 2, characterized in that: The method further includes: Feature points are pre-set inside the EMU depot, including travel feature points, turning feature points, and maintenance work station feature points; During operation, the AGV determines its current position by identifying feature points.

4. The method for AGV operation path planning and obstacle avoidance in a high-speed train depot according to claim 3, characterized in that: The method further includes: During AGV operation, if an obstacle is detected within the virtual navigation boundary, the remaining running path within the virtual navigation boundary is calculated to determine whether passage is possible. If the passage is permitted, the AGV will automatically plan its route to avoid obstacles; if the passage is not permitted, the AGV will stop and sound an alarm.

5. A path planning and obstacle avoidance system for AGVs in a high-speed train depot, characterized in that: The system is used to implement the method according to any one of claims 1-4, comprising: The model building module is used to obtain a three-dimensional model of the EMU depot channel by scanning the EMU depot channel with LiDAR; The boundary setting module is used to set the virtual navigation boundaries of the AGV in the 3D model; The initial running path planning module is used to plan the initial running path for the AGV within the virtual navigation boundary; The path waiting time setting module is used to obtain the entry and exit times of vehicles on each track from the vehicle scheduling system, predict the time period of the AGV running path passing through the track, and set the path waiting time for the AGV if there is an overlap. The final execution path module is used to determine the final execution path.

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