Automatic guiding carrier path planning method based on swarm intelligence

Abstract

The invention relates to the technical field of intelligent path planning, in particular to an automatic guided vehicle path planning method based on swarm intelligence. The method comprises the following steps: acquiring position information of multiple carriers based on swarm intelligence, identifying candidate paths and drivable space channels, judging a low-speed stable state in combination with a driving speed and a space crowding degree, screening stable channels, distributing residence time, and determining a stable state; and integrating the channel superior degree and the residence time to realize dynamic path conversion and optimal scheduling. According to the invention, through introducing swarm intelligence, multi-dimensional dynamic perception, low-speed stable state detection, stable space channel screening and a path conversion and short-time obstacle avoidance mechanism of smooth constraint, collaborative optimization of multi-carrier path planning is realized; the overall transportation efficiency and the operation reliability of the system are remarkably improved in the aspects of congestion management, channel safety, path switching smoothness and multi-vehicle ordered passing.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent path planning, and in particular to a path planning method for an automatic guided vehicle based on swarm intelligence. Background Art

[0002] Existing automated guided vehicle (AGV) path planning often relies on static maps or preset routes, requiring the vehicles to travel along fixed paths and lacking real-time adaptability to dynamic environments. When multiple vehicles share a shared channel, simple "first-come, first-served" or queue-based rules are often used to handle intersections and congestion. These methods fail to quantify channel congestion levels and fail to dynamically assess vehicle speed, spatial distribution, and real-time status. Furthermore, low-speed stable state recognition is insufficient, often relying solely on a single speed threshold. This can easily lead to misjudgments of congested areas, thus impacting dispatch efficiency. Path switching and short-term obstacle avoidance strategies often involve simple stops, waits, or detours, lacking smoothness constraints and dynamic optimization control. This results in frequent vehicle starts and stops, uneven paths, and potential compromises in cargo safety. Furthermore, channel superiority assessment lacks a comprehensive, multi-factor approach, often considering only a single metric, such as speed or distance. This results in a lack of scientific basis for path selection and can easily lead to bottleneck channel blockages. Summary of the Invention

[0003] Based on this, it is necessary to provide a path planning method for an automated guided vehicle based on swarm intelligence to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a swarm intelligence-based path planning method for an automated guided vehicle is provided, the method comprising the following steps: Step S1: Identify several candidate paths based on the position information between multiple automated guided vehicles and the target; and determine the spatial channel in which the vehicle can travel based on the center line and width of the candidate paths; Step S2: Acquire the driving speed of the AGV and the degree of congestion between the AGV and adjacent AGVs; detect whether the AGV is in a low-speed stable state based on the driving speed and congestion degree; Step S3: Filter the stable spatial channels in the spatial channels according to the low-speed stable state, and set the residence time based on the stable spatial channels; calculate the channel superiority of the stable spatial channels, and determine whether the transport vehicle needs to change the path based on the residence time.

[0005] Preferably, step S2 includes the following steps: Step S21: obtaining the driving speed of the AGV through the vehicle-mounted motion sensor; Step S22: activating the fleet location interconnection function on the automated guided vehicle to receive real-time coordinate information periodically broadcast by adjacent vehicles, wherein the real-time coordinate information includes the planar position of the vehicle in the work scene coordinate system; Step S23: For any one automated guided vehicle, calculate the straight-line distance between the two vehicles according to the position information and the received position information of the adjacent vehicle, and confirm the relative motion direction of the automated guided vehicle in combination with the trend of the planar position change; Step S24: According to the relative motion direction, confirm the space congestion degree of the automated guided vehicle and the adjacent vehicle in the same space channel; according to the driving speed and the congestion degree, detect whether the automated guided vehicle is in a low-speed stable state.

[0006] Preferably, step S24 comprises the following steps: Step S241: According to the relative motion direction, determine whether the automated guided vehicle and the adjacent vehicle are in the same space channel; Step S242: After determining that they are in the same space channel, according to the distance between the automated guided vehicle and the adjacent vehicle, the lateral gap, and the front and rear distribution, evaluate the space congestion degree of the channel; Step S243: Numerically quantify the space congestion degree of the channel based on a preset space congestion quantification table to obtain a space congestion degree coefficient; use the space congestion degree coefficient and the driving speed to detect whether the automated guided vehicle is in a low-speed stable state.

[0007] Preferably, in step S243, using the space congestion degree coefficient and the driving speed to detect whether the automated guided vehicle is in a low-speed stable state comprises: When the driving speed is less than a preset speed threshold and the space congestion degree coefficient is greater than or equal to a preset congestion threshold, the automated guided vehicle triggers a low-speed stable mode.

[0008] Preferably, in step S243, the construction of the space congestion quantification table comprises: Collect automated guided vehicle position distribution data under different operating scenarios, wherein the automated guided vehicle position distribution data includes longitudinal distance intervals and lateral gap intervals; Divide the space channel into several levels based on the longitudinal distance intervals and the lateral gap intervals, and define a congestion degree identifier for each level; For each congestion degree identifier, perform correlation analysis with the collected driving speed, brake frequency, and waiting time of the automated guided vehicle in actual operation, extract the numerical coefficient corresponding to each congestion level, and obtain the space congestion quantification table.

[0009] Preferably, step S3 comprises the following steps: Step S31: In the detected low-speed stable state of the automated guided vehicle, filter out the space channel where the automated guided vehicle is located and mark it as a candidate stable space channel; Step S32: Perform vehicle operation anomaly detection on each candidate stable space channel to confirm the stable space channel; and assign a residence time to each stable space channel; Step S33: Calculate the channel superiority degree of each stable space channel, and make a comprehensive comparison combined with the residence time. When the channel superiority degree is less than the preset superiority degree threshold, and the residence time is greater than or equal to the preset time threshold, a path conversion suggestion signal is generated and sent to the trolley scheduling control module; Step S34: When the channel superiority degree is greater than or equal to the preset superiority degree threshold, or the residence time is less than the preset time threshold, the original channel is kept running.

[0010] Preferably, the vehicle operation anomaly detection of each candidate stable space channel in step S32 comprises: Collecting the running data of the trolley in each candidate stable space channel, including vehicle speed, acceleration, obstacle distance in the channel, and vehicle lateral offset amount; When the vehicle speed deviates from the set low speed interval by more than ±0.2 m / s, or the absolute value of the acceleration continuously exceeds 0.4 m / s² for more than 3 seconds, or the obstacle distance is less than 1.0 m for more than 2 seconds, or the vehicle lateral offset amount exceeds 10 cm, it is determined that the vehicle is running abnormally; In a continuous monitoring period, if no vehicle operation anomaly is detected, the space channel is marked as a stable space channel; if any anomaly is detected in the monitoring period, the space channel is marked as an unstable space channel and is excluded, wherein the continuous monitoring period is 30-120 s.

[0011] Preferably, the formula for calculating the channel superiority degree of each stable space channel in step S33 is as follows: ; In the formula, is the channel superiority degree coefficient, is the normalized average speed, is the normalized channel congestion coefficient, is the normalized traffic efficiency, is the normalized stability coefficient, is the normalized risk degree, , ... is the weight coefficient, and satisfies + + + + =1.

[0012] Preferably, when the trolley scheduling control module of the trolley receives the path conversion suggestion signal, it further comprises: Setting the space channel where the trolley is located as the first channel; confirm the converted spatial channel and set as the second channel based on the path conversion suggestion signal; In the second channel, a smooth constraint prediction control is used for the trolley tracking, and after completing the short-time obstacle avoidance in the second channel, the trolley returns to the first channel, wherein the smooth constraint is specifically a trajectory curvature and its change rate, and a longitudinal acceleration and its change rate constraint.

[0013] Preferably, the short-time obstacle avoidance further comprises: When the trolley is detected to intersect with a trolley in the opposite direction, the trolley and the trolley in the opposite direction are assigned with a traffic priority to perform a vehicle sequential traffic operation.

[0014] The present application has the following beneficial effects: I. By introducing the idea of swarm intelligence, comprehensively utilizing the position information, driving speed and spatial congestion degree between multiple automatic guided trolleys and the target, dynamic perception and collaborative decision-making among multiple trolleys in the path planning process are realized, which can effectively solve the problems of path conflict, congestion backlog and unbalanced scheduling in the prior art, and significantly improve the overall transportation efficiency. Through the identification of the drivable spatial channel based on the center line and width of the candidate path, it is ensured that the path planning has higher spatial matching accuracy and feasibility.

[0015] II. In the low-speed stable state detection, the vehicle-mounted motion sensor and the vehicle fleet position interconnection function are used, combined with the real-time coordinate information of adjacent trolleys, to accurately calculate the straight-line distance and relative motion direction between the two trolleys, and to quantitatively evaluate through the spatial congestion quantization table, so that the congestion degree judgment is more objective and quantifiable, ensuring the reliability of the low-speed stable state recognition. At the same time, through the hierarchical design of the longitudinal distance interval and the lateral gap interval, and the correlation analysis with the traffic speed, brake frequency and waiting time, accurate congestion quantification in different scenarios is realized, which is more adaptable.

[0016] III. In the stable spatial channel screening process, a vehicle operation abnormality detection mechanism is introduced to comprehensively monitor multiple-dimensional operation parameters such as vehicle speed, acceleration, obstacle distance and vehicle lateral deviation, which can timely eliminate unstable channels and ensure the safety and continuity of path selection. Through the comprehensive judgment of the residence time and the superiority of the channel, frequent and ineffective path switching is avoided, and the stability of operation and the rationality of scheduling are improved.

[0017] IV. In the path conversion execution link, the trolley is tracked through the smooth constraint prediction control, the trajectory curvature and its change rate, and the longitudinal acceleration and its change rate are constrained, which ensures the smoothness of the path switching process and the safety of vehicle control. At the same time, in the short-time obstacle avoidance process, a traffic priority allocation mechanism is introduced, which effectively solves the problem of priority traffic when the trolleys in opposite directions intersect, avoids the efficiency reduction caused by mutual yielding of multiple trolleys, and realizes the orderly traffic under the cooperation of multiple trolleys. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A schematic diagram of the step flow of the path planning method of the automatic guided vehicle based on swarm intelligence is shown in the figure; Figure 2 A schematic diagram of the step flow of the path planning method of the automatic guided vehicle based on swarm intelligence is shown in the figure; Figure 1 A schematic diagram of the detailed implementation step flow of step S2 in the method is shown in the figure; Figure 3 A schematic diagram of the carrying of the automatic guided vehicle is shown in the figure; The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0019] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0021] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 A path planning method of an automatic guided vehicle based on swarm intelligence, the method comprising the following steps: Step S1: confirming a plurality of candidate paths based on the position information between the automatic guided vehicles and the target; confirming the space channel that the vehicle can travel according to the center line and the width of the candidate path; In an embodiment, reference can be made to Figure 3The system receives real-time position information between the automated guided vehicles and the target. For example, the position information obtained at a first time can be defined as first position data, and the first time is the start time of the current scheduling period. The system can calculate a plurality of candidate paths from the current position of each vehicle to the target position according to the first position data and the obstacle information in the environment map.

[0023] In some embodiments, the candidate paths can be used to represent drivable routes of the vehicles from the current position to the target position. For example, the system can generate a drivable space channel by analyzing the geometry of the candidate paths, extracting the center line of the paths, and determining the total width of the channel according to the width of the vehicle itself and the lateral safety distance.

[0024] For another example, when the center line of the candidate path satisfies the minimum safety distance with the obstacles on both sides throughout the path, the space channel of the path can be determined as an effective channel. When the distance between a certain segment of the center line and the obstacle is insufficient, the channel can be determined as an unusable channel and be removed.

[0025] For another example, at the first time and the second time, if the space channel of the same path is detected to remain unblocked and meet the width and safety distance requirements, the channel can be determined as a stable drivable channel. That is, the drivable channels with higher priority can also be selected by judging the continuity and stability of the channels, so as to facilitate subsequent accurate path tracking and obstacle avoidance operations.

[0026] Step S2: obtaining the driving speed of the automated guided vehicle and the congestion degree between the vehicle and the adjacent vehicle; detecting whether the vehicle is in a low-speed stable state through the driving speed and the congestion degree; In an embodiment, the driving speed information of the automated guided vehicle and the congestion degree data between the vehicle and the adjacent vehicle can be obtained in real time. For example, the driving speed obtained at a first time can be defined as first speed data, and the congestion degree obtained at the first time can be defined as first congestion data, and the first time is the start time of the current detection period. The system can determine the running state of the vehicle at the first time based on the first speed data and the first congestion data.

[0027] In another embodiment, the low-speed stable state can be used to represent that the vehicle has a low speed and small fluctuation in the driving process, and the surrounding environment is in a relatively stable traffic state. For example, when the driving speed of the vehicle is lower than a preset low-speed threshold and the speed change rate is less than a stable threshold, and the average distance between the vehicle and the adjacent vehicle is greater than a safety distance, the vehicle can be determined to be in a low-speed stable state.

[0028] For example, when the driving speed of the vehicle is detected to be lower than the low-speed threshold, the speed change rate is detected to be lower than the stability threshold, and the congestion degree of the adjacent vehicle is detected to be lower than the preset congestion threshold at both the first time and the second time, it can be determined that the vehicle is in a low-speed stable state.

[0029] For example, by continuously monitoring the speed and congestion degree change trend of the vehicle at multiple continuous times, it can be determined whether the vehicle is in a low-speed stable state, a congestion state or an acceleration state, so as to facilitate the subsequent scheduling system to take path optimization or vehicle distance adjustment measures in advance.

[0030] Step S3: screening a stable space channel in the space channel set according to the low-speed stable state, and setting a residence time based on the stable space channel; calculating the channel superiority degree of the stable space channel, and confirming whether the vehicle needs to perform path switching in combination with the residence time.

[0031] In an embodiment, based on the low-speed stable state detected in step S2, a channel that remains unblocked, has a width and a safety distance meeting the requirements, and has no potential conflicts at multiple continuous detection times can be screened from the space channel set as a stable space channel. For example, when a certain channel does not appear to have an obstacle intrusion or insufficient width in two continuous detections, it can be determined that the channel is a stable space channel.

[0032] In some embodiments, a residence time can be set for the stable space channel, which represents the time for the vehicle to keep driving in the channel without switching to other channels. For example, the residence time can be counted from the time when the vehicle enters the stable space channel, and ends when the channel state changes or the vehicle performs path switching.

[0033] For example, the channel superiority degree can be used to reflect the comprehensive advantages of the stable space channel in terms of traffic efficiency and safety. The system can calculate the superiority degree score according to the channel length, curvature, minimum safety distance from obstacles, expected traffic time, and potential conflict risk, and sort the stable space channels.

[0034] For example, when the superiority degree of a certain stable space channel is lower than that of other optional channels, and the residence time has exceeded a preset residence threshold, it can be determined that the vehicle needs to perform path switching to enter a channel with a higher superiority degree; when the difference in superiority degree is insufficient or the residence time does not exceed the threshold, the vehicle can maintain driving in the current channel to reduce the running fluctuations caused by frequent switching.

[0035] As an example of the present application, reference is made to FIG. 1, which shows a schematic diagram of a vehicle driving in a space channel according to an embodiment of the present application. In this example, the step S2 includes: Figure 2 Step S21: obtaining the driving speed of the automatic guided vehicle through a vehicle-mounted motion sensor; ​Step S22: enabling the vehicle fleet position interconnection function on the automated guided vehicle, receiving real-time coordinate information periodically broadcasted by the adjacent vehicles, wherein the real-time coordinate information comprises the planar position of the vehicle in the coordinate system of the working scene; Step S23: for any automated guided vehicle, calculating the straight-line distance between the vehicle and the adjacent vehicle according to the position information and the received position information of the adjacent vehicle, and confirming the relative motion direction of the automated guided vehicle in combination with the change trend of the planar position; Step S24: confirming the space congestion degree of the vehicle and the adjacent vehicle in the same space channel according to the relative motion direction; and detecting whether the vehicle is in a low-speed stable state according to the driving speed and the congestion degree.

[0036] In an embodiment, the vehicle-mounted motion sensor can include an inertial measurement unit, a wheel speed sensor, an odometer, etc., for real-time detection of the driving speed of the vehicle. The system can record the speed data at a preset sampling period (e.g., once per second) and transmit it to the dispatch control system for subsequent state determination. The vehicle fleet position interconnection function can be implemented based on a wireless communication module (such as an industrial wireless local area network, ultra-wideband communication, or dedicated short-range communication), and the adjacent vehicles will send their two-dimensional position coordinates in the scene coordinate system at a fixed broadcast period (e.g., 500 milliseconds), thereby realizing real-time position sharing between vehicles.

[0037] In some embodiments, the system can use the Euclidean distance formula to calculate the straight-line distance between the vehicle and the adjacent vehicle, and determine the relative motion direction by the change direction of the coordinates in a plurality of consecutive sampling periods, such as approaching each other, moving away from each other, or keeping parallel. The space congestion degree can be evaluated by the number of adjacent vehicles within a unit distance range and the relative motion direction. For example, in the same space channel, if the vehicle speed is low and the number of adjacent vehicles around is large, and the relative motion direction tends to be the same, it can be determined that it is in a low-speed stable state; otherwise, it is in a congested or dispersed driving state.

[0038] In another embodiment, the low-speed threshold can be set to 0.3-0.8 m / s, the stable speed change rate threshold can be set to 0.05-0.1 m / s2, and the distance range for judging the degree of space congestion can be set to 2-5 m. The low-speed threshold is set in the above range because, for most small and medium-sized automated guided vehicles, the vehicle can maintain smooth driving and will not affect the work efficiency in this speed range; below 0.3 m / s, the work efficiency will be significantly reduced, and above 0.8 m / s, the vehicles will frequently accelerate and decelerate. The range of the stable speed change rate threshold is set to ensure smooth driving at low speed and to prevent channel congestion caused by frequent acceleration or deceleration. The distance range for judging the degree of space congestion is determined in combination with the length of the vehicle (usually 1-1.5 m) and the necessary safety distance, and the range of 2-5 m can reflect the actual density between vehicles and avoid misjudgment of vehicles in adjacent but different channels as congested.

[0039] Preferably, step S24 comprises the following steps: Step S241: judging whether the vehicle and the adjacent vehicle are in the same space channel according to the relative motion direction; Step S242: after determining that they are in the same space channel, evaluating the degree of space congestion of the channel according to the distance between the vehicle and the adjacent vehicle, the lateral gap, and the front and rear distribution; Step S243: quantifying the degree of space congestion of the channel based on a preset space congestion quantification table to obtain a space congestion degree coefficient; and detecting whether the vehicle is in a low-speed stable state using the space congestion degree coefficient and the driving speed.

[0040] In an embodiment, the relative motion direction of the two vehicles can be judged according to the trend of the planar position change of the vehicle and the adjacent vehicle in the work scene coordinate system in combination with the direction of the channel center line. When the relative motion direction is consistent and the lateral offset of the two vehicles is less than a preset threshold, it can be determined that the two vehicles are in the same space channel; otherwise, it is determined that they are in different channels. The system can calculate the straight-line distance between the vehicle and the adjacent vehicles in front and behind, and comprehensively evaluate the degree of congestion in combination with the lateral gap between the two vehicles. For example, when the front distance is short and the lateral gap is insufficient, the system can determine that the region is a high-congestion region; otherwise, it is determined to be a low-congestion region.

[0041] In some embodiments, the space congestion quantification table can be classified according to the degree of congestion, for example, the coefficient is divided into continuous values between 0 and 1, and the larger the coefficient, the more congested the channel. The system can jointly determine the coefficient and the current driving speed of the vehicle: when the coefficient is greater than a set congestion threshold and the driving speed is lower than a low-speed threshold, it can be determined that the vehicle is in a low-speed stable state.

[0042] In another embodiment, the lateral offset threshold value can be set to 0.3-0.6 meters, the front-rear distance determination range can be set to 1.5-4 meters, the congestion threshold value of the space congestion degree coefficient can be set to 0.6-0.8, and the low-speed threshold value can be set to 0.3-0.8 meters per second. The lateral offset threshold value is set to 0.3-0.6 meters considering that the width of most automatic guided vehicles is about 0.8-1 meter, which can effectively distinguish whether they are in the same lane. The determination range of the front-rear distance combines the length of the vehicle (usually 1-1.5 meters) and the requirement of the safety distance, which can cover the passing interval in common operation scenarios. The congestion threshold value of the space congestion degree coefficient is set to 0.6-0.8 to trigger the low-speed stable state determination in time in the medium-high congestion state, avoiding frequent braking of the vehicle due to congestion. The low-speed threshold value is set to 0.3-0.8 meters per second to ensure smooth driving while taking into account the operation efficiency.

[0043] Preferably, the step S243 of detecting whether the vehicle is in the low-speed stable state by using the space congestion degree coefficient and the driving speed comprises: When the driving speed is less than the preset speed threshold value and the space congestion degree coefficient is greater than or equal to the preset congestion threshold value, the vehicle triggers the low-speed stable mode.

[0044] In an embodiment, when the driving speed is less than the preset speed threshold value and the space congestion degree coefficient is greater than or equal to the preset congestion threshold value, the vehicle triggers the low-speed stable mode. In the low-speed stable mode, the vehicle will drive smoothly at a lower speed while maintaining a safe distance from the adjacent vehicles to ensure the safety and stability of driving in the lane.

[0045] In some embodiments, the system can periodically monitor the driving speed and the space congestion degree coefficient of the vehicle, and when the speed threshold value and the congestion threshold value conditions are met for two or more consecutive detection periods, it is confirmed that the vehicle enters the low-speed stable mode, avoiding misjudgment of the state due to instantaneous fluctuations.

[0046] For example, in some embodiments, the speed threshold value can be set to 0.3-0.8 meters per second, and the congestion threshold value of the space congestion degree coefficient can be set to 0.6-0.8. The speed threshold value is set to 0.3-0.8 meters per second to ensure that the vehicle can still maintain stable driving at low speed without affecting the operation efficiency; lower than 0.3 meters per second leads to too low operation efficiency, and higher than 0.8 meters per second causes safety hazards in congested lanes. The congestion threshold value of the space congestion degree coefficient is set to 0.6-0.8 to trigger the low-speed stable mode in time in the medium-high congestion state, ensuring that the vehicle can run smoothly in the lane and avoiding frequent braking or collision risks.

[0047] Preferably, the construction of the space congestion quantification table in the step S243 comprises: Collect the position distribution data of the tugs under different running scenarios, wherein the position distribution data of the tugs includes longitudinal distance intervals and lateral gap intervals; The longitudinal distance intervals and the lateral gap intervals of the space channel are divided into several levels, and a congestion degree identifier is defined for each level; For each congestion degree identifier, the passing speed, brake frequency and waiting time of the tugs collected in actual operation are analyzed in association, the numerical coefficients corresponding to each congestion level are extracted, and a space congestion quantification table is obtained.

[0048] In an embodiment, the position distribution data of the tugs under different running scenarios is collected, wherein the position distribution data of the tugs includes longitudinal distance intervals and lateral gap intervals. For example, the actual positions of the tugs are collected under different scenarios such as multiple space channels and intersection points, curves and straight lines, and the distance information of each tug from the front, rear and both sides of the tugs is recorded.

[0049] Based on the longitudinal distance intervals and the lateral gap intervals of the space channel, the channel is divided into several levels, and a congestion degree identifier is defined for each level. For example, the longitudinal distance intervals can be divided into three levels of low, medium and high, and the lateral gap intervals can be divided into three levels of small, medium and large, thereby forming different congestion combinations, each combination corresponding to a congestion degree identifier.

[0050] For each congestion degree identifier, the passing speed, brake frequency and waiting time of the tugs collected in actual operation are analyzed in association, the numerical coefficients corresponding to each congestion level are extracted, and a space congestion quantification table is obtained. By analyzing the running characteristics of the tugs under different congestion levels, the congestion degree of the channel can be quantified, providing a basis for subsequent low-speed stable state determination.

[0051] In some embodiments, the longitudinal distance interval can be set to 1-4 meters, and the lateral gap interval can be set to 0.3-0.8 meters. The congestion degree identifier can be divided into three levels of low, medium and high, respectively corresponding to space congestion coefficients of 0.2-0.4, 0.4-0.6 and 0.6-0.8. The longitudinal distance interval is set to 1-4 meters based on the actual situation that the length of the tug is usually 1-1.5 meters, to ensure that the longitudinal distance can reflect the density of the vehicles and cover common operating scenarios; the lateral gap interval of 0.3-0.8 meters is determined in combination with the width of the tug and the safe lateral distance, to distinguish different congestion levels. The congestion degree coefficient is set to the range of 0.2-0.8, to quantify the congestion state of the space channel, so that the system can reasonably determine the low-speed stable mode according to different congestion coefficients, while taking into account the driving efficiency and safety.

[0052] Preferably, step S3 comprises the following steps: Step S31: In the detected low-speed stable state of the trolley, the space channel where the trolley is located is screened out and marked as a candidate stable space channel; Step S32: Vehicle operation anomaly detection is performed on each candidate stable space channel to confirm the stable space channel; and a residence time is allocated to each stable space channel; Step S33: The channel superiority degree of each stable space channel is calculated, and a comprehensive comparison is made in combination with the residence time; when the channel superiority degree is less than a preset superiority degree threshold value and the residence time is greater than or equal to a preset time threshold value, a path conversion suggestion signal is generated and sent to the trolley dispatching control module; Step S34: When the channel superiority degree is greater than or equal to the preset superiority degree threshold value or the residence time is less than the preset time threshold value, the original channel is kept running.

[0053] In an embodiment, based on the low-speed stable state detected in step S2, a channel that remains unblocked, has a width and a safety distance meeting the requirements in multiple consecutive detection periods can be screened out from the set of space channels, and marked as a candidate stable space channel. The operation anomaly detection can include judging the abnormal behaviors of the trolley in the channel, such as acceleration, deceleration, stopping, lateral deviation, etc. After confirming the stability of the channel, the system can start timing from the time when the trolley enters the channel, and calculate the residence time of the trolley in the channel, which is used for subsequent channel superiority degree and path conversion judgment.

[0054] In some embodiments, the channel superiority degree can be calculated by comprehensively considering factors such as channel length, curvature, minimum safety distance of obstacles, estimated passing time and potential conflict risk. When the channel superiority degree is low and the trolley stays in the channel for too long, the system generates a path conversion suggestion signal to prompt the trolley dispatching system to adjust the driving path. When the channel superiority degree is high or the trolley stays in the channel for a short time, the system maintains the trolley driving along the original channel, avoiding frequent path switching, and improving the overall driving efficiency and stability.

[0055] In another embodiment, the channel superiority degree threshold value can be set to 0.6 to 0.8, and the residence time threshold value can be set to 10 to 30 seconds. The channel superiority degree threshold value is set to 0.6 to 0.8 based on the comprehensive evaluation of the trolley channel passing efficiency and safety to ensure that the path conversion is triggered when the channel condition is poor, and the driving safety and efficiency are improved. The residence time threshold value is set to 10 to 30 seconds, which takes into account that the trolley may stay in the low-speed stable state due to traffic congestion or work factors, and exceeding the threshold value indicates that the channel has low efficiency or potential blockage, thereby reasonably triggering path optimization.

[0056] Preferably, the vehicle operation anomaly detection on each candidate stable space channel in step S32 includes: Collecting the running data of each candidate stable space channel, including vehicle speed, acceleration, obstacle distance in the channel, and vehicle lateral offset; When the vehicle speed deviates from the set low speed interval by more than ± 0.2 m / s, or the absolute value of acceleration continuously exceeds 0.4 m / s2 for more than 3 seconds, or the obstacle distance is less than 1.0 m for more than 2 seconds, or the vehicle lateral offset exceeds 10 cm, the system determines that the vehicle is running abnormally. If no vehicle running abnormality is detected within the continuous monitoring period, the space channel is marked as a stable space channel; if any abnormality is detected within the monitoring period, the space channel is marked as an unstable space channel and is excluded, wherein the continuous monitoring period is 30-120 seconds.

[0057] In an embodiment, the running data of each candidate stable space channel is collected, including vehicle speed, acceleration, obstacle distance in the channel, and vehicle lateral offset. Within the continuous monitoring period, the system records and analyzes the running state of the vehicle in real time to determine the running stability of the channel. When the vehicle speed deviates from the set low speed interval by more than ± 0.2 m / s, or the absolute value of acceleration continuously exceeds 0.4 m / s2 for more than 3 seconds, or the obstacle distance is less than 1.0 m for more than 2 seconds, or the vehicle lateral offset exceeds 10 cm, the system determines that the vehicle is running abnormally.

[0058] If no vehicle running abnormality is detected within the continuous monitoring period, the space channel is marked as a stable space channel; if any abnormality is detected within the monitoring period, the space channel is marked as an unstable space channel and is excluded. The continuous monitoring period can be set to 30-120 seconds to balance monitoring accuracy and response timeliness.

[0059] In some embodiments, through real-time analysis of various running parameters within the continuous monitoring period, the system can ensure that the channel is only included in the stable space channel set when the channel running condition is continuously stable, thereby improving the safety and reliability of path selection and scheduling.

[0060] Preferably, the formula for calculating the channel superiority degree of each stable space channel in step S33 is as follows: ; In the formula, is the channel superiority degree coefficient, is the normalized average speed, is the normalized channel congestion coefficient, is the normalized traffic efficiency, is the normalized stability coefficient, is the normalized risk degree, , ... is a weight coefficient, and satisfies + + + + =1.

[0061] In an embodiment, the parameter setting is as follows: average speed is used to reflect the smoothness of the passage, the higher the speed, the greater the superiority of the passage; passage congestion coefficient reflects the density of the trolley in the passage, the higher the congestion, the lower the superiority, so 1- is used in the formula; passage efficiency indicates the efficiency of the trolley actually passing through the passage, including factors such as stasis and brake times in the passage, the higher the efficiency, the higher the superiority; stability coefficient is used to measure the smoothness of the trolley running in the passage, such as the duration of the low-speed stable state and the number of abnormalities, the higher the stability, the higher the superiority; risk degree indicates potential safety hazards of the passage, such as insufficient minimum distance from obstacles or collision probability, the higher the risk, the lower the superiority, so it is used as a negative term in the formula; weight coefficient to can be configured according to the actual operation needs and safety strategies of the trolley to balance the influence of speed, congestion, efficiency, stability and risk on the superiority of the passage.

[0062] The passage selection in the prior art usually only considers a single factor (such as speed or passage length), which easily ignores the congestion level and safety risk in the passage, resulting in frequent braking of the trolley or potential collisions. The formula comprehensively evaluates the superiority of the passage in multiple dimensions, taking into account speed, congestion, efficiency, stability and risk, which can realize the quantification and sorting of the running state of the passage, make the path selection more scientific and reasonable, reduce the risk of frequent path switching and accidents, and improve the overall operation efficiency and safety of the trolley.

[0063] Preferably, when the trolley dispatching control module of the trolley receives the path switching suggestion signal, it further comprises: setting the space passage where the trolley is located as the first passage; confirming the switched space passage based on the path switching suggestion signal and setting it as the second passage; using smooth constraint predictive control for trolley tracking in the second passage and returning to the first passage after completing short-time obstacle avoidance in the second passage, wherein the smooth constraint is specifically the trajectory curvature and its change rate, and the longitudinal acceleration and its change rate constraint.

[0064] In an embodiment, the space channel where the trolley is located is set as a first channel for recording information of the current driving channel so as to realize regression after path switching. Based on the path switching suggestion signal, the switched space channel is confirmed and set as a second channel for temporarily switching the driving path of the trolley to realize channel optimization. The trolley is tracked by using a smooth constraint prediction control in the second channel, and the first channel is returned to after short-time obstacle avoidance in the second channel. The smooth constraint includes trajectory curvature and its change rate constraint, and longitudinal acceleration and its change rate constraint, which are used to ensure that the trolley keeps smooth and drivable during path switching and short-time obstacle avoidance, and avoid safety hazards caused by sudden acceleration or sharp turning of the vehicle.

[0065] In some embodiments, the system can collect the position, speed, acceleration and path curvature information of the trolley in real time, generate a smooth trajectory according to a prediction model, and dynamically adjust the longitudinal acceleration and the change rate of the curvature, so that the trolley can safely drive along the optimized channel, and return to the original channel smoothly after short-time obstacle avoidance.

[0066] In another embodiment, the trajectory curvature constraint can be set to 0.2 to 0.5 , the change rate of the curvature constraint can be set to 0.05 to 0.1 , wherein is meters, is seconds, the longitudinal acceleration constraint can be set to 0.3 to 1.0 m / s², and the change rate of the acceleration constraint can be set to 0.1 to 0.3 m / s³. The trajectory curvature and its change rate constraint are set in the above range based on the turning ability of the automatic guided trolley and the width of the channel in the working scene, to ensure that the vehicle does not turn too sharply when switching paths; the longitudinal acceleration and its change rate constraint are set in the above range to keep smooth acceleration and braking during short-time obstacle avoidance and regression, to avoid impact on goods, while ensuring safe and efficient operation of the trolley in the channel.

[0067] Preferably, the short-time obstacle avoidance further includes: When it is detected that the trolley intersects with a trolley in the opposite direction, the trolleys are assigned with passing priority to perform sequential passing operation of the vehicles.

[0068] In an embodiment, when it is detected that the trolley intersects with a trolley in the opposite direction in the space channel, the system assigns passing priority to the trolleys to perform sequential passing operation of the vehicles.

[0069] In some embodiments, the priority can be dynamically allocated based on the time sequence of the forklift entering the intersection, the current speed of the forklift, and the remaining space in the channel, etc. The forklift with priority passes through the intersection first, and the other forklift waits at a safe distance, and then passes through after the intersection area is safe, realizing safe and orderly traffic. The system can periodically monitor the relative distance and speed of the forklifts in the intersection to ensure that the priority allocation can be adjusted in real time, avoiding potential collision risks caused by speed differences or delayed judgments.

[0070] In some embodiments, the intersection detection range can be set to 3 to 6 meters, and the priority allocation is triggered when the forklifts are less than this distance apart; the safe waiting distance can be set to 0.5 to 1.0 meters to ensure that the waiting vehicle and the passing vehicle maintain a sufficient safety distance; when the speed difference between vehicles exceeds 0.3 meters per second, the forklift with higher speed can be allowed to pass through the intersection first. The reason for setting the intersection detection range to 3 to 6 meters is based on the comprehensive consideration of the braking distance and reaction time of the forklift, which can detect potential intersection risks in advance; the safe waiting distance of 0.5 to 1.0 meters ensures vehicle safety and does not occupy too much channel space, avoiding affecting the subsequent vehicle traffic; the setting of the speed difference of 0.3 meters per second is to reasonably determine the priority passing order when the forklift driving speed fluctuates to some extent, and to improve the intersection passing efficiency and safety.

[0071] Especially important is that the detection method for the intersection of the forklift and the forklift in the opposite direction further comprises: Obtaining the position coordinates of the forklift and the forklift in the opposite direction; Respectively performing trajectory extension prediction on the position coordinates of the forklift and the forklift in the opposite direction to obtain forklift predicted path data and opposite direction forklift predicted path data; Calculating the path intersection area of the forklift predicted path data and the opposite direction forklift predicted path data to generate intersection area candidate data; Performing time window overlap detection processing on the intersection area candidate data to obtain the forklift intersection detection result.

[0072] In an embodiment, the trajectory extension prediction can be based on the current speed, acceleration, and steering angle of the forklift to perform multi-step time series prediction, so as to judge the potential risk in advance before the actual intersection occurs; the time window overlap detection is used to judge whether the two vehicles enter the intersection area in the same time period, so as to effectively avoid the misjudgment of only path overlap but time overlap.

[0073] In another embodiment, the time range of trajectory extension prediction can be set to 2-5 seconds to balance the amount of calculation and prediction accuracy; the spatial determination threshold of the path intersection area can be set to 0.3-0.8 meters to take into account the vehicle size and positioning accuracy; the time overlap determination threshold of the time window overlap detection can be set to 0.5-1.5 seconds to determine whether two vehicles will arrive at the intersection area at the same time. The trajectory prediction time is set to 2-5 seconds because this range can cover the common braking reaction time of the forklift and the medium-short distance intersection scene, and the risk can be predicted in advance; the path intersection area threshold of 0.3-0.8 meters can ensure the safety margin of intersection determination under the premise of considering the actual size of the forklift (about 0.5-0.7 meters) and positioning error; the time window overlap threshold of 0.5-1.5 seconds can effectively balance the traffic efficiency and safety, and avoid false triggering of obstacle avoidance actions due to extremely short delays.

[0074] Especially important is that the time window overlap detection on the intersection area candidate data further includes: extracting the entering time and leaving time of the forklift and the forklift in the opposite direction at the intersection area according to the intersection area candidate data to generate intersection area time interval data; sorting the intersection area time interval data to arrange the entering and leaving records of each forklift in chronological order to generate time sorting list data; performing time window comparison of adjacent records using the time sorting list data to detect whether the stay time intervals of different forklifts overlap to generate time window overlap candidate data; filtering out records with time overlap and the same intersection area according to the time window overlap candidate data to generate effective overlap detection data; converting the effective overlap detection data into forklift intersection event records to generate forklift intersection detection results.

[0075] In an embodiment, the entering time and leaving time of the forklift and the forklift in the opposite direction at the intersection area are extracted according to the intersection area candidate data to generate intersection area time interval data. The entering time can be determined by the first intersection time of the vehicle trajectory and the boundary of the intersection area, and the leaving time can be determined by the last intersection time of the vehicle trajectory and the boundary of the intersection area. Secondly, the intersection area time interval data is sorted to arrange the entering and leaving records of each forklift in chronological order to generate time sorting list data. During the sorting process, the time records can be accurate to the millisecond level to improve the detection accuracy.

[0076] Then, time window comparison of adjacent records is performed by using the time-ordered list data to detect whether there is overlap between the stay time intervals of different trolleys, and time window overlap candidate data is generated. The time window comparison can be achieved by judging whether the entering time and leaving time of two trolleys are staggered. Subsequently, records with the same time overlap and corresponding intersection area are filtered out according to the time window overlap candidate data, and valid overlap detection data is generated. This step is used to eliminate misjudgment records with time overlap but located in different intersection areas. Finally, the valid overlap detection data is converted into trolley intersection event records, and trolley intersection detection results are generated. The results can be used as a trigger signal for subsequent passage priority allocation and short-time obstacle avoidance strategy execution.

[0077] In some embodiments, in order to further improve the robustness of detection, the overlap determination of time intervals can set a time overlap determination threshold, for example, 0.5 seconds to 1.5 seconds, and only when the time overlap exceeds the threshold, it is determined as an actual intersection, thereby reducing the false trigger phenomenon caused by small time errors.

[0078] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0079] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A path planning method for an automated guided vehicle based on swarm intelligence, characterized in that: The following steps are involved: Step S1: Identify several candidate paths based on the position information between multiple automated guided vehicles and the target; and determine the spatial channel in which the vehicle can travel based on the center line and width of the candidate paths; Step S2: Acquire the driving speed of the AGV and the degree of congestion between the AGV and adjacent AGVs; detect whether the AGV is in a low-speed stable state based on the driving speed and congestion degree; Step S3: screening a stable spatial channel among the spatial channels according to the low-speed stable state, and setting a dwell time based on the stable spatial channel; Calculate the channel superiority of the stable space channel and determine whether the transport vehicle needs to change its path based on the residence time.

2. The swarm intelligence-based automatic guided vehicle path planning method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining the driving speed of the AGV through the vehicle-mounted motion sensor; Step S22: activating the fleet location interconnection function on the automated guided vehicle to receive real-time coordinate information periodically broadcast by adjacent vehicles, wherein the real-time coordinate information includes the planar position of the vehicle in the work scene coordinate system; Step S23: For any AGV, the straight-line distance between the two vehicles is calculated based on the position information and the received position information of the adjacent vehicles, and the relative movement direction of the AGV is determined based on the change trend of the planar position; Step S24: confirming the degree of spatial congestion between the transport vehicle and adjacent transport vehicles in the same spatial channel according to the relative movement direction; and detecting whether the transport vehicle is in a low-speed stable state according to the driving speed and the degree of congestion.

3. The swarm intelligence-based automatic guided vehicle path planning method according to claim 2, characterized in that: Step S24 includes the following steps: Step S241: determining whether the transport vehicle and the adjacent transport vehicle are in the same spatial channel based on the relative movement direction; Step S242: After determining that the transport vehicles are located in the same spatial channel, the spatial congestion level of the channel is evaluated based on the distance between the transport vehicle and the adjacent transport vehicle, the lateral clearance, and the front-to-back distribution; Step S243: numerically quantify the spatial congestion degree of the channel based on a preset spatial congestion quantification table to obtain a spatial congestion degree coefficient; and use the spatial congestion degree coefficient and the driving speed to detect whether the transport vehicle is in a low-speed stable state.

4. The swarm intelligence-based automatic guided vehicle path planning method according to claim 3, characterized in that: In step S243, the method of detecting whether the transport vehicle is in a low-speed stable state by using the spatial congestion coefficient and the driving speed includes: When the driving speed is less than a preset speed threshold and the spatial congestion coefficient is greater than or equal to a preset congestion threshold, the transport vehicle triggers the low-speed stabilization mode.

5. The swarm intelligence-based automatic guided vehicle path planning method according to claim 2, characterized in that: The construction of the spatial congestion quantization table in step S243 specifically includes: Collect the position distribution data of transport vehicles under different operation scenarios, where the position distribution data of transport vehicles includes longitudinal distance intervals and lateral clearance intervals; The spatial channels are divided into several levels based on their longitudinal distance intervals and transverse gap intervals, and a congestion level indicator is defined for each level; For each congestion level indicator, a correlation analysis is performed with the travel speed, braking frequency, and waiting time of the transport vehicles collected during actual operation, and the numerical coefficient corresponding to each congestion level is extracted to obtain a spatial congestion quantification table.

6. The swarm intelligence-based path planning method for an automated guided vehicle according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: In the detected low-speed stable state of the transport vehicle, the spatial channel where the transport vehicle is located is screened out and marked as a candidate stable spatial channel; Step S32: Perform vehicle operation abnormality detection on each candidate stable spatial channel to confirm the stable spatial channel; allocate a dwell time to each stable spatial channel; Step S33: Calculate the channel superiority of each stable spatial channel and perform a comprehensive comparison based on the residence time. If the channel superiority is less than a preset superiority threshold and the residence time is greater than or equal to a preset time threshold, a path switching recommendation signal is generated and sent to the transport vehicle scheduling control module. Step S34: When the superiority of the channel is greater than or equal to the preset superiority threshold, or the residence time is less than the preset time threshold, the original channel operation is maintained.

7. The swarm intelligence-based path planning method for an automated guided vehicle according to claim 6, characterized in that: The vehicle operation abnormality detection for each candidate stable space channel in step S32 includes: Collect the operation data of the transport vehicle in each candidate stable space channel, including vehicle speed, acceleration, distance to obstacles in the channel, and lateral offset of the vehicle; If the truck's speed deviates from the set low-speed range by more than ±0.2m / s, or the absolute value of acceleration exceeds 0.4m / s² for more than 3 seconds, or the obstacle distance is less than 1.0m and lasts for more than 2 seconds, or the lateral offset of the vehicle exceeds 10cm, the vehicle is judged to be operating abnormally; During the continuous monitoring period, if no vehicle operation abnormality is detected, the spatial channel will be marked as a stable spatial channel; if any abnormality is detected during the monitoring period, the spatial channel will be marked as an unstable spatial channel and eliminated. The continuous monitoring period is 30 to 120 seconds.

8. The swarm intelligence-based path planning method for an automated guided vehicle according to claim 6, characterized in that: The formula for calculating the channel superiority of each stable spatial channel in step S33 is as follows: ； Where, is the channel superiority coefficient, is the normalized average speed, is the normalized channel crowding coefficient, is the normalized traffic efficiency, is the normalized stability coefficient, is the normalized risk, , ... is the weight coefficient and satisfies + + + + =1.

9. The swarm intelligence-based path planning method for an automated guided vehicle according to claim 7, characterized in that: When the transport vehicle dispatching control module receives the path switching suggestion signal, it also includes: Set the space channel where the transport vehicle is located as the first channel; confirming the converted spatial channel based on the path conversion suggestion signal and setting it as the second channel; In the second channel, predictive control with smoothness constraints is used to track the transport vehicle and complete short-term obstacle avoidance in the second channel before returning to the first channel. The smoothness constraints specifically include the trajectory curvature and its rate of change, as well as the longitudinal acceleration and its rate of change constraints.

10. The swarm intelligence-based path planning method for an automated guided vehicle according to claim 9, characterized in that: Short-term obstacle avoidance also includes: When it is detected that the transport vehicle intersects with the transport vehicle in the opposite direction, the transport vehicle and the transport vehicle in the opposite direction are allocated passage priority to perform the vehicle sequential passage operation.

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