An AGV driving path control method and device considering energy consumption optimization, equipment and medium

Abstract

The application discloses an AGV driving path control method and device considering energy consumption optimization, equipment and medium, relates to the AGV control technical field, and comprises the following steps: obtaining a first acceleration limit;First maximum load configuration;The first maximum load configuration and the first acceleration limit are segmented and proportionally scaled, and a plurality of first energy consumption optimization control sets are randomly combined;According to the first energy consumption optimization control set, the AGV drives, and the feature extraction is carried out on each group of AGV driving process, and the optimization evaluation value is evaluated;According to each group of first energy consumption optimization control set and the corresponding generated optimization evaluation value, the energy consumption optimization-AGV display model is constructed, and data analysis is carried out, the second energy consumption optimization control set is screened out, and is applied to the AGV driving path control, the application solves the energy consumption and stability contradiction problem caused by the coupling of AGV acceleration and load through multidimensional feature evaluation and optimization control set screening.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AGV control, and more particularly, to an AGV driving path control method and device considering energy consumption optimization, equipment and medium. BACKGROUND

[0002] With the development of intelligent manufacturing and smart logistics, AGV is widely used in warehouse distribution, production line material handling and other scenarios. The existing AGV driving path control method mainly focuses on the shortest path length, the shortest task response time or the optimal obstacle avoidance efficiency, and often uses graph search algorithm, trajectory tracking control, collaborative scheduling optimization and other technologies for path planning and execution control. However, the traditional path control method often ignores the optimization of the overall energy consumption of the system, especially in the application environment of frequent start-stop of multi-AGV system, significant load change and complex path, the actual energy consumption of AGV is closely related to path selection and control parameter setting.

[0003] In energy consumption optimization control, some existing technologies begin to introduce speed limit, obstacle avoidance frequency control and other strategies to reduce unnecessary energy consumption, but there is still a lack of systematic coupling modeling and regulation method for two key parameters with actual physical control significance, namely acceleration limit and maximum load configuration, during the AGV running process. These two parameters can be directly set by the control system, and they will also significantly affect the energy consumption of AGV in the aspects of acceleration stage, turning path selection, slope passing and start-stop frequency. The existing path control strategy fails to fully consider the collaborative constraints of acceleration and load at the path execution level, which easily leads to unstable energy consumption control and decreased running efficiency.

[0004] Specifically, acceleration limit and maximum load configuration have obvious defects when set at high and low values. If the acceleration limit is set too high, the motor power demand of AGV increases during startup and acceleration, and the energy consumption increases significantly, while there is a risk of slipping, losing control or tire wear too fast; if it is set too low, it will cause slow start, affect the task response speed, and even cause power shortage in complex paths such as climbing. Similarly, if the AGV load configuration is too large, although the single task efficiency is higher, the overall load increase will cause the energy consumption to increase sharply, and aggravate the problems of climbing difficulty, turning instability and other problems; on the contrary, if the load configuration is too low, the transportation frequency needs to be increased, which leads to overall path scheduling redundancy, and the proportion of empty running increases, and the unit task energy consumption actually increases.

[0005] There is a significant mutual coupling between the speed limit and the maximum load configuration. If the acceleration is not properly adjusted under high load conditions, the AGV is prone to control instability, energy waste and hardware damage during high inertia startup. If the high load is maintained under low acceleration conditions, the task may fail, the path may be invalid or the climbing may be limited due to insufficient power. Conversely, increasing acceleration under light load conditions can improve response efficiency, but may also cause energy conversion efficiency to decline and path tracking accuracy to decrease. Therefore, the two physical control parameters need to be coordinated to achieve energy efficiency balance in path control.

[0006] For example: for warehouse AGV cross-floor transportation tasks, the task weight is large, the path needs to pass through a slope, the load is large and the acceleration must be low, otherwise the vehicle will overload and lose control when climbing the slope.

[0007] For e-commerce sorting light load multi-frequency tasks, the load is light, the start and stop are frequent, the acceleration can be adjusted, but the maximum acceleration time period still needs to be limited for energy saving, the load is small, but the start and stop are frequent, and the acceleration frequency needs to be balanced to control energy consumption.

[0008] In the prior art, the control of AGV acceleration limit and maximum load configuration is mostly independent setting, lacking systematic analysis and joint optimization of the coupling relationship between the two, which is prone to cause insufficient power, excessive energy consumption or path failure during operation, and it is difficult to achieve stable path control under optimal energy consumption.

[0009] To solve the above problems, the present application provides a solution. SUMMARY

[0010] To overcome the above-mentioned defects of the prior art, embodiments of the present application provide an AGV driving path control method, device, equipment and medium considering energy consumption optimization, which effectively solves the contradiction between energy consumption and stability caused by the coupling of AGV acceleration and load through multi-dimensional feature evaluation and optimization control set screening.

[0011] To achieve the above-mentioned purposes, the present application provides the following technical solutions:

[0012] The application discloses an AGV driving path control method considering energy consumption optimization, which comprises the following steps: obtaining a first path, initializing an acceleration limit based on the curve characteristics of the first path to obtain a first acceleration limit; estimating a maximum load according to the terrain slope and key node load demand of the first path to obtain a first maximum load configuration; defining an energy consumption optimization control set, wherein the energy consumption optimization control set comprises a maximum load configuration and an acceleration limit; performing segmented proportional scaling processing on the first maximum load configuration and the first acceleration limit, and randomly combining to generate a plurality of first energy consumption optimization control sets; controlling AGV driving according to the first energy consumption optimization control set, extracting features of each AGV driving process, and evaluating an optimization evaluation value; constructing an energy consumption optimization-AGV display model according to each first energy consumption optimization control set and the corresponding generated optimization evaluation value, performing data analysis, screening out a second energy consumption optimization control set, and applying the second energy consumption optimization control set to AGV driving path control.

[0013] In a preferred embodiment, the first path is a pre-acquired initial AGV path; a sequence of spatial coordinate points of the first path is acquired; the change of the turning angle between adjacent coordinate points on the path is calculated; the turning radius of each point on the path is calculated based on the change of the turning angle; the minimum turning radius in the path is extracted; the maximum lateral acceleration range allowed is determined according to the minimum turning radius and in combination with the lateral stability requirement of the vehicle; the maximum acceleration limit is set in combination with the longitudinal dynamic performance of the vehicle, so that the maximum acceleration limit does not exceed the maximum lateral acceleration range; and the maximum acceleration limit is taken as the first acceleration limit.

[0014] In a preferred embodiment, the maximum load is estimated according to the terrain slope and key node load demand of the first path to obtain a first maximum load configuration, specifically: the slope data of the first path is acquired, including the slope values of each position point on the path; the maximum continuous interval of the slope in the first path is identified, and the maximum slope value of the interval is extracted; the maximum load that can be carried by the vehicle under the condition of the maximum slope for safe climbing is calculated as the maximum load value under the slope limit in combination with the driving force model and dynamic parameters of the vehicle; the design maximum carrying mass of all bridges in the first path is acquired, and the minimum bridge carrying mass value is extracted; the maximum load limit of all narrow passages in the path is acquired, and the minimum narrow passage load limit value is extracted; the minimum value is selected as the maximum load limit value of the key node of the path from the maximum load limit of the slope, the minimum bridge carrying mass and the minimum narrow passage load limit.

[0015] In a preferred embodiment, the first maximum load configuration and the first acceleration limit are segmented and scaled proportionally, and a plurality of first energy consumption optimization control sets are generated by random combination, specifically: a scaling sequence of the acceleration limit is set, the scaling sequence includes a plurality of scaling factors, the scaling factors are based on the first acceleration limit, and a group of acceleration limit candidate values are generated by scaling; a scaling sequence of the maximum load configuration is set, the scaling sequence includes a plurality of scaling factors, the scaling factors are based on the first maximum load configuration, and a group of maximum load configuration candidate values are generated by scaling; based on the acceleration limit candidate values and the maximum load configuration candidate values, a plurality of combinations of the acceleration limit and the maximum load configuration are generated by random combination as the first energy consumption optimization control set.

[0016] In a preferred embodiment, the features include energy consumption features, turning stability features, and task response speed features; the specific acquisition method of the energy consumption features is as follows: real-time current and voltage data of the driving motor of the AGV during the execution of the path task are collected; based on the collected current and voltage data, the instantaneous electric power at each sampling time is calculated; the instantaneous power in the entire task execution time interval is integrated to obtain the total energy consumption during the task; the cumulative travel distance of the AGV during the task execution is recorded; the unit distance energy consumption is calculated, and the total energy consumption is divided by the cumulative travel distance to eliminate the influence of the path length difference; the load weight change data during the task is obtained, and the load weighting coefficient is calculated, which is the time average value of the ratio of the load weight during the task to the maximum load; the unit distance energy consumption and the load weighting coefficient are combined, and weighted calculation is performed according to the set weight coefficient to obtain the energy consumption features.

[0017] In a preferred embodiment, the specific acquisition method of the task response speed features is as follows: response time data from receiving a scheduling instruction to actually starting an action during the execution of the task by the AGV is collected; the completion time of each key node during the task execution is recorded; the average value of all response times and key node completion times is calculated; the standard deviation of the response time and the key node completion time is calculated to reflect the fluctuation of the response time; the response speed variation coefficient is calculated by using the average value and the standard deviation as the task response speed features.

[0018] In a preferred embodiment, the energy consumption optimization-AGV display model is constructed according to each set of first energy consumption optimization control set and corresponding generated optimization evaluation value, specifically: each set of energy consumption optimization control set and its corresponding optimization evaluation value are one-to-one corresponding, forming a mapping relationship of energy consumption optimization control set and optimization evaluation value, that is, a point; the energy consumption optimization-AGV display model is a multi-dimensional parameter space, which is used to intuitively present the distribution of different control sets in energy consumption and performance; the mapping relationship of a plurality of energy consumption optimization control sets and optimization evaluation values is mapped to the energy consumption optimization-AGV display model, and a plurality of points are fitted, and the energy consumption optimization control set corresponding to the peak value is taken as the second energy consumption optimization control set and applied to the AGV driving path control.

[0019] An AGV driving path control system considering energy consumption optimization comprises an acceleration limit initialization module, a maximum load configuration initialization module, an energy consumption optimization control set acquisition module, a feature extraction module and a screening control module.

[0020] An electronic device comprises at least one processor and a memory connected in communication with the at least one processor, wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the AGV driving path control method considering energy consumption optimization.

[0021] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the AGV driving path control method considering energy consumption optimization.

[0022] The AGV driving path control method, device, equipment and medium considering energy consumption optimization have the following technical effects and advantages:

[0023] The application scientifically sets the acceleration limit and maximum load configuration of the AGV by systematically combining the path curve characteristics, terrain slope and key node load limit, and further constructs multiple energy consumption optimization control sets, and comprehensively evaluates and screens based on multiple characteristics such as energy consumption, turning stability and task response speed, so as to ensure that the optimized control parameters realize the optimal comprehensive performance of the lowest energy consumption, high driving stability and fast task response speed on the premise of guaranteeing the vehicle dynamics safety and path physical constraints. The method effectively solves the complex coupling problem between acceleration and load, improves the safety and efficiency of AGV path execution, enhances the adaptability and robustness of the control strategy, and promotes the efficient and stable operation of the intelligent logistics vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A flowchart of the AGV driving path control method considering energy consumption optimization of the application.

[0025] Figure 2 A structural schematic diagram of the AGV driving path control device considering energy consumption optimization of the application.

[0026] Figure 3 An electronic device structural schematic diagram of the AGV driving path control method considering energy consumption optimization of the application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0028] Embodiment 1, Figure 1 The AGV driving path control method considering energy consumption optimization of the application is given, including the following steps:

[0029] S1, obtaining a first path, initializing an acceleration limit based on the curve characteristics of the first path to obtain a first acceleration limit.

[0030] In this embodiment, the first path is an initial AGV path obtained in advance, and the first acceleration limit is initialized based on the curve characteristics of the first path.

[0031] The first path is a pre-obtained initial AGV path;

[0032] Obtaining a sequence of spatial coordinate points of the first path;

[0033] Calculate the corner change between adjacent coordinate points on the path;

[0034] Calculate the turning radius of each point on the path based on the corner change;

[0035] Extract the minimum turning radius in the path;

[0036] According to the minimum turning radius, combined with the lateral stability requirement of the vehicle, determine the allowable maximum lateral acceleration range;

[0037] Combined with the longitudinal dynamic performance of the vehicle, set the maximum acceleration limit so that it does not exceed the maximum lateral acceleration range;

[0038] The maximum acceleration limit is used as the first acceleration limit.

[0039] It should be noted that the determination of the maximum lateral acceleration range can be based on the tire grip of the vehicle and the vehicle speed calculation, that is, the lateral centrifugal force generated by the vehicle during turning cannot exceed the maximum friction between the tire and the ground, so as to ensure the turning stability of the vehicle. The maximum lateral acceleration can be obtained through empirical data, or based on the vehicle dynamics model, combined with the tire friction coefficient, vehicle gravity height and other parameters to calculate and determine.

[0040] It should be noted that the setting of the maximum acceleration limit not only considers the maximum lateral acceleration range, but also should be combined with the maximum output capacity of the vehicle motor and driving system, energy consumption and mechanical safety limit and other factors, to ensure that the acceleration limit will not exceed the safety boundary of the longitudinal dynamic performance of the vehicle. The limit value can be obtained by test data, or can be dynamically adjusted in actual operation, and the following is a feasible first acceleration limit acquisition formula:

[0041] Maximum lateral acceleration range: ;

[0042] Actual lateral acceleration of the vehicle when turning: ;

[0043] Allowable maximum longitudinal acceleration: ;

[0044] Combined with the maximum output capacity of the motor, the smaller value is finally taken as the first acceleration limit:

[0045] ;

[0046] In the formula, is the friction coefficient between the tire of the vehicle and the ground; g is the acceleration of gravity; v is the maximum estimated driving speed of the vehicle on the first path; is the minimum turning radius on the first path.

[0047] It should be noted that the turning radius of the path is approximated by calculating the radius of the circular arc formed by three consecutive points on the path, and the corner change is the measurement of the included angle of adjacent path segments. This method is a commonly used path curve characteristic analysis technique. The first path is a discrete set of spatial coordinate points, which can be obtained by sensor acquisition or planning system output.

[0048] S2, estimate the maximum load according to the terrain slope of the first path and the load demand of the key node, and obtain the first maximum load configuration;

[0049] In this embodiment, the first maximum load configuration is obtained by estimating the maximum load according to the terrain slope of the first path and the load demand of the key node, specifically:

[0050] Obtain the slope data of the first path, including the slope values of each position point on the path;

[0051] Identify the maximum continuous interval of the slope in the first path, and extract the maximum slope value of the interval;

[0052] Combine the driving force model and the dynamics parameters of the vehicle to calculate the maximum load that the vehicle can carry under the condition of the maximum slope, which is the maximum load value under the slope restriction;

[0053] Obtain the design maximum carrying capacity of all bridges in the first path, and extract the minimum bridge carrying capacity value;

[0054] Obtain the maximum load limit of all narrow passages in the path, and extract the minimum narrow passage load limit value;

[0055] From the slope restriction maximum load, the minimum bridge carrying capacity and the minimum narrow passage load limit, select the minimum value as the maximum load limit value of the key node of the path;

[0056] Compare the rated maximum load designed for the vehicle with the above-mentioned maximum load limit value of the key node of the path, and take the smaller value of the two as the first maximum load configuration. The following is the formula of the feasible first maximum load configuration: , wherein is the maximum carrying capacity calculated by the vehicle according to the driving force model in the maximum slope interval of the first path; is the minimum design carrying capacity of all bridges in the first path, is the minimum allowable load limit value of all narrow passages in the first path, is the rated maximum load value specified in the vehicle design parameters, wherein the calculation formula of the feasible maximum carrying capacity is as follows (without considering air resistance): , wherein is the maximum driving force, is the slope angle, is the rolling resistance coefficient (generally 0.01~0.03), is the vehicle self-weight.

[0057] It should be noted that the slope data can be obtained based on the first path through the path design system, or obtained by measuring with a topographic map in cooperation with a laser radar, a high-precision GPS, etc. The slope value of each position point in the path can be expressed as the ratio of the height difference within a certain distance before and after the point to the horizontal distance. The obtained continuous slope data is used for subsequent slope interval identification and analysis.

[0058] It should be noted that the identification of the maximum slope interval refers to screening all continuous uphill sections from the path, calculating the average slope and the maximum slope value of each section, and then extracting the overall maximum slope value as the boundary condition for subsequent load capacity analysis, to ensure that the vehicle can still operate normally under the most unfavorable slope.

[0059] It should be noted that the safe load of the vehicle under the maximum slope can be combined with the maximum output torque of the vehicle driving motor, the friction coefficient between the tire and the ground, the total mass of the vehicle, and the wheel diameter, etc. The maximum effective traction force that can be achieved on the slope is estimated through the vehicle driving capacity model, and the maximum allowed total load mass is converted to ensure that the vehicle will not slip or have insufficient power under the slope.

[0060] It should be noted that the maximum load capacity of the bridge is a structural design parameter provided in advance by the path planning system or the map database, which is usually calibrated or recorded by the infrastructure maintenance unit. If the path contains multiple bridges, the lowest load capacity among them is extracted as the maximum allowed load under the bridge restriction condition.

[0061] It should be noted that the maximum load limit of the narrow passage is determined according to the maximum bearing capacity of the structure on both sides of the passage or the requirement for safe passage, which is provided by the factory transportation specification or engineering design data. If there are multiple narrow passages in the path, the minimum load limit among them is also extracted as the constraint condition.

[0062] It should be noted that the maximum load limit value of the key node of the path is the unification of the most stringent conditions among the above three types of constraint factors, i.e. the minimum value among the three is the upper limit of the maximum operating load that the path can bear. This method can ensure that the vehicle safely passes through under all physical structures and dynamic conditions without structural overload or passage failure.

[0063] It should be noted that the rated maximum load of the vehicle is determined by the vehicle design parameters, which is usually provided by the manufacturer. If the value is higher than the limit value of the key node of the path, it must be adjusted downward to adapt to the path characteristics. Finally, the smaller one of the two is selected as the first maximum load configuration to ensure that the dual requirements of path safety and vehicle stability are met.

[0064] It should be noted that the first path is the initial default path, which is the path information generated by the AGV before the task begins or in the early stages of scheduling planning based on the preset target area and operational requirements. This path can be generated from map data by a path planning algorithm before the task is issued, and includes the main path coordinates, structural information, and known constraints of the area to be traversed. During actual operation, this path can be adjusted in real time according to factors such as dynamic obstacle avoidance, task updates, and changes in traffic scheduling. New path reconstruction or partial detours may occur, therefore the first path does not necessarily cover all potential path features.

[0065] Furthermore, when estimating the maximum load configuration, if the first path does not include some of the three types of constraint factors (such as missing bridge information or narrow passage restriction information), then by default, this factor does not constitute a constraint. That is, the constraint is set to an infinite value or a value greater than the maximum design load of the vehicle. This ensures that the lack of this information does not affect the evaluation of the maximum load configuration by other factors, and the overall estimation result is still valid and feasible.

[0066] S3, Define an energy consumption optimization control set, which includes a maximum load configuration and an acceleration limit;

[0067] S4, perform segmented scaling on the first maximum load configuration and the first acceleration limit, and randomly combine them to generate several first energy consumption optimization control sets;

[0068] In this embodiment, the step of performing piecewise scaling on the first maximum load configuration and the first acceleration limit, and randomly combining them to generate several first energy consumption optimization control sets, specifically involves:

[0069] A scaling sequence for setting acceleration limits is defined, the scaling sequence including multiple scaling factors, the scaling factors being based on a first acceleration limit and generating a set of candidate acceleration limit values ​​proportionally.

[0070] Set a scaling sequence for the maximum load configuration, the scaling sequence including multiple scaling factors, the scaling factors being based on a first maximum load configuration and generating a set of candidate maximum load configuration values ​​proportionally;

[0071] Based on candidate values ​​for acceleration limits and maximum load configurations, multiple combinations of acceleration limits and maximum load configurations are generated using a random combination method, which serve as the first energy consumption optimization control set.

[0072] It is to be noted that the scaling ratio sequence of the acceleration limit is selected in the range of 0.7 to 1.3, which is set by equal interval or experience value, to ensure that the generated candidate acceleration limit can cover the low-speed stable interval required for safe operation of the vehicle and include higher acceleration to meet the dynamic response requirement. It is to be noted that the scaling ratio sequence of the maximum load configuration is selected in the range of 0.6 to 1.2, and the specific value of the scaling ratio is adjusted according to the design rated load of the vehicle and the maximum load limit value of the key node of the path.

[0073] It is to be noted that the number of combinations of the generated multiple acceleration limits and maximum load configurations is determined according to actual optimization requirements and computing resources, and is not less than 10 groups and not more than 50 groups, to balance the coverage of the search space and the computing efficiency.

[0074] It is to be noted that each combination generated in the first energy consumption optimization control set needs to meet the vehicle dynamics safety constraints and the path physical constraints, to ensure that the acceleration limit and the load configuration corresponding to the combination are feasible and safe in actual operation.

[0075] S5, controlling the AGV to travel according to the first energy consumption optimization control set, and extracting features of each AGV travel process to evaluate an optimization evaluation value;

[0076] In the embodiment, the feature extraction of each AGV travel process is specifically:

[0077] The features include energy consumption features, turning stability features, and task response speed features.

[0078] The energy consumption feature is an index value for quantifying the overall energy consumption level of the AGV in executing the path task under different combinations of acceleration limit and maximum load configuration. The feature is calculated by collecting real-time current, voltage, driving power or battery power change parameters of the AGV in the entire travel path, combining with the vehicle running time and path length, calculating the total power consumption per task or energy consumption per unit distance, and finally obtaining a numerical feature for evaluating energy efficiency performance.

[0079] Analyzing the energy consumption feature has the following advantages for evaluating the effects of different energy consumption optimization control sets and for screening and finally solving the coupling problem of acceleration and load:

[0080] Quantitative comparison of control sets: through the extraction and analysis of the energy consumption feature index, the energy consumption performance of different control strategies under the same working condition can be quantitatively evaluated, thereby providing a unified criterion for screening the optimal control set.

[0081] Reveal the nonlinear coupling effect of acceleration and load on energy consumption: Energy consumption feature analysis can reveal the combined effect of acceleration variation (such as start frequency, acceleration / deceleration gradient) and load fluctuation (such as load step change) on energy consumption under the control set, which helps to identify the sensitive area of the coupling of the two on system energy efficiency.

[0082] Generation of support adaptive control factors: Through the mapping relationship between energy consumption features and control parameters, a regression model can be established to guide the design of adjustment factors for subsequent optimization control set, achieving energy efficiency optimal response for different load-acceleration combinations.

[0083] Enhance the robustness and generalization ability of control strategy: Analyze and extract stable energy consumption features under various working conditions, so that the selected control set is more robust and can adapt to typical and abnormal load-acceleration coupling situations, avoiding over-fitting optimization under a single working condition.

[0084] Promote multi-objective collaborative optimization: Incorporate energy consumption features into the control set optimization evaluation system to achieve a trade-off between energy consumption, response smoothness, mechanical impact, and other objectives, which helps to optimize the overall performance of the final control strategy.

[0085] The specific method for obtaining the energy consumption feature is as follows:

[0086] Collect real-time current and voltage data of the drive motor when the AGV executes the path task;

[0087] Based on the collected current and voltage data, calculate the instantaneous electric power at each sampling time;

[0088] Integrate the instantaneous power over the entire task execution time interval to obtain the total energy consumption during the task;

[0089] Record the cumulative travel distance of the AGV during the task execution;

[0090] Calculate the energy consumption per unit distance by dividing the total energy consumption by the cumulative travel distance to eliminate the influence of path length difference;

[0091] Obtain the load weight change data during the task, and calculate the load weighting coefficient, which is the time average of the ratio of the load weight during the task to the maximum load;

[0092] Combine the energy consumption per unit distance and the load weighting coefficient, and perform weighted calculation according to the set weight coefficient to obtain the energy consumption feature.

[0093] The specific calculation formula of the energy consumption feature is as follows:

[0094] Instantaneous electric power: ;

[0095] Total energy consumption: ;

[0096] Energy consumption per unit distance: ;

[0097] Load weighting coefficient: ;

[0098] Energy consumption characteristics: ;

[0099] In the formula, is the voltage; is the current; is the task time; is the load weight during the task; is the maximum load configuration; is the load influence weight coefficient, with a value range of 0.1 to 0.5, and a default value of 0.3, and D is the cumulative travel distance.

[0100] It should be noted that the calculation of total energy consumption uses the integral method to accumulate the instantaneous electric power in the time domain, and the integral interval should strictly correspond to the time range from the start of the task to the end of the task of the AGV. This time window is usually provided by the task scheduling system or the upper control system, ensuring that the energy consumption calculation is strictly aligned with the path task, avoiding the inclusion of idle or standby phase energy consumption, which affects the accuracy of the evaluation.

[0101] It should be noted that the cumulative distance of the AGV travel path is calculated in real time by its internal odometer or inertial navigation system, and the error should be within 2%. If the system has a large distance measuring error, a mileage correction module should be introduced to ensure the credibility of the energy consumption per unit distance.

[0102] It should be noted that the setting of the load influence weight coefficient is determined by engineering experience data, with a higher weight indicating a higher sensitivity to load changes, suitable for logistics task scenarios with large load fluctuations; a lower weight is suitable for applications in environments with stable load and high path complexity.

[0103] The turning stability feature is an index value used to quantify the ability of AGV to maintain posture balance and tire adhesion force control under different acceleration limits and maximum load configurations during path turning. This feature collects dynamic parameters such as yaw rate, lateral acceleration of the vehicle body, and steering trajectory offset of the AGV in the path turning section, evaluates whether the AGV has side slip, offset, or lateral instability phenomena during turning, and extracts numerical features of stability performance in combination with the maximum roll angle or attitude fluctuation amplitude of the vehicle when passing through a typical bend. This feature can reflect the direct impact of acceleration limits and load configurations on vehicle turning safety.

[0104] Analyzing the cornering stability feature has the following advantages for evaluating the effects of different energy consumption optimization control sets, screening, and ultimately solving the coupling problem of acceleration and load:

[0105] Expanded evaluation dimensions: The evaluation is not limited to energy consumption, but also covers dynamic stability, safety, etc.

[0106] More accurate control set screening: By analyzing the response of the vehicle during cornering, it can be found that some control strategies are energy-saving but have poor stability, helping to eliminate unsuitable solutions.

[0107] Coupling problem visualization: The coupling of acceleration and load is essentially reflected in actual handling, and this coupling can be quantitatively represented through the stability index.

[0108] Promote systematic optimization: Not only optimize energy consumption, but also ensure the dynamic performance of the whole vehicle, achieving multi-objective coordinated control.

[0109] The specific method for obtaining the cornering stability feature is as follows:

[0110] Collect the lateral acceleration data and steering angle data of each cornering section during the AGV executing the path task;

[0111] Identify all cornering sections in the path and extract the maximum value of lateral acceleration in each cornering section;

[0112] Compare the maximum value of lateral acceleration of each cornering section with the vehicle lateral stability threshold to determine whether it exceeds the threshold;

[0113] Count the number of times the lateral acceleration exceeds the stability threshold in all cornering sections as the number of cornering stability abnormalities;

[0114] Record the average value of the actual driving speed in all cornering sections and compare it with the upper limit of the safe cornering speed allowed by the vehicle design;

[0115] Count the number of cornering sections with speed exceeding the safe upper limit as the number of overspeed cornering;

[0116] Add the number of cornering stability abnormalities and the number of overspeed cornering to get the total number of unstable cornering events;

[0117] Divide the number of unstable cornering events by the total number of cornering sections to calculate the proportion of unstable cornering events as the cornering stability feature value.

[0118] It should be noted that the lateral acceleration data refers to the acceleration value perpendicular to the vehicle's driving direction detected by an inertial sensor (such as an accelerometer) when the vehicle is cornering, which is used to reflect the lateral force condition of the vehicle during cornering and evaluate its lateral stability.

[0119] It should be noted that the vehicle lateral stability threshold is a lateral acceleration limit value preset according to vehicle chassis design, tire grip, center of gravity height and ground adhesion coefficient, etc. Exceeding this value may cause the vehicle to slide, deflect or lose control. This threshold can be obtained through simulation and field test.

[0120] It should be noted that the actual driving speed is the instantaneous speed value collected by the AGV body speed sensor in real time. The time average of this speed data in each turning section can obtain the average turning speed of the section, reflecting the dynamic performance of AGV in the turning process.

[0121] It should be noted that the upper limit of the safe turning speed is a theoretical value calculated according to the vehicle dynamics model, turning radius and maximum allowable lateral acceleration, which is used to determine whether the AGV has the behavior of too fast speed in the turning process, and is preset by the vehicle control system.

[0122] It should be noted that the unstable turning event ratio, as a turning stability characteristic value, has a normalized characteristic, which can be applied to path tasks of different lengths and different structural complexities, so that the turning stability results of different tasks are comparable.

[0123] The task response speed feature is used to quantify the response efficiency of AGV after receiving the scheduling instruction. This feature measures the delay time between the issuance of the instruction and the actual start of the action by AGV, as well as the completion time of each key node in the task execution process, to comprehensively reflect the response speed performance of AGV under different acceleration limits and maximum load configurations.

[0124] Analyzing the task response speed feature has the following advantages for evaluating the effects of different energy consumption optimization control sets and for screening and finally solving the coupling problem of acceleration and load:

[0125] Directly reflects the dynamic response capability of AGV under different control parameter combinations, helping to identify the key factors affecting the work efficiency.

[0126] Reveals the influence of acceleration limit on the starting and accelerating process of AGV, avoiding task delay caused by slow response.

[0127] Reflects the influence of maximum load configuration on mechanical burden and response speed of power system, helping to balance the coordination of load and acceleration.

[0128] Promote effective control of task execution timeliness while ensuring energy consumption optimization.

[0129] Helps to screen out parameter combinations that can reduce energy consumption and maintain fast response, improving overall operation efficiency.

[0130] The specific acquisition method of the task response speed feature is as follows:

[0131] Collect the response time data from receiving the scheduling instruction to actually starting the action during the AGV executing the task;

[0132] Record the completion time of each key node in the task execution process;

[0133] Calculate the average value of all response times and key node completion times;

[0134] Calculate the standard deviation of the response time and the key node completion time to reflect the fluctuation of the response time;

[0135] Calculate the response speed variation coefficient as the task response speed feature using the average value and the standard deviation.

[0136] The evaluation generates an optimized evaluation value, specifically:

[0137] Based on the extracted energy consumption feature, turning stability feature and task response speed feature, a standard optimization evaluation model is constructed;

[0138] The standard optimization evaluation model is trained based on machine learning, and the model parameters are adjusted to fit the relationship between the features and the optimization target;

[0139] The best weight parameter of the standard optimization evaluation model is solved through the training process;

[0140] The solved optimization evaluation model takes the maximum acceleration limit and the maximum load configuration in the control set as input, and outputs the corresponding optimization evaluation value;

[0141] The optimization evaluation value is used to quantify the comprehensive performance of the AGV driving under different control sets, guiding the subsequent parameter selection and optimization;

[0142] The standard optimization evaluation model is a weighted form of the energy consumption feature, the turning stability feature and the task response speed feature, and the target of machine learning training is the corresponding best weight.

[0143] S6, according to each group of first energy consumption optimization control set and the corresponding generated optimization evaluation value, construct an energy consumption optimization-AGV display model, and perform data analysis to screen out a second energy consumption optimization control set, which is applied to AGV driving path control;

[0144] In this embodiment, the energy consumption optimization-AGV display model is constructed according to each group of first energy consumption optimization control set and the corresponding generated optimization evaluation value, specifically:

[0145] Corresponding to each energy consumption optimization control set and its corresponding optimization evaluation value, a mapping relationship between the energy consumption optimization control set and the optimization evaluation value is formed, that is, a point;

[0146] The energy consumption optimization-AGV display model is a multi-dimensional parameter space for intuitively presenting the distribution of different control sets in energy consumption and performance.

[0147] The mapping relationship between the energy consumption optimization control set and the optimization evaluation value is mapped to the energy consumption optimization-AGV display model, and the several points are fitted, the energy consumption optimization control set corresponding to the peak value is taken as the second energy consumption optimization control set, and is applied to AGV driving path control.

[0148] The present application scientifically sets the acceleration limit and maximum load configuration of AGV by systematically combining the path curve characteristics, terrain slope and key node load limit, and further constructs a plurality of energy consumption optimization control sets, and comprehensively evaluates and selects based on multi-dimensional features such as energy consumption, turning stability and task response speed, etc., to ensure that the optimized control parameters can realize the optimal comprehensive performance of the lowest energy consumption, high driving stability and fast task response under the premise of ensuring the safety of vehicle dynamics and the physical constraints of the path. The method effectively solves the complex coupling problem between acceleration and load, improves the safety and efficiency of AGV path execution, and enhances the adaptability and robustness of the control strategy, and promotes the efficient and stable operation of intelligent logistics vehicles.

[0149] Embodiment 2, Figure 2 An AGV driving path control system considering energy consumption optimization is given, which includes an acceleration limit initialization module, a maximum load configuration initialization module, an energy consumption optimization control set acquisition module, a feature extraction module and a screening control module.

[0150] The acceleration limit initialization module is used to obtain a first path, initialize the acceleration limit based on the curve characteristics of the first path, and obtain a first acceleration limit.

[0151] The maximum load configuration initialization module is used to estimate the maximum load according to the terrain slope and key node load demand of the first path, and obtain a first maximum load configuration.

[0152] The energy consumption optimization control set acquisition module is used to define an energy consumption optimization control set, which includes a maximum load configuration and an acceleration limit; the first maximum load configuration and the first acceleration limit are subjected to segmented proportional scaling processing and randomly combined to generate a plurality of first energy consumption optimization control sets.

[0153] The feature extraction module is used to control AGV driving according to the first energy consumption optimization control set, and to extract features from each AGV driving process to generate optimization evaluation values.

[0154] The screening control module is used for constructing an energy consumption optimization-AGV display model according to each group of first energy consumption optimization control sets and corresponding generated optimization evaluation values, performing data analysis, and screening out second energy consumption optimization control sets for application in AGV travel path control.

[0155] The application also includes an electronic device, characterized in that the electronic device comprises:

[0156] at least one processor;

[0157] and a memory in communication connection with the at least one processor;

[0158] wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the AGV travel path control method considering energy consumption optimization.

[0159] The application also includes a computer readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to implement the AGV travel path control method considering energy consumption optimization.

Claims

1. A method for controlling a travel path of an AGV with consideration of energy consumption optimization, characterized by, The method comprises the following steps: obtaining a first path, initializing an acceleration limit based on the curve characteristics of the first path, and obtaining a first acceleration limit; estimating the maximum load based on the terrain slope and key node load demand of the first path, and obtaining a first maximum load configuration; defining an energy consumption optimization control set, which includes a maximum load configuration and an acceleration limit; segmenting and scaling the first maximum load configuration and the first acceleration limit, and randomly combining to generate a plurality of first energy consumption optimization control sets; the segmentation and scaling of the first maximum load configuration and the first acceleration limit, and the random combination to generate a plurality of first energy consumption optimization control sets, specifically: setting a scaling ratio sequence of the acceleration limit, which includes a plurality of scaling factors, and generating a group of acceleration limit candidate values based on the first acceleration limit; setting a scaling ratio sequence of the maximum load configuration, which includes a plurality of scaling factors, and generating a group of maximum load configuration candidate values based on the first maximum load configuration; based on the acceleration limit candidate values and the maximum load configuration candidate values, generating a plurality of combinations of acceleration limit and maximum load configuration by random combination as the first energy consumption optimization control set; controlling the AGV to travel according to the first energy consumption optimization control set, and extracting features from each AGV travel process to generate an optimization evaluation value; constructing an energy consumption optimization-AGV display model according to each first energy consumption optimization control set and the corresponding generated optimization evaluation value, and performing data analysis to screen out a second energy consumption optimization control set for AGV travel path control.

2. The AGV travel path control method considering energy consumption optimization according to claim 1, characterized by, The method comprises the following steps: the first path is a pre-obtained initial AGV path; obtaining a sequence of spatial coordinate points of the first path; calculating the change of the angle between adjacent coordinate points on the path; based on the change of the angle, calculating the turning radius of each point on the path; extracting the minimum turning radius in the path; determining the maximum lateral acceleration range allowed according to the minimum turning radius and the vehicle lateral stability requirement; combining the vehicle longitudinal dynamic performance to set the maximum acceleration limit so that it does not exceed the maximum lateral acceleration range; the maximum acceleration limit is used as the first acceleration limit.

3. The AGV travel path control method considering energy consumption optimization according to claim 2, characterized by, The method comprises the following steps: obtaining the slope data of the first path, including the slope values of each position point on the path; identifying the maximum continuous interval of the slope in the first path and extracting the maximum slope value of the interval; combining the driving force model and the dynamics parameters of the vehicle to calculate the maximum load that the vehicle can carry safely under the condition of the maximum slope, as the maximum load value under the slope limit; obtaining the design maximum carrying capacity of all bridges in the first path, and extracting the minimum bridge carrying capacity value; obtaining the maximum load limit of all narrow passages in the path, and extracting the minimum narrow passage load limit value; From the maximum load limit of slope limit, the minimum bridge load limit and the minimum narrow channel load limit, the minimum value is selected as the maximum load limit value of the path key node.

4. The AGV travel path control method considering energy consumption optimization according to claim 3, characterized by, The features include energy consumption features, turning stability features, and task response speed features. The specific acquisition method of the energy consumption features is as follows: Real-time current and voltage data of the driving motor when the AGV executes the path task are collected. Based on the collected current and voltage data, the instantaneous electric power at each sampling time is calculated. The total energy consumption during the task is obtained by integrating the instantaneous power in the entire task execution time interval. The cumulative travel distance of the AGV during the task execution is recorded. The unit distance energy consumption is calculated by dividing the total energy consumption by the cumulative travel distance to eliminate the influence of path length difference. Load weight change data during the task are obtained, and a load weighting coefficient is calculated, which is the time average of the ratio of the load weight during the task to the maximum load. The unit distance energy consumption and the load weighting coefficient are combined, and weighted calculation is performed according to the set weight coefficient to obtain the energy consumption features.

5. The AGV travel path control method considering energy consumption optimization according to claim 4, characterized by, The specific acquisition method of the task response speed features is as follows: Response time data from receiving the dispatching instruction to actually starting the action during the AGV executing the task are collected. The completion time of each key node during the task execution is recorded. The average value of all response times and key node completion times is calculated. The standard deviation of the response time and the key node completion time is calculated to reflect the fluctuation of the response time. The response speed variation coefficient is calculated using the average value and the standard deviation as the task response speed feature.

6. The AGV travel path control method considering energy consumption optimization according to claim 5, characterized by, The energy consumption optimization-AGV display model is constructed according to each group of first energy consumption optimization control sets and corresponding generated optimization evaluation values, specifically: Each energy consumption optimization control set and its corresponding optimization evaluation value are one-to-one corresponding to form a mapping relationship between the energy consumption optimization control set and the optimization evaluation value, i.e., a point. The energy consumption optimization-AGV display model is a multi-dimensional parameter space for intuitively presenting the distribution of different control sets in energy consumption and performance. The mapping relationship between several energy consumption optimization control sets and optimization evaluation values is mapped to the energy consumption optimization-AGV display model, and several points are fitted. The energy consumption optimization control set corresponding to the peak value is taken as the second energy consumption optimization control set and applied to the AGV driving path control.

7. A system using the AGV travel path control method with consideration of energy consumption optimization according to any one of claims 1 to 6, characterized by It includes an acceleration limit initialization module, a maximum load configuration initialization module, an energy consumption optimization control set collection module, a feature extraction module, and a screening control module. The acceleration limit initialization module is used to obtain a first path, initialize the acceleration limit based on the curve characteristics of the first path, and obtain a first acceleration limit. The maximum load configuration initialization module is used to estimate the maximum load according to the terrain slope and key node load demand of the first path to obtain a first maximum load configuration. The energy consumption optimization control set collection module is used to define an energy consumption optimization control set, which includes a maximum load configuration and an acceleration limit. The first maximum load configuration and the first acceleration limit are segmented and proportionally scaled, and randomly combined to generate several first energy consumption optimization control sets. The first maximum load configuration and the first acceleration limit are segmented and scaled, and a plurality of first energy consumption optimization control sets are generated by random combination, specifically: A scaling ratio sequence of the acceleration limit is set, the ratio sequence includes a plurality of ratio factors, the ratio factors are based on the first acceleration limit, and a group of acceleration limit candidate values are generated by scaling; A scaling ratio sequence of the maximum load configuration is set, the ratio sequence includes a plurality of ratio factors, the ratio factors are based on the first maximum load configuration, and a group of maximum load configuration candidate values are generated by scaling; Based on the acceleration limit candidate values and the maximum load configuration candidate values, a plurality of combinations of acceleration limit and maximum load configuration are generated by random combination as the first energy consumption optimization control set; A feature extraction module is configured to control AGV driving according to the first energy consumption optimization control set, extract features from each AGV driving process, and evaluate and generate an optimization evaluation value; A screening control module is configured to construct an energy consumption optimization-AGV display model according to each first energy consumption optimization control set and the corresponding generated optimization evaluation value, perform data analysis, and screen out a second energy consumption optimization control set for AGV driving path control.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the AGV driving path control method considering energy consumption optimization according to any one of claims 1 to 6.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the AGV driving path control method considering energy consumption optimization according to any one of claims 1 to 6. The computer program is executed by the processor to implement the AGV driving path control method considering energy consumption optimization according to any one of claims 1 to 6.

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