Omnidirectional AGV automatic online control method and device, electronic equipment and storage medium

Abstract

The invention discloses an omnidirectional AGV automatic online control method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the preliminary screening of linear paths in a map through a preset search radius and an angle deviation threshold value, and obtaining a first candidate path set; removing the paths with projection positions exceeding the path end points to obtain a second candidate path set, performing further screening based on a minimum distance threshold to obtain a third candidate path set, scoring the paths in the third candidate path set through a preset scoring function, and determining an optimal online path; and finally, the AGV is controlled to move towards the optimal on-line path based on a path following algorithm, and on-line operation is completed and the AGV runs to the starting point or the ending point of the path. According to the invention, the characteristic of multiple motion modes of the omnidirectional AGV can be combined, and efficient, stable and automatic on-line of the omnidirectional AGV can be realized.

Description

Technical Field

[0001] This application relates to the field of AGV control technology, and in particular to an automatic online control method, device, electronic equipment and storage medium for omnidirectional AGVs, which is applicable to the automatic online control of omnidirectional mobile industrial heavy-duty AGVs with multiple motion modes. Background Technology

[0002] Omnidirectional mobile industrial heavy-duty AGVs are a type of AGV formed by adding different docking mechanisms to an omnidirectional mobile chassis. These AGVs have multiple movement modes, including forward and backward straight movement, left and right lateral movement, and diagonal movement, offering high flexibility. Currently, laser-guided AGVs of this type are widely used in scenarios such as inter-building transfers within factories and replacing production lines with vehicles.

[0003] AGV automatic deployment refers to the process of an AGV automatically moving from an area outside the path marked on the AGV work area map to the path within the map. Only AGVs that have been deployed can be scheduled by the dispatching system. Currently, existing AGV automatic deployment methods are generally designed for two-wheel differential drive AMRs and single-steering wheel drive forklift AGVs, while methods for heavy-duty omnidirectional AGVs are less common. Because heavy-duty omnidirectional AGVs are typically large, they are not suitable for large spin angles during movement, making them unsuitable for the smooth curve-based automatic deployment methods used by forklift AGVs. Another simpler method is to select the shortest straight path, rotate the vehicle to an angle parallel to the path, then move laterally onto the path, and finally straighten the drive wheels. However, this method requires frequent starts and stops, resulting in lower efficiency.

[0004] Therefore, given the characteristics of omnidirectional AGVs, there is an urgent need for an efficient, stable, and highly adaptable automatic online control method to solve the problems existing in the current technology. Summary of the Invention

[0005] The present invention aims to solve the problems of poor adaptability and low efficiency of existing automatic online methods for omnidirectional AGVs, and provides a control method and related device that can combine the characteristics of multiple motion modes of omnidirectional AGVs to achieve efficient and stable automatic online operation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution, wherein the method includes the following steps:

[0007] S1, based on the preset search radius SR and the preset angle deviation threshold φmax, filter all straight paths in the AGV work area map to obtain the first candidate path set;

[0008] S2, filter the candidate paths in the first candidate path set, and remove the candidate paths in which the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, to obtain the second candidate path set.

[0009] S3, based on the preset minimum distance threshold dmin, remove candidate paths from the second candidate path set where the distance from the vertical projection position of the AGV to be deployed on the path to the end point of the path is less than the preset minimum distance threshold dmin, and obtain the third candidate path set;

[0010] S4. All candidate paths in the third candidate path set are scored based on a preset scoring function, and the optimal online path is determined from the third candidate path set based on the scoring results.

[0011] S5, control the AGV to be launched to move towards the optimal launch path based on the preset path following algorithm until the AGV to be launched is successfully launched and runs to the start or end of the path.

[0012] Preferably, step S1 includes:

[0013] S11, with the current position of the AGV to be put into operation as the center, search all straight paths in the AGV working area map based on the preset search radius SR, and calculate the vertical distance dv from the AGV to each straight path by vector cross multiplication.

[0014] S12, calculate the deviation value |φ-φp| between the direction angle φp of each straight path and the attitude angle φ of the AGV to be put online;

[0015] S13, the straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax are summarized to form the first candidate path set.

[0016] Preferably, step S2 includes:

[0017] S21, calculate the ratio t of the projection length of the AGV to be deployed on each candidate path in the first candidate path set by the vector dot multiplication method;

[0018] S22, remove all candidate paths with t>1 from the first candidate path set to obtain the second candidate path set, where t>1 indicates that the vertical projection position of the AGV to be deployed on the path exceeds the end point of the path.

[0019] Preferably, in step S4, the preset scoring function is expressed as follows:

[0020] score_func=wd*|dv| / SR+wa*|φ-φp| / φmax+p_func

[0021] Wherein, score_func represents the candidate path score, wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and p_func represents the penalty function. When the path direction or motion pattern does not match the preset preference, p_func = 0.1, otherwise p_func = 0.

[0022] Preferably, determining the optimal online path from the third candidate path set based on the scoring results includes:

[0023] Compare the scores of all candidate paths, and determine the candidate path with the lowest score as the optimal online path;

[0024] When there are multiple candidate paths with the lowest scores, the candidate path with the smallest distance from the AGV to the end point is selected as the optimal online path.

[0025] Preferably, the preset minimum distance threshold dmin is determined as follows:

[0026] When the directional angle deviation between the AGV to be deployed and the path is 0, the minimum distance when the AGV to be deployed is completely perpendicular to the path is the preset minimum distance threshold dmin.

[0027] Preferably, between step S3 and step S4, the method further includes:

[0028] S3' When the candidate path in the third candidate path set is empty, the user is prompted that the automatic online process has failed, and the AGV to be online is moved to a new position and S1 to S3 are re-executed.

[0029] On the other hand, this application provides an omnidirectional AGV automatic online control device, the device comprising:

[0030] The first path filtering module 201 is used to filter all straight paths in the AGV work area map based on the preset search radius SR and the preset angle deviation threshold φmax to obtain the first candidate path set.

[0031] The second path filtering module 202 is used to filter the candidate paths in the first candidate path set, and remove the candidate paths in which the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, so as to obtain the second candidate path set.

[0032] The third path filtering module 203 is used to remove candidate paths from the second candidate path set whose distance from the vertical projection position of the AGV to be launched on the path to the end point of the path is less than the preset minimum distance threshold dmin, based on a preset minimum distance threshold dmin, to obtain a third candidate path set.

[0033] The optimal online path determination module 204 is used to score all candidate paths in the third candidate path set based on a preset scoring function, and determine the optimal online path from the third candidate path set according to the scoring results.

[0034] The AGV online control module 205 is used to control the AGV to be online to move towards the optimal online path based on a preset path following algorithm until the AGV to be online is successfully online and runs to the start or end of the path.

[0035] Preferably, the first path filtering module 201 is specifically used for:

[0036] Using the current position of the AGV to be deployed as the center, search all straight paths in the AGV's working area map based on the preset search radius SR, and calculate the vertical distance dv from the AGV to each straight path using the vector cross multiplication method.

[0037] Calculate the deviation value |φ-φp| between the direction angle φp of each straight path and the attitude angle φ of the AGV to be deployed;

[0038] The straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax are summarized to form the first candidate path set.

[0039] Preferably, the second path filtering module 202 is specifically used for:

[0040] The projection length ratio t of the AGV to be deployed on each candidate path in the first candidate path set is calculated by vector dot multiplication.

[0041] Remove all candidate paths with t>1 from the first candidate path set to obtain the second candidate path set, where t>1 indicates that the vertical projection position of the AGV to be deployed on the path exceeds the end point of the path.

[0042] Preferably, the preset scoring function is expressed as follows:

[0043] score_func=wd*|dv| / SR+wa*|φ-φp| / φmax+p_func

[0044] Wherein, score_func represents the candidate path score, wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and p_func represents the penalty function. When the path direction or motion pattern does not match the preset preference, p_func = 0.1, otherwise p_func = 0.

[0045] Preferably, determining the optimal online path from the third candidate path set based on the scoring results includes:

[0046] Compare the scores of all candidate paths, and determine the candidate path with the lowest score as the optimal online path;

[0047] When there are multiple candidate paths with the lowest scores, the candidate path with the smallest distance from the AGV to the end point is selected as the optimal online path.

[0048] Preferably, the preset minimum distance threshold dmin is determined as follows:

[0049] When the directional angle deviation between the AGV to be deployed and the path is 0, the minimum distance when the AGV to be deployed is completely perpendicular to the path is the preset minimum distance threshold dmin.

[0050] Preferably, the device further includes:

[0051] When the candidate paths in the third candidate path set are empty, the user is prompted that the automatic online process has failed, and the AGV to be online is moved to a new position. The first path filtering module 201, the second path filtering module 202 and the third path filtering module 203 are then controlled to work in sequence.

[0052] In another aspect, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described omnidirectional AGV automatic online control method.

[0053] In another aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described omnidirectional AGV automatic online control method.

[0054] Compared with the prior art, this application has the following beneficial effects:

[0055] 1. This invention is designed specifically for the characteristics of omnidirectional AGVs, enabling them to go online without requiring large-scale self-rotation, thus avoiding safety hazards caused by large vehicle size;

[0056] 2. By selecting the optimal deployment path through multiple rounds of screening and scoring mechanisms, the automatic deployment time is shortened, and the deployment success rate and efficiency are improved;

[0057] 3. The entire deployment process does not require additional deployment path planning. Instead, it relies on the AGV's own path following method to move to the path in the AGV's work area map. The control logic is simple and the reliability is high.

[0058] 4. It is highly adaptable and can be applied to the automatic deployment requirements of omnidirectional AGVs in different scenarios, and has broad practical value. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating an embodiment of the omnidirectional AGV automatic online control method in this application.

[0061] Figure 2 This is a schematic diagram of the vertical distance dv from the AGV to be deployed to the straight path in one embodiment of this application;

[0062] Figure 3 This is a schematic diagram of a preset minimum distance threshold dmin in one embodiment of this application;

[0063] Figure 4 This is a schematic diagram illustrating two different directional path online methods in one embodiment of this application;

[0064] Figure 5 This is a schematic diagram illustrating the online connection method of two collinear and unidirectional paths in one embodiment of this application;

[0065] Figure 6 This is a schematic diagram of an optimal online path starting in front of the AGV in one embodiment of this application.

[0066] Figure 7 This is a schematic diagram of an optimal online path starting behind the AGV in one embodiment of this application.

[0067] Figure 8 This is a schematic diagram of the structure of an omnidirectional AGV automatic online control device in one embodiment of this application;

[0068] Figure 9 This is a schematic diagram of the structure of an electronic device in one embodiment of this application. Detailed Implementation

[0069] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described below are merely illustrative. For example, the division of units and modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or modules can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical, or other forms.

[0071] In addition, each functional unit in the various embodiments of this application can be integrated into a single processor, or each unit can be a separate device, or two or more units can be integrated into a single device; each functional unit in the various embodiments of this application can be implemented in hardware or in the form of hardware plus software functional units.

[0072] Those skilled in the art will understand that all or part of the steps of the following method embodiments can be implemented by program instructions and related hardware. The aforementioned program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, they perform the steps of the following method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0073] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "a plurality of" or "several" means two or more, unless otherwise explicitly specified.

[0074] This application provides an embodiment of an omnidirectional AGV automatic online control method, such as... Figure 1 As shown, the method may include the following steps:

[0075] S1, based on the preset search radius SR and the preset angle deviation threshold φmax, filter all straight paths in the AGV work area map to obtain the first candidate path set;

[0076] S2, filter the candidate paths in the first candidate path set, and remove the candidate paths in which the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, to obtain the second candidate path set.

[0077] S3, based on the preset minimum distance threshold dmin, remove candidate paths from the second candidate path set where the distance from the vertical projection position of the AGV to be launched on the path to the end point of the path is less than the preset minimum distance threshold dmin, and obtain the third candidate path set;

[0078] S4. Score all candidate paths in the third candidate path set based on the preset scoring function, and determine the optimal online path from the third candidate path set according to the scoring results.

[0079] S5 controls the AGV to be launched to move towards the optimal launch path based on a preset path following algorithm until the AGV is successfully launched and runs to the start or end of the path.

[0080] The omnidirectional AGV automatic online control method in this embodiment determines the optimal online path through multi-step screening and scoring, and finally controls the AGV to complete the online process. Specifically, firstly, the straight paths in the map are initially screened using preset search radius and angle deviation thresholds to obtain a first candidate path set. This step ensures that the candidate paths are within the reachable distance and angle range of the AGV. Next, paths whose projection positions exceed the path endpoint are eliminated to obtain a second candidate path set, ensuring that the AGV can reasonably enter the path. Then, based on the minimum distance threshold, further screening is performed to obtain a third candidate path set, preventing the AGV from running off the path due to being too close to the endpoint during the online process. Afterwards, the paths in the third candidate path set are scored using a preset scoring function to determine the optimal online path, making the selected path more in line with the AGV's motion characteristics and actual needs. Finally, the AGV is controlled to move towards the optimal online path based on a path following algorithm, completing the online process and running to the path's start or end point.

[0081] In summary, the omnidirectional AGV automatic deployment control method of this embodiment improves the accuracy and rationality of path selection by gradually narrowing down the candidate path range through multiple rounds of screening. Simultaneously, by combining a scoring mechanism to select the optimal path, it fully considers the AGV's motion state and environmental factors, making the AGV deployment process more efficient and stable. This method avoids the problems of large-scale AGV spinning or frequent start-stop operations in existing technologies, improving the efficiency and safety of omnidirectional AGV automatic deployment, and is applicable to various omnidirectional AGV working scenarios.

[0082] In this embodiment, the preset search radius SR and the preset angle deviation threshold φmax can be set as needed.

[0083] Specifically, the preset search radius SR is the threshold range for the AGV to filter nearby candidate paths. Its determination needs to comprehensively consider the working scenario, size, and motion performance of the omnidirectional AGV. The specific principles are as follows:

[0084] 1. Coverage of reasonable offline areas

[0085] The preset search radius SR needs to cover the maximum distance from offline areas outside the AGV's working area map path to the path. For example, in the factory inter-building transfer scenario, the typical distance between AGV temporary docking points, loading and unloading areas and the main path needs to be considered; in the workshop production line scenario, the distance from the buffer area next to the production line to the production line path needs to be matched to ensure that AGVs in offline state can find a valid path.

[0086] 2. Adapt to your own athletic ability

[0087] The search radius SR should be set based on the AGV's maximum safe travel distance (such as braking distance and acceleration performance) and the distribution of obstacles in the environment. It should not be too large (to avoid including excessively long paths that reduce efficiency) nor too small (to avoid missing accessible paths). For example, for heavy-duty omnidirectional AGVs (typically large in size, such as 3-5 meters long), the preset search radius SR can be set to 1-3 times its vehicle length (e.g., 3-15 meters), and should be fine-tuned according to the factory aisle width, obstacle spacing, and other on-site environmental factors.

[0088] 3. Matching path density

[0089] If the paths within the work area are densely distributed (e.g., densely distributed production line paths within a workshop), the preset search radius SR can be appropriately reduced (e.g., within 5 meters); if the paths are sparsely distributed (e.g., long-distance transfer paths between buildings), the preset search radius SR needs to be increased accordingly (e.g., 10-15 meters) to ensure that at least one valid candidate path can be found.

[0090] Specifically, the preset angle deviation threshold φmax is a threshold that limits the range of deviation between the path direction angle and the AGV attitude angle. Its core purpose is to prevent the AGV from spinning excessively due to excessive angle deviation (a key limitation for omnidirectional AGVs). The specific principles are as follows:

[0091] 1. Based on spin confinement

[0092] Omnidirectional AGVs are heavy-duty and large in size. Large-scale spinning can easily lead to instability of the center of gravity or collisions with surrounding equipment. Therefore, the preset angle deviation threshold φmax must be strictly limited to a range that can be adjusted through its own motion mode without large-scale spinning. Usually, the preset angle deviation threshold φmax is set to a small value, generally between 15° and 30° (e.g., 20°), to ensure that the AGV can correct the angle deviation through a compound motion of "vertical translation + longitudinal translation" (rather than spinning).

[0093] 2. Ability to adapt to different sports modes

[0094] Omnidirectional AGVs can smoothly adjust their slant and lateral movement modes with small angular deviations. The preset angular deviation threshold φmax needs to match the adjustment accuracy of these modes: if the AGV's slant movement mode has high angle adjustment accuracy (e.g., stable control within ±5°), the preset angular deviation threshold φmax can be appropriately widened to 30°; if the adjustment accuracy is limited, it needs to be reduced to 15° to avoid instability in the online trajectory due to excessive deviation.

[0095] 3. Combining path direction characteristics

[0096] For paths primarily consisting of "straight forward and backward movement", the preset angle deviation threshold φmax can be slightly relaxed (e.g., 30°), as the AGV can correct deviations through straight movement plus small-angle lateral movement. For paths primarily consisting of "lateral movement" (which are more sensitive to angles), the preset angle deviation threshold φmax needs to be more stringent (e.g., 15°) to avoid directional deviations during lateral movement.

[0097] In summary, the preset search radius SR is based on the principles of "covering offline areas, adapting to motion capabilities, and matching path density"; the preset angle deviation threshold φmax is based on the principle of "avoiding large-scale spins and adapting to motion modes". The specific values ​​of both need to be determined through debugging and optimization in actual applications, taking into account the on-site environment (such as obstacles and path distribution) and AGV parameters (such as size and motion accuracy).

[0098] In one embodiment, step S1 includes:

[0099] S11, with the current position of the AGV to be launched as the center, search all straight paths in the AGV working area map based on the preset search radius SR, and calculate the vertical distance dv from the AGV to each straight path through the vector cross multiplication method.

[0100] S12, calculate the deviation value |φ-φp| between the direction angle φp of each straight path and the attitude angle φ of the AGV to be deployed;

[0101] S13. Summarize the straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax to form the first candidate path set.

[0102] In this embodiment, step S11 uses the current position of the AGV as the center, searches all straight paths based on a preset search radius SR, and calculates the vertical distance dv from the AGV to each straight path using the vector cross product method. For example... Figure 2 As shown, the vector cross product is calculated as follows: Let the starting point of the path be s, the ending point be e, and the position of the AGV be point a. The vector... Let be a vector from s to e. Let be the vector from s to a, then the perpendicular distance is... Where “×” represents the cross product of vectors, and |se| represents the vector cross product. The modulus of dv is positive, indicating that it is on the left side of path se, and negative dv indicates that it is on the right side of path se. Step S12 calculates the deviation value |φ-φp| between the direction angle φp and the attitude angle φ of each straight path. The direction angle φp of the path is calculated using the coordinates of the starting point and the ending point of the path, i.e., φp=atan2(ey-sy,ex-sx), where (sx,sy) are the coordinates of the starting point s, (ex,ey) are the coordinates of the ending point e, and the attitude angle φ of the AGV is the current orientation angle of the AGV. Step S13 summarizes the straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax to form the first candidate path set.

[0103] This embodiment calculates the vertical distance using the vector cross product method, accurately determining the distance relationship between the AGV and each straight path, providing a precise distance basis for subsequent path selection. Calculating the deviation between the path direction angle and the AGV attitude angle allows for the selection of paths similar to the AGV's current orientation, reducing turning operations during deployment and improving deployment efficiency. This selection method ensures that the paths in the first candidate path set are feasible in terms of both distance and angle, laying a solid foundation for further selection.

[0104] In one embodiment, step S2 includes:

[0105] S21, calculate the ratio t of the projected lengths of the AGV to be deployed on each candidate path in the first candidate path set by using the vector dot multiplication method;

[0106] S22, remove all candidate paths with t>1 from the first candidate path set to obtain the second candidate path set, where t>1 indicates that the vertical projection position of the AGV to be launched on the path exceeds the end point of the path.

[0107] In this embodiment, step S21 calculates the ratio t of the projected lengths of the AGV on each candidate path in the first candidate path set using the vector dot multiplication method, such as... Figure 2 As shown, the specific calculation method is as follows: Let point p be the vertical projection point of the AGV onto the path, and the vector... Let the vector be the path from the starting point to the ending point. Let be the vector from the starting point of the path to the current position of the AGV, then the projection length of the AGV on the path is... Further normalization is performed, and t represents the ratio of the AGV's projected length on the path to the total path length (projected length ratio). Then the projected length ratio... Where “·” represents vector dot product. In step S22, when t > 1, it means that the vertical projection position of the AGV on the path exceeds the end point of the path. Such paths are removed from the first candidate path set to obtain the second candidate path set.

[0108] This embodiment calculates the projection length ratio t using vector dot multiplication, accurately determining whether the AGV's projection position on the path is within the path's range. Paths whose projection positions exceed the path's endpoint are eliminated, preventing the AGV from selecting paths it cannot normally enter, thus ensuring the feasibility of subsequent deployment. This step further optimizes the candidate path set, improving the rationality and effectiveness of path selection.

[0109] In one embodiment, in step S4, the preset scoring function is expressed as follows:

[0110] score_func=wd*|dv| / SR+wa*|φ-φp| / φmax+p_func

[0111] Wherein, score_func represents the candidate path score, wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and p_func represents the penalty function. When the path direction or motion pattern does not match the preset preference, p_func = 0.1, otherwise p_func = 0.

[0112] Among all candidate paths, some move forward, while others move backward. Some paths move in a forward-backward pattern, while others move laterally. Furthermore, the AGV is far from some paths but has a small angular deviation from them, while it is close to some paths but has a large angular deviation from them. How do we select the optimal path? This embodiment proposes the following method: designing a scoring function `score_func`, where a lower score indicates a better path.

[0113] Design a horizontal distance weighting coefficient (wd), an angle deviation weighting coefficient (wa), and a penalty function (p_func). When the user wants to prioritize the path with the shortest distance, increase the value of wd and decrease the value of wa, for example, wd = 0.6, wa = 0.4; when the user wants to prioritize the path with the smallest angle deviation, increase the value of wa and decrease the value of wd, for example, wd = 0.4, wa = 0.6; if both are equally important, they can be designed to have the same value, for example, wa = wd = 0.5.

[0114] When the path's movement pattern or direction does not conform to the user's expectations, the penalty function p_func is used to penalize it, thereby increasing the path's overall score.

[0115] In this embodiment, the scoring function is score_func = wd*|dv| / SR + wa*|φ-φp| / φmax + p_func, where wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and wd+wa=1. p_func represents the penalty function. The purpose of |dv| / SR and |φ-φp| / φmax is to perform normalization processing so that their values ​​are all within the range of [0,1], thereby avoiding the influence of different units on the weights. When the path direction or motion pattern does not conform to the preset preference, p_func = 0.1; otherwise, p_func = 0. This scoring function can comprehensively consider factors such as the vertical distance from the AGV to the path, the deviation between the AGV attitude angle and the path direction angle, and whether the path conforms to the preset preference, to score the candidate path.

[0116] The scoring function in this embodiment integrates multiple factors affecting the efficiency and stability of AGV deployment. By setting weight coefficients, the importance of each factor can be adjusted according to actual needs. The introduction of a penalty function enables the AGV to prioritize paths that conform to preset preferences, improving the smoothness of the deployment process. By scoring candidate paths using this scoring function, the optimal deployment path can be selected more scientifically and rationally, further improving the efficiency and reliability of AGV automatic deployment.

[0117] like Figure 4 As shown, there are two paths, se and es, where se is the forward direction and es is the backward direction. The AGV's lateral distance and angular deviation from these two paths are the same, therefore the scores for the wd*|dv| / SR+wa*|φ-φp| / φmax portion of score_func are the same. Assume the user wants to prioritize going online via the forward path. Since es is the backward path, it will receive 0.1 more points in p_func, resulting in a higher total score than the se path. Therefore, the lower-scoring se path will be selected as the online path from among the two candidate paths.

[0118] Reference Figure 4 On the right side of the map, the AGV should move from point s to point e using a forward / backward movement pattern, and then move from point e to point b using a left / right lateral movement pattern. If the AGV is located as shown in the diagram, the vertical distance from the AGV to path se and path eb is the same, and the angular deviation from paths se and eb is also the same. The first two terms of the scoring function for both paths are the same: wd*|dv| / SR+wa*|φ-φp| / φmax. Suppose the user wants to prioritize the path using the forward / backward movement pattern. Since eb is a path using the left / right lateral movement pattern, it will receive 0.1 more points in the p_func term, resulting in a higher total score than the se path. Therefore, the lower-scoring se path will be selected as the online path among these two candidate paths.

[0119] In one embodiment, determining the optimal deployment path from the third candidate path set based on the scoring results includes:

[0120] Compare the scores of all candidate paths and determine the candidate path with the lowest score as the optimal online path;

[0121] When there are multiple candidate paths with the lowest scores, the candidate path with the shortest distance from the AGV to the end point is selected as the optimal path for deployment.

[0122] In this embodiment, when determining the optimal deployment path based on the scoring results, the scores of all candidate paths are first compared, and the candidate path with the lowest score is determined as the optimal deployment path. When there are multiple candidate paths with the lowest and the same score, the candidate path with the shortest distance from the AGV to the path endpoint is selected as the optimal deployment path.

[0123] Choosing the path with the lowest score as the optimal deployment path ensures that it is the most suitable path for AGV deployment after comprehensively considering various factors. When multiple paths with the same score exist, selecting the path with the shortest distance to the destination reduces the distance the AGV travels after deployment, further improving overall efficiency. This method of determining the optimal path makes path selection more accurate and reasonable, meeting the needs of practical applications.

[0124] In one embodiment, the preset minimum distance threshold dmin is determined as follows:

[0125] When the directional angle deviation between the AGV to be launched and the path is 0, the minimum distance when the AGV to be launched is completely perpendicular to the path is the preset minimum distance threshold dmin.

[0126] During the deployment of AGVs, their movement trajectory is typically curved. The translational movement of an AGV is a combination of vertical translation towards the path and longitudinal translation towards the path's endpoint. Therefore, if the projection point of the AGV on the path is too close to the endpoint, it may exceed the path's endpoint during deployment.

[0127] To avoid this situation, a preset minimum distance threshold dmin is set. For example... Figure 3 As shown, the distance from the projection point of the AGV on the path to the destination is pe. Traverse all candidate paths, and if pe < dmin, then remove it from the candidate path.

[0128] It should be noted that dmin is related to the AGV's own following algorithm and needs to be determined based on specific circumstances. This embodiment proposes a simple empirical method for determining the dmin value: when the directional angle deviation between the AGV and the path is 0, the minimum distance at which the AGV approaches the path completely perpendicularly is the preset minimum distance threshold dmin. This distance is determined based on factors such as the AGV's size, motion characteristics, and the accuracy of the path following algorithm, ensuring that the AGV will not collide with the path endpoint or exceed the path range when approaching the path perpendicularly.

[0129] The preset minimum distance threshold dmin determined in this embodiment ensures that the AGV maintains a sufficient safe distance from the path endpoint during the deployment process, preventing the AGV from running off the path due to being too close to the endpoint, thus improving the safety and reliability of the deployment process. At the same time, the determination of this threshold takes into account the actual characteristics of the AGV, making the selected path more consistent with the AGV's movement requirements.

[0130] like Figure 3 As shown, the AGV is a dual-steering-wheel type. To correct the lateral distance deviation dv, when |dv|≥dmin, the steering wheels will be completely perpendicular to the path, and the AGV will move perpendicularly towards the path se.

[0131] In one embodiment, between step S3 and step S4, the method further includes:

[0132] S3': When the candidate paths in the third candidate path set are empty, the user is prompted that the automatic online process has failed, and the AGV to be online is moved to a new position and S1 to S3 are re-executed.

[0133] This embodiment adds an exception handling step S3'. When the candidate paths in the third candidate path set are empty, it means that there are no suitable paths for the AGV to go online after the first three steps of screening. At this time, the system prompts the user that the AGV has failed to go online automatically, and controls the AGV to be put online to move to a new position. Then, steps S1 to S3 are executed again to screen paths again.

[0134] This exception handling procedure improves the system's fault tolerance and flexibility. When no suitable candidate path is available, prompting the user and controlling the AGV to move to a new location for re-selection increases the likelihood of successful AGV deployment and avoids the entire deployment process being terminated due to the lack of a suitable path. This makes the AGV's automatic deployment process more intelligent and automated, improving the system's usability.

[0135] Specifically, the new location can be automatically determined according to pre-set rules (such as specific movement direction and movement distance), or it can be reset by the user after receiving a prompt that the online connection has failed.

[0136] In one embodiment, the specific method for controlling the AGV to be launched to move towards the optimal launch path based on a preset path following algorithm in step S5 until the AGV to be launched successfully goes online and runs to the start or end of the path is as follows:

[0137] After selecting the optimal online path using steps S1-S4, the AGV automatically moves onto the selected path based on its own path-following motion control algorithm. At this time, the following control algorithm corrects the AGV's angular and distance deviations. When both the distance and angular deviations are reduced to within the allowable threshold range, the AGV is successfully online. If the AGV has not yet reached the starting point of the path (the starting point is in front of the AGV), it will automatically move to the starting point and stop; if the AGV has not yet reached the ending point of the path (the starting point is behind the AGV), it will automatically move to the ending point and stop.

[0138] like Figure 6 As shown, a path ec near the AGV is selected as the optimal online path. At this time, the AGV's projected position p is located before the path's starting point e. The AGV starts online from position 1. When it moves to position 2, the online connection is successful. At this point, the AGV has not yet reached the path's starting point, so the AGV automatically runs to the starting point e and stops.

[0139] like Figure 7 As shown, a path ec near the AGV is selected as the online path. At this time, the AGV's projected position p is located in the middle of path ec. The AGV starts online from position 1. When it moves to position 2, the online connection is successful. At this point, the AGV has not yet reached the end of the path, so the AGV automatically runs to the end point c and stops.

[0140] It should be noted that since the dmin design value is not an exact value, there may be a situation where the AGV has not been successfully launched but has already exceeded the end point of the currently selected launch path. In this case, return to step S1 and start again.

[0141] like Figure 8 As shown in the figure, this application embodiment also provides an omnidirectional AGV automatic online control device, which may include:

[0142] The first path filtering module is used to filter all straight paths in the AGV work area map based on the preset search radius SR and the preset angle deviation threshold φmax to obtain the first candidate path set.

[0143] The second path filtering module is used to filter the candidate paths in the first candidate path set, and remove the candidate paths where the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, so as to obtain the second candidate path set.

[0144] The third path filtering module is used to remove candidate paths from the second candidate path set that are less than the preset minimum distance threshold dmin from the vertical projection position of the AGV to be launched on the path to the end point of the path, based on the preset minimum distance threshold dmin, so as to obtain the third candidate path set.

[0145] The optimal online path determination module is used to score all candidate paths in the third candidate path set based on a preset scoring function, and determine the optimal online path from the third candidate path set based on the scoring results.

[0146] The AGV online control module is used to control the AGV to be online to move towards the optimal online path based on a preset path following algorithm until the AGV to be online is successfully online and runs to the start or end of the path.

[0147] The omnidirectional AGV automatic online control device in this embodiment achieves automatic online control of the omnidirectional AGV through the coordinated work of various modules. Specifically, the first path filtering module filters straight paths in the map based on a preset search radius and angle deviation threshold to obtain a first candidate path set; the second path filtering module removes paths whose projection positions exceed the path endpoint to obtain a second candidate path set; the third path filtering module further filters based on a minimum distance threshold to obtain a third candidate path set; the optimal online path determination module scores the paths in the third candidate path set using a preset scoring function to determine the optimal online path; and the AGV online control module controls the AGV to move towards the optimal online path based on a path following algorithm to complete the online process.

[0148] This omnidirectional AGV automatic online control device employs a modular design, breaking down the automatic online control process into multiple independent functional modules. These modules have clearly defined roles and work collaboratively, improving the device's maintainability and scalability. Each module is specifically responsible for a particular function, efficiently completing its assigned task and ensuring the efficient and stable operation of the entire automatic online control process. This device is adaptable to the characteristics of omnidirectional AGVs, achieving efficient and safe automatic online operation and meeting the application needs of various scenarios.

[0149] In one embodiment, the first path filtering module is specifically used for:

[0150] Using the current position of the AGV to be deployed as the center, search all straight paths in the AGV's work area map based on the preset search radius SR, and calculate the vertical distance dv from the AGV to each straight path using the vector cross multiplication method.

[0151] Calculate the deviation value |φ-φp| between the direction angle φp of each straight path and the attitude angle φ of the AGV to be deployed.

[0152] The straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax are summarized to form the first candidate path set.

[0153] In one embodiment, the second path filtering module is specifically used for:

[0154] The ratio t of the projected length of the AGV to be deployed on each candidate path in the first candidate path set is calculated by vector dot multiplication.

[0155] Remove all candidate paths with t>1 from the first candidate path set to obtain the second candidate path set, where t>1 indicates that the vertical projection position of the AGV to be deployed on the path exceeds the end point of the path.

[0156] In one embodiment, the preset scoring function is represented as follows:

[0157] score_func=wd*|dv| / SR+wa*|φ-φp| / φmax+p_func

[0158] Wherein, score_func represents the candidate path score, wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and p_func represents the penalty function. When the path direction or motion pattern does not match the preset preference, p_func = 0.1, otherwise p_func = 0.

[0159] In one embodiment, determining the optimal deployment path from the third candidate path set based on the scoring results includes:

[0160] Compare the scores of all candidate paths and determine the candidate path with the lowest score as the optimal online path;

[0161] When there are multiple candidate paths with the lowest scores, the candidate path with the shortest distance from the AGV to the end point is selected as the optimal path for deployment.

[0162] In one embodiment, the preset minimum distance threshold dmin is determined as follows:

[0163] When the directional angle deviation between the AGV to be launched and the path is 0, the minimum distance when the AGV to be launched is completely perpendicular to the path is the preset minimum distance threshold dmin.

[0164] In one embodiment, the device further includes:

[0165] When the candidate paths in the third candidate path set are empty, the user is prompted that the automatic online process has failed, and the AGV to be online is moved to a new position. The first path filtering module, the second path filtering module, and the third path filtering module are then controlled to work in sequence.

[0166] It should be noted that the omnidirectional AGV automatic online control device in the above embodiments has the same working principle and technical effect as the omnidirectional AGV automatic online control method in the above embodiments, and will not be repeated here.

[0167] like Figure 9 As shown in the embodiments of this application, an electronic device is also provided, which may include:

[0168] The system includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and executable on the processor 302. The memory 301 and the processor 302 communicate with each other via a bus 304. When the processor 302 executes the computer program 303, it implements the steps of the omnidirectional AGV automatic online control method as described in the above-described method embodiments of this application.

[0169] In this embodiment, electronic device 3 provides a hardware platform for the implementation of the omnidirectional AGV automatic online control method. Through the execution of computer programs by the processor, it can quickly and accurately complete various calculation and control tasks, ensuring the real-time performance and reliability of the automatic online process. The electronic device's storage function can store information such as AGV working area maps, preset parameters, and computer programs, facilitating easy retrieval and updates. This electronic device can be integrated into the control system of the omnidirectional AGV, providing strong support for the automatic online deployment of AGVs.

[0170] Specifically, the electronic device 3 can be an intelligent device with memory and processor, such as an industrial control computer, PC, or smart mobile terminal, or a computer component with memory and processor, such as a CPU or GPU. In this embodiment, the electronic device 3 is the ECU module of the AGV.

[0171] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the omnidirectional AGV automatic online control method described in the above embodiments.

[0172] In this embodiment, the computer-readable storage medium facilitates the storage and dissemination of computer programs, enabling the omnidirectional AGV automatic online control method to be easily ported and used across different electronic devices, thus improving the method's versatility and scalability. Storing the program on the storage medium also allows for backup and updates, ensuring the program's security and integrity. This storage medium provides a convenient carrier for implementing the omnidirectional AGV automatic online control method, facilitating its widespread application.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0174] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this application.

[0175] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0176] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. This application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatic online control of omnidirectional AGVs, characterized in that, The method includes the following steps: S1, based on the preset search radius SR and the preset angle deviation threshold φmax, filter all straight paths in the AGV working area map to obtain the first candidate path set; S2, filter the candidate paths in the first candidate path set, and remove the candidate paths in which the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, to obtain the second candidate path set. S3, based on the preset minimum distance threshold dmin, remove candidate paths from the second candidate path set where the distance from the vertical projection position of the AGV to be deployed on the path to the end point of the path is less than the preset minimum distance threshold dmin, and obtain the third candidate path set; S4. All candidate paths in the third candidate path set are scored based on a preset scoring function, and the optimal online path is determined from the third candidate path set based on the scoring results. S5, control the AGV to be launched to move towards the optimal launch path based on the preset path following algorithm until the AGV to be launched is successfully launched and runs to the start or end of the path.

2. The omnidirectional AGV automatic online control method according to claim 1, characterized in that, Step S1 includes: S11, with the current position of the AGV to be put into operation as the center, search all straight paths in the AGV working area map based on the preset search radius SR, and calculate the vertical distance dv from the AGV to each straight path by vector cross multiplication. S12, calculate the deviation value |φ-φp| between the direction angle φp of each straight path and the attitude angle φ of the AGV to be put online; S13, the straight paths that satisfy |dv|≤SR and |φ-φp|≤φmax are summarized to form the first candidate path set.

3. The omnidirectional AGV automatic online control method according to claim 1, characterized in that, Step S2 includes: S21, calculate the ratio t of the projection length of the AGV to be deployed on each candidate path in the first candidate path set by the vector dot multiplication method; S22, remove all candidate paths with t>1 from the first candidate path set to obtain the second candidate path set, where t>1 indicates that the vertical projection position of the AGV to be deployed on the path exceeds the end point of the path.

4. The omnidirectional AGV automatic online control method according to claim 2, characterized in that, In step S4, the preset scoring function is expressed as follows: score_func=wd*|dv| / SR+wa*|φ-φp| / φmax+p_func Wherein, score_func represents the candidate path score, wd represents the lateral distance weight coefficient, wa represents the angle deviation weight coefficient, and p_func represents the penalty function. When the path direction or motion pattern does not match the preset preference, p_func = 0.1, otherwise p_func = 0.

5. The omnidirectional AGV automatic online control method according to claim 4, characterized in that, In step S4, determining the optimal online path from the third candidate path set based on the scoring results includes: Compare the scores of all candidate paths, and determine the candidate path with the lowest score as the optimal online path; When there are multiple candidate paths with the lowest scores, the candidate path with the smallest distance from the AGV to the end point is selected as the optimal online path.

6. The omnidirectional AGV automatic online control method according to claim 1, characterized in that, In step S3, the preset minimum distance threshold dmin is determined as follows: When the directional angle deviation between the AGV to be deployed and the path is 0, the minimum distance when the AGV to be deployed is completely perpendicular to the path is the preset minimum distance threshold dmin.

7. The omnidirectional AGV automatic online control method according to any one of claims 1-6, characterized in that, Between step S3 and step S4, the method further includes: S3' When the candidate path in the third candidate path set is empty, the user is prompted that the automatic online process has failed, and the AGV to be online is moved to a new position and S1 to S3 are re-executed.

8. An omnidirectional AGV automatic online control device, characterized in that, The device includes: The first path filtering module is used to filter all straight paths in the AGV work area map based on the preset search radius SR and the preset angle deviation threshold φmax to obtain the first candidate path set. The second path filtering module is used to filter the candidate paths in the first candidate path set, and remove the candidate paths in which the vertical projection position of the AGV to be launched on the path exceeds the end point of the path, so as to obtain the second candidate path set. The third path filtering module is used to remove candidate paths from the second candidate path set that are less than the preset minimum distance threshold dmin, based on a preset minimum distance threshold dmin, to obtain a third candidate path set. The optimal online path determination module is used to score all candidate paths in the third candidate path set based on a preset scoring function, and determine the optimal online path from the third candidate path set according to the scoring results. The AGV online control module is used to control the AGV to be online to move towards the optimal online path based on a preset path following algorithm until the AGV to be online is successfully online and runs to the start or end of the path.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the omnidirectional AGV automatic online control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the omnidirectional AGV automatic online control method as described in any one of claims 1-7.

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