Intelligent control method for guided vehicle cruise

Abstract

This invention provides a guided vehicle cruise intelligent control method, comprising the following steps: S1. Obtaining obstacle distribution information in the working area of ​​the guided vehicle and planning a feasible path for the guided vehicle in the working area; S2. Obtaining the current self-information and charge information of the guided vehicle, and determining the remaining driving range of the guided vehicle based on the load information and charge information; S3. Determining the optimal path for the guided vehicle based on its current driving range, and controlling the guided vehicle to travel along the optimal path; During the execution of a task, the guided vehicle can determine the optimal driving path based on its own load and charge information, thereby ensuring that the guided vehicle can continuously and stably complete the task without human intervention, effectively improving the task execution efficiency of the guided vehicle and reducing labor costs.

Description

Technical Field

[0001] This invention relates to the field of automated guided vehicles, and more particularly to an intelligent control method for cruise control of guided vehicles. Background Technology

[0002] An Automated Guided Vehicle (AGV) is a battery-powered auxiliary loading mechanism used to transport a target to its destination.

[0003] Automated Guided Vehicles (AGVs) require path planning during operation to ensure stable movement. Current technologies rely on obstacle distribution within their work area and employ algorithms like Floyd-Warshall for path planning. While these methods can determine the path, they suffer from several drawbacks: They assume the AGV is in good charge condition. However, if the charge level is insufficient for complete path completion, task execution is interrupted. Current technologies rely on automatic battery monitoring and alerts; insufficient charge interrupts the task. If the AGV is already engaged in a task, human intervention is required, leading to inefficiency.

[0004] Therefore, in order to solve the above-mentioned technical problems, it is urgent to propose a new technical approach. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a cruise intelligent control method for a guided vehicle, which can determine the optimal driving path based on its own load and charge information during the execution of a task, thereby ensuring that the guided vehicle can continuously and stably complete the task without human intervention, which can effectively improve the task execution efficiency of the guided vehicle and reduce labor costs.

[0006] The present invention provides a guided vehicle cruise intelligent control method, comprising the following steps:

[0007] S1. Obtain obstacle distribution information in the working area of ​​the guide vehicle and plan a feasible path for the guide vehicle in the working area;

[0008] S2. Obtain the current information of the guided vehicle itself and its charge information, and determine the remaining driving range of the guided vehicle based on the load information and charge information;

[0009] S3. Determine the optimal path for the current guiding vehicle based on its current driving range, and control the guiding vehicle to travel along the optimal path.

[0010] Furthermore, the feasible path planning for the guided vehicle within the work area specifically includes:

[0011] Obtain the distribution of charging piles in the working area of ​​the guide vehicle, the starting position of the guide vehicle, and the ending position of the guide vehicle;

[0012] Determine the distance of each feasible path between the starting position and the ending position of the guide vehicle, and sort the feasible paths in ascending order of distance to form a set of feasible paths;

[0013] Calculate the distances between the starting and ending points of the guide vehicle and the locations of the charging piles, and sort the distances between the starting and ending points of the guide vehicle and the locations of the charging piles in ascending order to form a set of conditions.

[0014] Furthermore, in step S2, the self-load information of the guide vehicle includes the unloaded weight of the guide vehicle, the real-time load weight of the guide vehicle, and the transmission efficiency of the guide vehicle.

[0015] The charge information of the guided vehicle includes the remaining battery power of the current guided vehicle and the battery discharge efficiency.

[0016] Furthermore, in step S2, the remaining driving range of the guided vehicle is determined according to the following method:

[0017]

[0018] Where A is the mileage coefficient, B is the transmission efficiency constant, η1 is the discharge coefficient of the guide vehicle's battery, η2 is the transmission efficiency of the guide vehicle, β is the load coefficient of the guide vehicle, F1 is the wind resistance of the guide vehicle, F2 is the rolling resistance of the guide vehicle, and t is the ambient temperature of the guide vehicle's working area.

[0019] Furthermore, the wind resistance of the guided vehicle is determined using the following method:

[0020] Among them, S R For the frontal area of ​​the guide vehicle, c d γ is the drag coefficient, v is the speed of the guided vehicle, and γ is the wind resistance calculation constant.

[0021] Furthermore, the rolling resistance of the guide car is determined using the following method:

[0022] F2 = Mf, where M is the total weight of the guide car and f is the rolling resistance coefficient.

[0023] Furthermore, determining the optimal route for the current guided vehicle based on its current driving range specifically includes:

[0024] S31. Determine whether the current driving range S of the guided vehicle is less than the shortest path in the set of feasible paths. If not, take the shortest path in the current set of feasible paths as the optimal path; if so, proceed to step S32.

[0025] S32. Filter out the paths from the set of conditions where the distance between the starting point and the charging station is less than the driving range S, and proceed to step S33;

[0026] S33. Determine whether the selected charging piles are currently charging. If not, take the path with the shortest distance between the current starting position of the guide vehicle and the charging pile as the best path. If yes, proceed to step S34.

[0027] S34. Calculate the remaining charging time Ti for each selected charging station, calculate the travel time Tsi from the current guide vehicle to each selected charging station, and calculate the judgment time T: T = Ti - Tsi, and sort T; select the charging station with the smallest judgment time, and take the current starting position - charging station - destination position as the best path.

[0028] The beneficial effects of this invention are as follows: Through this invention, the guided vehicle can determine the optimal driving path based on its own load and charge information when performing tasks, thereby ensuring that the guided vehicle can continuously and stably complete the tasks without human intervention, which can effectively improve the task execution efficiency of the guided vehicle and reduce labor costs. Attached Figure Description

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0030] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0031] The present invention will be further described below:

[0032] The present invention provides a guided vehicle cruise intelligent control method, comprising the following steps:

[0033] S1. Obtain obstacle distribution information in the working area of ​​the guide vehicle and plan a feasible path for the guide vehicle in the working area;

[0034] S2. Obtain the current information of the guided vehicle itself and its charge information, and determine the remaining driving range of the guided vehicle based on the load information and charge information;

[0035] S3. Determine the optimal path for the current guided vehicle based on its current driving range, and control the guided vehicle to travel along the optimal path; during the execution of the task, the guided vehicle can determine the optimal driving path based on its own load and charge information, thereby ensuring that the guided vehicle can continuously and stably complete the task without human intervention, which can effectively improve the task execution efficiency of the guided vehicle and reduce labor costs.

[0036] In this embodiment, the feasible path planning for the guided vehicle in the work area specifically includes:

[0037] Obtain the distribution of charging piles in the working area of ​​the guide vehicle, the starting position of the guide vehicle, and the ending position of the guide vehicle;

[0038] Determine the distance of each feasible path between the starting position and the ending position of the guide vehicle, and sort the feasible paths in ascending order of distance to form a set of feasible paths;

[0039] Calculate the distances between the starting and ending points of the guide vehicle and the locations of the charging piles, and sort the distances between the starting and ending points of the guide vehicle and the locations of the charging piles in ascending order to form a set of conditions.

[0040] In this embodiment, in step S2, the self-load information of the guide vehicle includes the unloaded weight of the guide vehicle, the real-time load weight of the guide vehicle, and the transmission efficiency of the guide vehicle.

[0041] The charge information of the guided vehicle includes the remaining battery power of the current guided vehicle and the battery discharge efficiency.

[0042] In this embodiment, in step S2, the remaining driving range of the guided vehicle is determined according to the following method:

[0043]

[0044] Where A is the mileage coefficient, B is the transmission efficiency constant, η1 is the discharge coefficient of the guide vehicle's battery, η2 is the transmission efficiency of the guide vehicle, β is the load coefficient of the guide vehicle, F1 is the wind resistance of the guide vehicle, F2 is the rolling resistance of the guide vehicle, and t is the ambient temperature of the guide vehicle's working area.

[0045] Specifically: The wind resistance of the guided vehicle is determined according to the following method:

[0046] Among them, S R For the frontal area of ​​the guide vehicle, c d γ is the drag coefficient, v is the speed of the guided vehicle, and γ is the wind resistance calculation constant.

[0047] The rolling resistance of the guided vehicle is determined using the following method:

[0048] F2 = Mf, where M is the total weight of the guide car and f is the rolling resistance coefficient.

[0049] In this embodiment, determining the optimal path for the current guided vehicle based on its current driving range specifically includes:

[0050] S31. Determine whether the current driving range S of the guided vehicle is less than the shortest path in the set of feasible paths. If not, take the shortest path in the current set of feasible paths as the optimal path; if so, proceed to step S32.

[0051] S32. Filter out the paths from the set of conditions where the distance between the starting point and the charging station is less than the driving range S, and proceed to step S33;

[0052] S33. Determine whether the selected charging piles are currently charging. If not, take the path with the shortest distance between the current starting position of the guide vehicle and the charging pile as the best path. If yes, proceed to step S34.

[0053] S34. Calculate the remaining charging time Ti for each selected charging pile, calculate the travel time Tsi from the current guide vehicle to each selected charging pile, and calculate the judgment time T: T = Ti - Tsi, and sort T; select the charging pile with the smallest judgment time, and take the current starting position - charging pile - destination position as the best path, where i represents the i-th charging pile among the selected charging piles.

[0054] The following is a specific example to further illustrate this point:

[0055] Assume the current guided vehicle is vehicle A:

[0056] The starting point is A, the ending point is D, and there are 5 charging piles in the working area of ​​the guide vehicle, namely H, I, J, K, and L;

[0057] Using the above method, the set of feasible paths is determined based on the obstacles in the work area. There are N feasible paths in total, which are (line1, line2, line3, ..., lineN) and have been arranged in ascending order.

[0058] The distances from starting point A to each charging station are ordered as (AH, AJ, AI, AK, AL).

[0059] So, if the remaining driving range S of the current guided vehicle is greater than line1, then line1 will be the best driving route. Of course, if it is greater than any path in the set of feasible paths, then line1 will be the best driving route.

[0060] If the remaining range S is less than line1, then we need to select from the set of conditions. If the remaining range is less than AK but greater than AI, then AH, AJ, and AI are further candidates.

[0061] If no guide vehicle is currently charging at charging stations H, J, and I, then AH is the optimal path, i.e., the optimal path is AHD. The guide vehicle travels to charging station H to charge, and then travels from H to D. If all the alternative charging stations are currently being charged by the guide vehicle, then it is necessary to calculate the remaining charging time Ti for charging stations H, J, and I, where i = 1, 2, 3, and i = 1 for charging station H, i = 2 for charging station J, and i = 3 for charging station I.

[0062] Then, the travel time Tsi from the guide vehicle to these three charging stations is calculated. The travel time is compared with the remaining charging time, and the difference is calculated to obtain the judgment time T. If T = 0, it means that when the guide vehicle arrives at the charging station, the charging station has just finished charging the previous guide vehicle. If T < 0, it means that the time it takes for the guide vehicle to arrive at the charging station is less than the remaining charging time of the charging station. Conversely, if T = 0, the travel time of the guide vehicle is greater than the remaining charging time of the charging station. In this case, the charging station with the smallest T value is taken as the path, that is, the path from the starting point, the charging station with the smallest T value, and the destination is taken as the optimal path.

[0063] Of course, for greater accuracy, further judgment can be made:

[0064] Calculate the charging time Tc required for the current guided vehicle;

[0065] Calculate the travel time Tmi of the guided vehicle from charging stations T=0 and T<0 to the destination, calculate the waiting time Td after the guided vehicle arrives at the charging station T<0, and calculate the total time Tzi required for the guided vehicle to travel from the starting point to the charging station and then to the destination. For example:

[0066] When traveling from A to H, T < 0; when traveling from A to J, T = 0; and when traveling from A to I, T > 0. In this case, we only need to determine the total time required for AHD and AJD. For AHD: Tz1 = t1 + Td + Tc + Tm1; for AJD: Tz2 = t2 + Tc + Tm2. If Tz1 < Tz2, then AHD is the best path; if Tz1 > Tz2, then AJD is the best path; if they are equal, then either one can be chosen.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent control of guided vehicle cruise, characterized in that: Includes the following steps: S1. Obtain obstacle distribution information in the working area of ​​the guide vehicle and plan a feasible path for the guide vehicle in the working area; S2. Obtain the current information of the guided vehicle itself and its charge information, and determine the remaining driving range of the guided vehicle based on the load information and charge information; specifically: Determine the remaining driving range of the guided vehicle using the following method: Where A is the mileage coefficient, B is the transmission efficiency constant, η1 is the discharge coefficient of the guide vehicle's battery, η2 is the transmission efficiency of the guide vehicle, β is the load coefficient of the guide vehicle, F1 is the wind resistance of the guide vehicle, F2 is the rolling resistance of the guide vehicle, and t is the ambient temperature of the guide vehicle's working area. S3. Determine the optimal path for the current guiding vehicle based on its current driving range, and control the guiding vehicle to travel along the optimal path; The feasible path planning for the guided vehicle in the work area specifically includes: Obtain the distribution of charging piles in the working area of ​​the guide vehicle, the starting position of the guide vehicle, and the ending position of the guide vehicle; Determine the distance of each feasible path between the starting position and the ending position of the guide vehicle, and sort the feasible paths in ascending order of distance to form a set of feasible paths; Calculate the distances between the starting and ending points of the guide vehicle and the locations of the charging piles, and sort the distances between the starting and ending points of the guide vehicle and the locations of the charging piles in ascending order to form a set of conditions. Determining the optimal route for the current guided vehicle based on its current driving range specifically includes: S31. Determine whether the current driving range S of the guided vehicle is less than the shortest path in the set of feasible paths. If not, take the shortest path in the current set of feasible paths as the optimal path; if so, proceed to step S32. S32. Filter out the paths from the set of conditions where the distance between the starting point and the charging station is less than the driving range S, and proceed to step S33; S33. Determine whether the selected charging piles are currently charging. If not, take the path with the shortest distance between the current starting position of the guide vehicle and the charging pile as the best path. If yes, proceed to step S34. S34. Calculate the remaining charging time Ti for each selected charging station, calculate the travel time Tsi from the current guide vehicle to each selected charging station, and calculate the judgment time T: T = Ti - Tsi, and sort T; select the charging station with the smallest judgment time, and take the current starting position - charging station - destination position as the best path.

2. The intelligent control method for guided vehicle cruise according to claim 1, characterized in that: In step S2, the self-load information of the guide vehicle includes the unloaded weight of the guide vehicle, the real-time load weight of the guide vehicle, and the transmission efficiency of the guide vehicle. The charge information of the guided vehicle includes the remaining battery power of the current guided vehicle and the battery discharge efficiency.

3. The intelligent control method for guided vehicle cruise according to claim 2, characterized in that: The wind resistance of the guided vehicle is determined using the following method: Among them, S R For the frontal area of ​​the guide vehicle, c d γ is the drag coefficient, v is the speed of the guided vehicle, and γ is the wind resistance calculation constant.

4. The intelligent control method for guided vehicle cruise according to claim 2, characterized in that: The rolling resistance of the guided vehicle is determined using the following method: F2 = Mf, where M is the total weight of the guide car and f is the rolling resistance coefficient.

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