CN-122015871-A - AGV cluster energy consumption optimization path planning method

CN122015871ACN 122015871 ACN122015871 ACN 122015871ACN-122015871-A

Abstract

The invention provides a path planning method for AGV cluster energy consumption optimization, and belongs to the technical field of AGV path planning. The path planning method for the AGV cluster energy consumption optimization comprises the steps of obtaining current cluster path planning data, conducting energy-saving optimization analysis based on load limitation to form load limitation energy-saving optimization data, conducting energy-saving adjustment analysis based on material conveying time limit according to the load limitation energy-saving optimization data to form time limit energy-saving adjustment data, conducting real-time energy consumption optimization monitoring analysis according to the time limit energy-saving adjustment data to form cluster real-time energy consumption optimization data. The method realizes cluster path planning operation which is more energy-saving and meets the production requirement by considering energy consumption optimization.

Inventors

WANG JIE
Ma qu

Assignees

深圳凌鼎智能装备科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260401

Claims (10)

1. The path planning method for the energy consumption optimization of the AGV cluster is characterized by comprising the following steps of: acquiring current cluster path planning data, and performing energy-saving optimization analysis based on load limitation to form load limitation energy-saving optimization data; According to the load limiting energy-saving optimization data, carrying out energy-saving adjustment analysis based on material conveying time limit to form time limit energy-saving adjustment data; and carrying out real-time energy consumption optimization monitoring analysis according to the time limit energy saving adjustment data to form cluster real-time energy consumption optimization data.
2. The method of claim 1, wherein the obtaining current cluster path planning data for performing a load-limitation-based energy-saving optimization analysis to form load-limitation energy-saving optimization data comprises: Determining target loads of different target AGVs according to the current cluster path planning data; According to the target loads of different target AGVs, carrying out loading capacity analysis to calibrate all the target loaded AGVs; and carrying out combined operation energy-saving optimization analysis on different target loading AGVs to form the load limiting energy-saving optimization data.
3. The method of claim 2 wherein said calibrating all target loaded AGVs based on load capacity analysis of said target loads of different said target AGVs includes: determining target maximum loads of different target AGVs according to the current cluster path planning data; for different target AGVs, determining corresponding target accommodating loads according to the corresponding target maximum loads and the target loads; And carrying out loading capacity analysis on different target AGVs according to the corresponding target accommodating loads in the following modes: if any other target AGVs which do not contain the target AGVs are not more than the target accommodating load, the target AGVs are marked as the target loading AGVs, and other AGVs which are corresponding to the target loading AGVs and are not more than the target accommodating load are collected to form a combined transport target set.
4. The method of claim 3 wherein said performing a combined transport energy saving optimization analysis on different ones of said target loaded AGVs to form said load limiting energy saving optimization data comprises: sequentially extracting different target AGVs in the combined transport target set corresponding to the target loaded AGVs, and determining loading intervals of two loading points, unloading intervals of two unloading points, current positions of the two AGVs and latest allowed arrival time for reaching the two unloading points according to current planning paths of the two AGVs; Carrying out path planning according to the current position, the loading interval, the unloading interval and the latest allowable arrival time, and determining a merging planning path of a single AGV for merging and meeting the latest allowable arrival time of two unloading points; Acquiring total combined transport energy consumption corresponding to the combined transport path and total independent transport energy consumption when two AGVs independently transport materials, and determining the difference of the total combined transport energy consumption; And according to the combined transport energy consumption difference of all AGVs in the combined transport target set corresponding to the target loading AGVs, carrying out the following analysis and judgment: If the sum operation energy consumption difference is straight, determining a sum operation planning path corresponding to the largest sum operation energy consumption difference as sum operation planning data corresponding to the target loading AGV; if the combined transport energy does not exist, the target loading AGV does not perform combined transport treatment; And carrying out material conveying interference analysis on the combined conveying planning data corresponding to different target loading AGVs to form the load limiting energy-saving optimization data.
5. The method of claim 4, wherein the performing a material handling interferometry on the aggregate transport planning data corresponding to different target loaded AGVs to form the load limiting energy saving optimization data includes: Loading AGVs to different targets, and determining two corresponding AGV objects according to the corresponding combined transportation planning data; carrying out repeated comparison on AGV objects related to different co-operation planning data: If no repetition of the AGVs exists in any two pieces of the concurrent operation planning data, all pieces of the concurrent operation planning data and current planning data corresponding to the rest target AGVs which are not subjected to the concurrent operation processing are collected to form load limiting energy-saving optimization data; If the AGVs with the two combined operation planning data are repeated, comparing the combined operation total energy consumption of the combined operation planning data with the repetition, reserving the combined operation planning data with lower combined operation total energy consumption, carrying out new combined operation analysis on the target loading AGVs corresponding to the combined operation planning data which are not reserved to form new combined operation planning data, continuously carrying out repeated comparison on the combined operation planning data of a new journey until the fact that the new combined operation planning data are not repeated is confirmed, and collecting all the combined operation planning data and current planning data corresponding to the rest target AGVs which are not subjected to combined operation processing to form load limiting energy saving optimization data; If the AGVs with the two combined operation planning data are repeated, comparing the combined operation total energy consumption of the combined operation planning data, reserving the combined operation planning data with lower combined operation total energy consumption, carrying out new combined operation analysis on the target loading AGVs corresponding to the combined operation planning data which are not reserved, forming new combined operation planning data, continuously carrying out repeated comparison on the combined operation planning data of a new journey until the new combined operation planning data are not formed, and collecting all the combined operation planning data and the current planning data corresponding to the rest target AGVs which are not subjected to combined operation processing to form the load limiting energy saving optimization data.
6. The method of claim 5, wherein said performing energy conservation adjustment analysis based on a material transportation time limit based on said load limitation energy conservation optimization data to form time limit energy conservation adjustment data comprises: according to the load limiting energy-saving optimization data, carrying out overall energy-saving adjustment analysis based on material conveying time limit to form overall time limit adjustment energy-saving data; And performing independent time limit energy-saving adjustment analysis aiming at the target object according to the integral time limit energy-saving adjustment data to form the time limit energy-saving adjustment data.
7. The method of claim 6, wherein said performing an overall energy conservation adjustment analysis based on a material handling time limit based on said load limit energy conservation optimization data to form overall time limit adjustment energy conservation data comprises: Determining target plan arrival time of each target AGV reaching a final unloading point and target plan permission time limit of the corresponding final unloading point according to the load limiting energy-saving optimization data; For different target AGVs, determining corresponding target time tolerance according to the corresponding target plan arrival time and the target plan permission time limit; Acquiring the minimum value of the target time tolerance corresponding to different target AGVs, and calibrating the minimum value as a time limit allowable adjustment quantity; And taking the time limit allowable adjustment amount as a target, and performing deceleration adjustment on the whole planning path for each target AGV to form the whole time limit adjustment energy-saving data.
8. The method of claim 7 wherein said targeting said time limit permitted adjustment amount for each said target AGV to make a deceleration adjustment over the entire planned path to form said overall time limit adjusted energy saving data includes: determining speed change information on the whole route according to the corresponding planning path for different target AGVs; Carrying out uniform speed reduction on different target AGVs according to the speed change information until the time difference between the time when the target AGVs reach the final unloading point after adjustment and the time when the target AGVs reach the final unloading point before adjustment is equal to the time limit allowable adjustment quantity position; and integrating all the path planning data regulated by the target AGV to form the integral time limit regulation energy-saving data.
9. The method of claim 8, wherein said performing an independent time-limited energy-saving adjustment analysis for a target object based on said overall time-limited energy-saving adjustment data to form said time-limited energy-saving adjustment data comprises: according to the integral time limit adjustment energy-saving data, determining planning path information of different target AGVs; Performing speed reduction adjustment on different target AGVs by taking the target plan permission time limit as a target to form corresponding independent speed reduction planning paths; and carrying out interference adjustment processing on path crossing points of the independent deceleration planning paths corresponding to all the target AGVs in the following way: The speed increasing adjustment of any target AGV in the interference two sides is carried out on any path intersection in a speed adjusting range taking the center of the path intersection as the center of a circle, so that the distance between the two target AGVs in the speed adjusting range is not smaller than the limit of the interference interval all the time; and acquiring the planned paths of all the target AGVs after speed regulation to form the time limit energy-saving adjustment data.
10. The method of claim 9, wherein performing real-time energy consumption optimization monitoring analysis according to the time limit energy saving adjustment data to form clustered real-time energy consumption optimization data comprises: acquiring real-time material conveying data, and determining the distance between two target AGVs in the speed regulation range corresponding to the path intersection point; If the distance between the two target AGVs in the speed regulation range corresponding to the path crossing point is smaller than the interference interval limit, the following steps are carried out: If the speed of the target AGVs with the speed increased is reduced, the speed is reduced, so that the distance between the two target AGVs in the speed regulation range corresponding to the path crossing point is not smaller than the interference interval limit, and the information of the speed reduction is determined to be real-time optimized planning data corresponding to the target AGVs; If the speed of the accelerated target AGVs is reduced, the speed is reduced, so that the distance between the two target AGVs in the speed regulation range corresponding to the path crossing point is smaller than the interference interval limit, the accelerated target AGVs are subjected to speed increasing adjustment again until the distance between the two target AGVs in the speed regulation range corresponding to the path crossing point is not smaller than the interference interval limit all the time, and the speed increasing adjustment information is determined to be real-time optimized planning data corresponding to the target AGVs; And collecting the real-time optimization planning data and the planning data of the rest target AGVs to form the cluster real-time energy consumption optimization data.

Description

AGV cluster energy consumption optimization path planning method Technical Field The invention relates to the technical field of AGV path planning, in particular to a path planning method for AGV cluster energy consumption optimization. Background AGV cluster path planning is an important research direction in the fields of intelligent logistics and automation, and aims to plan efficient and collision-free paths for a plurality of AGVs (automatic guided vehicles) in a complex environment. At present, different technical treatment schemes are adopted for fast and effective path planning of an AGV, but most of the technical treatment schemes pay attention to the work efficiency and the operation synergy, and the consideration from the energy consumption direction is rarely carried out so as to fully realize the optimal energy consumption path planning result. Therefore, designing a path planning method for optimizing the energy consumption of an AGV cluster, which realizes cluster path planning operation which is more energy-saving and meets the production requirement based on the consideration of energy consumption optimization, is a problem to be solved urgently at present. Disclosure of Invention The embodiment of the invention provides a path planning method for AGV cluster energy consumption optimization. In order to achieve the above purpose, the invention adopts the following technical scheme: The method comprises the steps of obtaining current cluster path planning data, conducting energy-saving optimization analysis based on load limitation to form load limitation energy-saving optimization data, conducting energy-saving adjustment analysis based on material conveying time limit according to the load limitation energy-saving optimization data to form time limit energy-saving adjustment data, conducting real-time energy-saving optimization monitoring analysis according to the time limit energy-saving adjustment data to form cluster real-time energy-saving optimization data to obtain the current cluster path planning data, conducting energy-saving optimization analysis based on load limitation to form load limitation energy-saving optimization data, conducting energy-saving adjustment analysis based on material conveying time limit according to the load limitation energy-saving optimization data to form time limit energy-saving adjustment data, and conducting real-time energy-saving optimization monitoring analysis according to the time limit energy-saving adjustment data to form cluster real-time energy-saving optimization data. Therefore, the optimization of the cluster AGV paths is fully considered through three aspects of material conveying load limitation, time limitation and real-time monitoring, and the effect of energy consumption optimization is more fully and effectively realized from different aspects. Compared with a single energy consumption optimization mode, the path is optimized comprehensively and effectively from multiple aspects by considering energy consumption, and the practical energy reduction effect can be realized while the timeliness and the high efficiency of material transportation are effectively ensured. Optionally, current cluster path planning data are acquired, energy-saving optimization analysis based on load limitation is performed to form load limitation energy-saving optimization data, wherein the method comprises the steps of determining target loads of different target AGVs according to the current cluster path planning data, performing loading capacity analysis according to the target loads of the different target AGVs, calibrating all the target loaded AGVs, and performing combined operation energy-saving optimization analysis on the different target loaded AGVs to form the load limitation energy-saving optimization data. Therefore, the analysis of energy consumption optimization is performed on the AGV cluster, and the fact that the current path planning of the AGV cluster is performed with high efficiency is considered, so that the energy consumption is high, the improvement of the efficiency does not necessarily increase the overall production efficiency of the system, and after all, objective production process time is consumed in different working procedures, so that the planned path is necessary to be optimized from the angle of energy consumption optimization. The application considers that the energy consumption optimization is implemented on the basis of the formed current path planning, so that the analysis is carried out according to the sequence from the load to the path selection to the operation control, and the relationship of the three is progressive on the condition elements of the path planning, namely, the path is required to be determined firstly for completing the adjustment of the operation control, the selection and the determination of the path are implemented after the load is determined, and the load determines the destination or the de