CN117550273A - Multi-transfer robot cooperation method based on bee colony algorithm and transfer robot - Google Patents
Multi-transfer robot cooperation method based on bee colony algorithm and transfer robot Download PDFInfo
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- 238000012546 transfer Methods 0.000 title claims abstract description 103
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims description 37
- 235000012907 honey Nutrition 0.000 claims description 46
- 241000257303 Hymenoptera Species 0.000 claims description 21
- 238000001514 detection method Methods 0.000 claims description 11
- 230000015654 memory Effects 0.000 claims description 11
- 238000004891 communication Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000006978 adaptation Effects 0.000 claims description 4
- 230000006870 function Effects 0.000 description 6
- 230000003203 everyday effect Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
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- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007787 long-term memory Effects 0.000 description 2
- 238000013486 operation strategy Methods 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 239000000725 suspension Substances 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/137—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
- B65G1/1373—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed for fulfilling orders in warehouses
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
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Abstract
When a plurality of existing transfer robots operate cooperatively, goods use is facilitated by setting the stay shelf time of a fixed transfer robot, and real demands cannot be met when the goods use is caused. And the cargo information is acquired through the artificial bee colony algorithm, and the optimal operation scheme of the transfer robot corresponding to the cargo information is calculated, so that the operation efficiency of the transfer robot is improved.
Description
Technical Field
The invention relates to a multi-transfer robot cooperation method based on a bee colony algorithm and a transfer robot, belonging to an intelligent warehousing system.
Background
The transfer robot has been very widely used in warehouse system, has transfer robot use possibility in traditional manual work business turn over storehouse and full automatization's intelligent warehouse, and transfer robot's operating efficiency is an important index when designing transfer robot, and especially the more warehouse system of goods shelves has many transfer robots to cooperate the operation in order to improve transportation efficiency. In this case many transfer robot scheduling schemes and parking schemes have developed.
Many theories have proposed that a transfer robot is used as an edge device by utilizing the internet of things technology, and cargo information, operation information, profile conditions, operation control and the like of the transfer robot are transmitted in a cloud server, so that functions such as data visualization and the like are realized. In the parking scheme of the transfer robots, 1-2 transfer robots are reserved for waiting in parking spaces when the cooperation operation of the transfer robots is mainly idle, and a single transfer robot vehicle records the single-day operation of the transfer robots by using a long-period memory recurrent neural network, so that the transfer robots are sequentially parked on a shelf which is stopped when started according to the operation record sequence from the morning to the evening when the transfer robots are idle. The scheme can not necessarily effectively improve the operation efficiency of the transfer robot, because when the transfer robot is used for placing goods, the next goods taking can not be strictly recursively performed according to the sequence when entering from the morning to the evening, and the sequence has certain randomness, for example, the first goods are placed in the transfer robot to be stored on the first goods shelf, the second goods are required to be stored on the second goods shelf, the second goods shelf is required to be taken when the transfer robot is just idle, if the transfer robot waits on the first goods shelf according to the long-term memory recursion neural network algorithm model, and the transfer robot is required to be moved from the first goods shelf to the second goods shelf, so that the resource waste is caused and the operation efficiency of the transfer robot is reduced. In addition, the long-term and short-term memory recurrent neural network is designed only for a single transfer robot, and the problem of cooperation among a plurality of transfer robots is not considered.
Therefore, the present design of the transfer robot has the following two defects that firstly, the corresponding goods shelves can not be configured according to the goods shelf picking and placing frequency in different time periods every day and can not be parked when the goods shelves are idle in different time periods, and secondly, the optimal goods carrying path can not be coordinated according to the simultaneous use condition of multiple batches of goods, so that the carrying work can be completed in the least steps.
Disclosure of Invention
In view of the above problems, the invention provides a multi-transfer robot cooperation method based on a bee colony algorithm and a transfer robot, which can optimize a parking scheme according to the use habits of cargoes in different time periods, and simultaneously seek an optimal cargo path according to a plurality of cargo demands, so as to realize the completion of carrying work in minimum steps.
In one embodiment, a multi-transfer robot cooperation method based on a bee colony algorithm is implemented by the following steps:
(1) The controller receives the goods information, and initializes the position of the honey source according to the description of the goods information to obtain the serial number of the parking carrying robot, wherein the honey source is the solution of picking and placing the goods information；
(2) Setting and calculating the fitness of each bee in the population;
(3) The bee calculates the fitness according to the solution generated in the first step;
(4) Selecting honey sources by bees according to a greedy algorithm;
(5) Calculating honey sourceProbability of (2)；
(6) Observing bees according to probabilitySelecting honey sourcesIf the new adaptation value of the honey source is increased, the process proceeds to the next step, if the new adaptation value is not increasedDiscarding the honey source, and changing the collected bees corresponding to the honey source into detection bees, and returning to the step (2) to execute calculation again;
(7) Observing bees to select honey sources according to greedy strategies;
(8) Determining whether a honey source to be abandoned exists, if so, randomly generating a honey source to replace the honey source, and if not, returning to the step (2) to execute calculation again;
(9) And recording the optimal solution until the optimal solution reaches the preset iteration times or error threshold value, outputting the optimal solution, controlling the corresponding transfer robot to run to the position where the goods need to be taken and placed by the transfer robot vehicle-mounted controller according to the optimal solution, and executing the goods taking and placing.
In the embodiment, the principle that honey sources are sought by simulating the bee colony is utilized, the configuration between the actual use requirement of cargo carrying and the actual parking racks of each carrying robot car is fully considered, the optimal carrying robot use scheme is coordinated through simulation calculation, the carrying robot operation efficiency is improved, the freight use experience is enhanced, the carrying robot operation scheme in a fixed mode is avoided, and the resource waste is reduced.
Preferably, the new honey source obtained after the initialization operation in step (1)The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, …, SN, d is the dimension to solve the problem, d=1, x id Is the honey source explored by bee colony, x kd Is a honey source in the field,is the interval [ -1,1]The random number, k=1, 2, …, SN is the number of bees and observed bees set, k+.i.
Preferably, the method comprises the steps of,wherein r is the interval [0,1 ]]The random number on the random number is used for the random number,andis the lower and upper bounds of dimension d.
The transfer robot cooperation method integrates the K-means algorithm and the swarm algorithm, so that the transfer robot cooperation method has the advantages of the K-means algorithm and the swarm algorithm, and meanwhile, the adaptability and the operation efficiency of the swarm algorithm can be further improved by adding the correction value to the fitness solving formula of the swarm algorithm, and the freight experience sense and the transfer robot intellectualization are further improved.
In one embodiment, a transfer robot has the following apparatus:
a sensor that obtains data including parking shelf, cargo shelf data;
a memory storing sensor detection data and/or storing model data for executing the cooperation method of the previous embodiment;
and a controller capable of executing the model data of the memory and/or transmitting the detection data stored in the memory to the server, acquiring the model data obtained by executing the cooperation method described in the previous embodiment by the server, and executing the model data.
Preferably, in the transfer robot, when the controller transmits the detection data stored in the memory to the server, and the model data obtained by executing the cooperation method described in the previous embodiment is acquired by the server and executed, the transfer robot further has a communication module capable of transmitting the detection data of the controller to the server and transmitting the model data calculated by the server to the controller.
Preferably, in the handling robot, the communication module is a wireless communication module or a wired communication module.
Preferably, in the above-mentioned transfer robot, a sensor for acquiring the number of loads may be replaced with a video capturing device or a camera device.
According to the invention, the transfer robot is coordinated according to the cargo information through the artificial bee colony algorithm, so that the distance between the transfer robot and the cargo to be transferred can be further reduced, the use efficiency of the transfer robot can be improved, the resource waste is reduced, the transfer robot and the cargo to be transferred can be independently used, and a better effect can be achieved when the transfer robot and the cargo to be transferred are simultaneously used, and a good synergistic effect is generated between the transfer robot and the cargo to be transferred.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
fig. 1 is a block diagram of an intelligent multi-carrier robot collaboration device provided by an embodiment of the invention;
fig. 2 is a flowchart of an intelligent multi-transfer robot collaborative artificial bee colony algorithm provided by an embodiment of the invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
The artificial bee colony algorithm is an optimization method provided by simulating bee behaviors, and is a specific application of the intelligent thought of the colony. The method is mainly characterized in that the problems are compared only in terms of quality without knowing special information of the problems, and the global optimal value is finally highlighted in the group through the local optimizing behavior of each artificial bee individual, so that the method has a relatively high convergence rate. In one embodiment of the invention, freight transportation may use the transfer robots densely in different time periods, and thus, the transfer robots are required to be intelligently parked and the transfer robots are required to be transported recently, so that the operation efficiency of the transfer robots is improved and the operation time is saved. Therefore, in order to meet the requirement, the invention also provides a multi-transfer robot cooperation method based on the bee colony algorithm.
Assuming an application scenario, eight transfer robots are required to run simultaneously or in an idle state, and one transfer robot is required to be able to park on a designated shelf in the idle state. At this time, referring to fig. 2, the control method of the in-vehicle controller of the transfer robot to control the transfer robot is as follows:
step 1: the controller receives the cargo information, knowing that the cargo demand information solution space is one-dimensional according to the cargo information description, initializing the position of a first generation honey source, and randomly generating the position of the honey source of honey peak according to normal distribution, wherein the honey source is the solution of the cargo demand information, namely parking and carryingSerial number of robot. Initializing honey sources(i=1, 2, …, SN), wherein the number of bees collected and observed bees set by SN is also the total number of honey sources.
Step 2: and setting and calculating the fitness of each bee in the population. The fitness function can be designed by a greedy algorithm, and other similar algorithms can be adopted instead.
Step 3: the bee is picked up according to the new solution generated in the first stepAnd calculate the fitness.
Step 4: the bees select the honey source according to a greedy algorithm.
Step 5: calculating honey sourceProbability of (2)。
Step 6: observing bees according to probabilitySelecting honey sourcesIf the fitness value of a honey source is increased within a given step, then the honey source is discarded and the bees corresponding to the honey source become the detection bees if not increased.
Step 7: the bees were observed to select the honey source according to a greedy strategy.
Step 8: determining whether there is a honey source to be discarded, if so, randomly generating a honey source to replace the honey source, and if not, returning to the step 2.
Step 9: and recording the optimal solution until the preset iteration times or the error threshold value is reached, outputting the optimal solution, and controlling the corresponding transfer robot to run to the cargo position and execute the cargo demand by the transfer robot controller according to the optimal solution, wherein the termination condition can be a conventional algorithm termination condition.
And after the transfer robot controller obtains the optimal solution, the transfer robot controller instructs the corresponding transfer robot to run to the cargo position. Because through artificial bee colony algorithm, can avoid among the prior art common transfer robot operation that is not closest to the goods, but according to the problem that the transfer robot of original settlement operated, avoid the rigidification of transfer robot operation, improved transfer robot operating efficiency.
The K-means algorithm is an unsupervised learning method and is mainly applied to the clustering problem.
The Gap statistical method is a method for setting the K value of the K-means algorithm, and Gap can be regarded as the difference between the loss of random samples and the loss of actual samples. Assuming that the optimal cluster number corresponding to the actual sample is k, the loss of the actual sample should be relatively small, and the difference between the random sample loss and the actual sample loss also reaches the maximum correspondingly, that is, the k value corresponding to the maximum value of Gap is the optimal cluster number.
In one embodiment of the present invention, as shown in fig. 1, the block diagram of the device is shown in fig. 1, the vehicle-mounted control system of the transfer robot is composed of a sensor or a device with similar function, a counter, a controller and a memory, the vehicle-mounted control system identifies the number of goods in the transfer robot through the sensor or the device with similar function, the counter of the transfer robot records the goods shelves where the transfer robot is parked, of course, the counter can be replaced by the sensor, as long as the same function can be realized, the data is stored in the memory, and the electric control system uploads the data to the cloud server, and the uploading mode can be transmitted through the internet of things, which is not limited to the mode, and other modes only can be realized.
The cloud server stores a K-means algorithm and/or an artificial bee colony algorithm, one of the two algorithms can be stored, the cloud server executes the corresponding algorithm according to the type of the algorithm stored in the cloud server after receiving the data, if the two algorithms are both executable, or only one of the two algorithms can be executed according to the requirement.
The cloud server establishes a usage degree model of the transfer robot at different time intervals every day through a K-means algorithm according to the acquired information, then transmits a model result back to the controller, and executes a parking strategy of the transfer robot under the model when idle so as to predict goods shelves for taking and placing goods of the transfer robot at different time intervals every day and park the idle transfer robot for waiting.
The cloud server establishes a use model of the transfer robot when the goods are required to be used according to the collected information through a manual bee colony algorithm, then transmits a model result back to the controller, executes a transfer robot operation strategy under the model, enables the transfer robot to operate the transfer robot closest to the goods to be transferred to execute the transportation strategy according to the strategy, and avoids the operation stiffness problem of the existing scheme.
Of course, in the above embodiment, the server may be a local server, as long as the modeling by the K-means algorithm and/or the artificial bee colony algorithm can be implemented.
Of course, in the above embodiment, a server may not be used, the K-means algorithm and/or the artificial bee colony algorithm are stored in the transfer robot controller, and the controller directly collects data and runs the above algorithm, builds a model therein based on the algorithm, and runs a model policy.
In another embodiment of the invention, the transfer robot performs its parking method based on a K-means algorithm model, the parking method being as follows:
and collecting the collected data such as the carrying time, the number of cargos and the goods shelves of the carrying robot, and the like, collecting the carrying data according to the time and the goods shelves to form a sample, and finally predicting the goods shelves where the carrying robot is required to park at all times. The specific implementation steps are as follows:
step 1: the initialized k samples are selected as an initial cluster center a=a 1 ,a 2 ,…,a k . The value of k is artificially selected, an interval statistic method is adopted, and samples are data collected by the controller.
Using interval statisticsThe corresponding calculation formula of the method isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofAs a loss function, hereRefers toIs not limited to the above-described embodiments. This value is typically generated by Monte Carlo simulation, randomly generating as many random samples as the original number of samples in the region where the samples are located in a uniform distribution, and K-Means is applied to the random samples to obtain a sample. The reciprocating is repeated for a plurality of times, usually 20 times, and 20 times can be obtainedOf course, the number of reciprocations may be other, as just one example. For these 20Is averaged to obtainAnd finally interval statistics can be calculated. And k corresponding to the maximum value of the interval statistic is the optimal k. In the above clustering method, the interval statistic method is not the only method, and other methods with similar effects may be used as the alternative, which is only an example and not the only implementable mode in the present embodiment.
Step 2: for each sample in the datasetCalculating the distance from the cluster center to k cluster centers and dividing the distance into classes corresponding to the cluster centers with the smallest distance, wherein i=1, 2, …, k。
Step 3: for each class, its cluster center, i.e. the centroid of all samples belonging to that class, is recalculated.
Step 4: repeating the step 2 and the step 3 until the suspension condition is reached, and obtaining a clustering result; the suspension condition may be a conventional number of iterations, a minimum error change, etc.
After the cloud is completed in calculation and training, the result model is transmitted to a vehicle-mounted controller (such as a PLC) of the transfer robot through wireless communication equipment, and the transfer robot is directly controlled to park according to an operation strategy formulated by the model.
Through the K-means model, the number of goods carried by the carrying robot to different shelves according to each time period is taken as a sample, the samples collected by the sensor are established in a two-dimensional coordinate system, the time and the freight flow of the shelves are taken as coordinate axes, the sample is taken as a point, and the sample is continuously made to approach a clustering center in iteration, so that the machine learns to obtain sample freight habits gathered according to different time periods, the shelves are automatically parked at idle time in different time periods every day according to the habit prediction of freight, the highest efficiency is achieved, and the defect that the optimal shelves can not be automatically planned to park according to different time periods is avoided.
In another embodiment of the invention, in order to make the freight efficiency higher, the K-means algorithm and the swarm algorithm are integrated into the operation of the transfer robot, the transfer robot stays on a goods shelf adapting to freight requirements through the K-means algorithm during normal operation of the transfer robot, when the temporary requirement transfer robot of the warehousing system reaches the goods shelf needing to pick up goods, the transfer robot controller receives an operation instruction, the controller brings the solving result of the K-means algorithm into the swarm algorithm, and constrains the increasing constant of the fitness formula in the swarm algorithm, so that the fitness value can be solved more quickly by the swarm algorithm, then the optimal solution can be solved in the swarm algorithm according to the fitness, and the transfer robot vehicle can be correspondingly operated.
In the embodiment, by integrating the K-means algorithm and the swarm algorithm, the method has the advantages of the K-means algorithm and the swarm algorithm, and in addition, the fitness solving formula is optimized when the K-means algorithm and the swarm algorithm are used, so that the K-means algorithm and the swarm algorithm can be relatively simply integrated, the efficiency is higher, the freight transportation use requirement is better met, and the intellectualization of the transfer robot is further improved.
Claims (6)
1. A multi-transfer robot cooperation method based on a bee colony algorithm is characterized in that: comprising the following steps:
(1) The controller receives the goods information, and initializes the position of the honey source according to the description of the goods information to obtain the serial number of the parking carrying robot, wherein the honey source is the solution of picking and placing the goods information；
(2) Setting and calculating the fitness of each bee in the population;
(3) The bee calculates the fitness according to the solution generated in the first step;
(4) Selecting honey sources by bees according to a greedy algorithm;
(5) Calculating honey sourceProbability of->；
(6) Observing bees according to probabilitySelecting Mi Yuan->If the new adaptation value of the honey source is increased, entering the next step, if the new adaptation value is not increased, discarding the honey source, and changing the collected bees corresponding to the honey source into detection bees, and returning to the step (2) to execute calculation again;
(7) Observing bees to select honey sources according to greedy strategies;
(8) Determining whether a honey source to be abandoned exists, if so, randomly generating a honey source to replace the honey source, and if not, returning to the step (2) to execute calculation again;
(9) And recording the optimal solution until the optimal solution reaches the preset iteration times or error threshold value, outputting the optimal solution, controlling the corresponding transfer robot to run to the position where the goods need to be taken and placed by the transfer robot vehicle-mounted controller according to the optimal solution, and executing the goods taking and placing.
2. The multi-transfer robot cooperation method based on the swarm algorithm according to claim 1, wherein: new honey source obtained after initializing operation in step (1)The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, …, SN, d is the dimension to solve the problem, d=1, x id Is the honey source explored by bee colony, x kd Is a source of honey in the field, and is->Is the interval [ -1,1]The random number, k=1, 2, …, SN is the number of bees and observed bees set, k+.i.
3. The multi-carrier robot cooperation method based on the swarm algorithm according to claim 2, wherein:wherein r is the interval [0,1 ]]Random number on->And->Is the lower and upper bounds of dimension d.
4. A transfer robot characterized in that it has the following equipment:
a sensor that acquires data including a berthing shelf, a cargo shelf;
a memory storing sensor detection data and/or storing computer-executable instructions that are executed model data obtained by the multi-carrier robot collaboration method based on a swarm algorithm according to claim 1;
a controller configured to execute the model data of the memory and/or transmit the detection data stored in the memory to a server, acquire the model data obtained by executing the multi-carrier robot cooperation method based on the swarm algorithm according to claim 1, and execute the model data.
5. The transfer robot according to claim 4, wherein: when the controller transmits the detection data stored in the memory to the server, the model data is acquired through the server, and the model data is executed;
the transfer robot further comprises a communication module, wherein the communication module transmits detection data of the controller to the server, and transmits model data calculated by the server to the controller.
6. The transfer robot according to claim 5, wherein: the communication module is a wireless communication module or a wired communication module.
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| CN117555338A (en) * | 2024-01-10 | 2024-02-13 | 成都电科星拓科技有限公司 | K-means algorithm-based multi-automatic guided vehicle cooperative parking method and automatic guided vehicle |
Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104808665A (en) * | 2015-04-16 | 2015-07-29 | 上海大学 | Multi robot path planning method based on multi-target artificial bee colony algorithm |
| CN106709692A (en) * | 2017-02-24 | 2017-05-24 | 北京远大宏略科技股份有限公司 | Logistics center storage position allocation method |
| US20180059685A1 (en) * | 2016-08-23 | 2018-03-01 | King Fahd University Of Petroleum And Minerals | Gps-free robots |
| CN108036790A (en) * | 2017-12-03 | 2018-05-15 | 景德镇陶瓷大学 | Robot path planning method and system based on mutillid algorithm under a kind of obstacle environment |
| US20180333855A1 (en) * | 2017-05-19 | 2018-11-22 | Stc.Unm | System and methods for multiple-place swarm foraging with dynamic depots |
| CN109118008A (en) * | 2018-08-23 | 2019-01-01 | 厦门北卡信息科技有限公司 | A kind of chaos artificial bee colony optimal data acquisition methods, paths planning method and resource regulating method |
| CN109205163A (en) * | 2018-08-13 | 2019-01-15 | 山东大学 | Across tunnel Multilayer shuttle car warehousing system design method, system and storage medium |
| US20190080270A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Production scheduling method and system based on improved artificial bee colony algorithm and storage medium |
| US20190107404A1 (en) * | 2017-06-13 | 2019-04-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival |
| CN109765915A (en) * | 2019-03-13 | 2019-05-17 | 中南大学 | A kind of storage transfer robot task scheduling system based on computerized algorithm |
| CN110817226A (en) * | 2019-12-03 | 2020-02-21 | 苏州毫厘文化传媒科技有限公司 | Honeycomb type material conveying robot and working method thereof |
| CN111142524A (en) * | 2019-12-27 | 2020-05-12 | 广州番禺职业技术学院 | Garbage picking robot, method and device and storage medium |
| CN111674795A (en) * | 2020-05-27 | 2020-09-18 | 浙江工业大学 | Task scheduling method of cross-layer and cross-roadway shuttle storage system |
| CN113324545A (en) * | 2021-04-22 | 2021-08-31 | 南京航空航天大学 | Multi-unmanned aerial vehicle collaborative task planning method based on hybrid enhanced intelligence |
| US20210286361A1 (en) * | 2020-03-12 | 2021-09-16 | National Deep Sea Center | Method and system for path planning of wave glider |
| CN113759927A (en) * | 2021-09-16 | 2021-12-07 | 重庆邮电大学 | Warehousing robot path planning method based on artificial bee colony-adaptive genetic algorithm |
| WO2022257838A1 (en) * | 2021-06-10 | 2022-12-15 | 深圳市海柔创新科技有限公司 | Goods transportation method and apparatus, device, robot, and warehousing system |
| CN115865785A (en) * | 2023-02-27 | 2023-03-28 | 南京邮电大学 | VANET clustering routing method based on k-means clustering |
| US20230106134A1 (en) * | 2020-06-12 | 2023-04-06 | Hai Robotics Co., Ltd. | Warehouse robot control method and apparatus, device, and readable storage medium |
| CN116203959A (en) * | 2023-02-24 | 2023-06-02 | 济南大学 | Robot path planning method and system based on HAC algorithm |
| CN116205550A (en) * | 2023-03-09 | 2023-06-02 | 福建三钢闽光股份有限公司 | Logistics garden vehicle goods taking scheduling method based on self-adaptive discrete particle swarm algorithm |
| CN116702694A (en) * | 2023-07-07 | 2023-09-05 | 成都电科星拓科技有限公司 | Printed circuit board two-end wiring method, medium and device based on ant colony algorithm |
| CN117032298A (en) * | 2023-07-24 | 2023-11-10 | 南京航空航天大学 | Unmanned aerial vehicle task allocation planning method under synchronous operation and cooperative distribution mode of truck unmanned aerial vehicle |
-
2024
- 2024-01-10 CN CN202410033936.8A patent/CN117550273B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104808665A (en) * | 2015-04-16 | 2015-07-29 | 上海大学 | Multi robot path planning method based on multi-target artificial bee colony algorithm |
| US20180059685A1 (en) * | 2016-08-23 | 2018-03-01 | King Fahd University Of Petroleum And Minerals | Gps-free robots |
| CN106709692A (en) * | 2017-02-24 | 2017-05-24 | 北京远大宏略科技股份有限公司 | Logistics center storage position allocation method |
| US20180333855A1 (en) * | 2017-05-19 | 2018-11-22 | Stc.Unm | System and methods for multiple-place swarm foraging with dynamic depots |
| US20190107404A1 (en) * | 2017-06-13 | 2019-04-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival |
| US20190080270A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Production scheduling method and system based on improved artificial bee colony algorithm and storage medium |
| CN108036790A (en) * | 2017-12-03 | 2018-05-15 | 景德镇陶瓷大学 | Robot path planning method and system based on mutillid algorithm under a kind of obstacle environment |
| CN109205163A (en) * | 2018-08-13 | 2019-01-15 | 山东大学 | Across tunnel Multilayer shuttle car warehousing system design method, system and storage medium |
| CN109118008A (en) * | 2018-08-23 | 2019-01-01 | 厦门北卡信息科技有限公司 | A kind of chaos artificial bee colony optimal data acquisition methods, paths planning method and resource regulating method |
| CN109765915A (en) * | 2019-03-13 | 2019-05-17 | 中南大学 | A kind of storage transfer robot task scheduling system based on computerized algorithm |
| CN110817226A (en) * | 2019-12-03 | 2020-02-21 | 苏州毫厘文化传媒科技有限公司 | Honeycomb type material conveying robot and working method thereof |
| CN111142524A (en) * | 2019-12-27 | 2020-05-12 | 广州番禺职业技术学院 | Garbage picking robot, method and device and storage medium |
| US20210286361A1 (en) * | 2020-03-12 | 2021-09-16 | National Deep Sea Center | Method and system for path planning of wave glider |
| CN111674795A (en) * | 2020-05-27 | 2020-09-18 | 浙江工业大学 | Task scheduling method of cross-layer and cross-roadway shuttle storage system |
| US20230106134A1 (en) * | 2020-06-12 | 2023-04-06 | Hai Robotics Co., Ltd. | Warehouse robot control method and apparatus, device, and readable storage medium |
| CN113324545A (en) * | 2021-04-22 | 2021-08-31 | 南京航空航天大学 | Multi-unmanned aerial vehicle collaborative task planning method based on hybrid enhanced intelligence |
| WO2022257838A1 (en) * | 2021-06-10 | 2022-12-15 | 深圳市海柔创新科技有限公司 | Goods transportation method and apparatus, device, robot, and warehousing system |
| CN113759927A (en) * | 2021-09-16 | 2021-12-07 | 重庆邮电大学 | Warehousing robot path planning method based on artificial bee colony-adaptive genetic algorithm |
| CN116203959A (en) * | 2023-02-24 | 2023-06-02 | 济南大学 | Robot path planning method and system based on HAC algorithm |
| CN115865785A (en) * | 2023-02-27 | 2023-03-28 | 南京邮电大学 | VANET clustering routing method based on k-means clustering |
| CN116205550A (en) * | 2023-03-09 | 2023-06-02 | 福建三钢闽光股份有限公司 | Logistics garden vehicle goods taking scheduling method based on self-adaptive discrete particle swarm algorithm |
| CN116702694A (en) * | 2023-07-07 | 2023-09-05 | 成都电科星拓科技有限公司 | Printed circuit board two-end wiring method, medium and device based on ant colony algorithm |
| CN117032298A (en) * | 2023-07-24 | 2023-11-10 | 南京航空航天大学 | Unmanned aerial vehicle task allocation planning method under synchronous operation and cooperative distribution mode of truck unmanned aerial vehicle |
Non-Patent Citations (3)
| Title |
|---|
| 张岩岩;侯媛彬;李晨;: "基于人工免疫改进的搬运机器人蚁群路径规划", 计算机测量与控制, no. 12, 31 December 2015 (2015-12-31), pages 4124 - 4127 * |
| 张荣 等: "生鲜农产品车辆和无人机组合配送路径优化及效果测试", 河南农业大学学报, vol. 58, no. 01, 30 November 2023 (2023-11-30), pages 96 - 105 * |
| 琚春华 等: "复杂社会情境下的个性化推荐方法与应用", vol. 1, 31 August 2018, 浙江工商大学出版社, pages: 082 - 086 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117555338A (en) * | 2024-01-10 | 2024-02-13 | 成都电科星拓科技有限公司 | K-means algorithm-based multi-automatic guided vehicle cooperative parking method and automatic guided vehicle |
| CN117555338B (en) * | 2024-01-10 | 2024-03-19 | 成都电科星拓科技有限公司 | K-means algorithm-based multi-automatic guided vehicle cooperative parking method and automatic guided vehicle |
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