Unmanned forklift dispatching method and system based on warehouse management system

Abstract

The invention discloses an unmanned forklift dispatching method and system based on a warehouse management system, and belongs to the technical field of intelligent warehousing and logistics automation. Aiming at the problems of limited interaction, asynchronous tasks, static path planning, poor multi-forklift coordination and incomplete exception handling of the existing unmanned forklift dispatching and warehouse management system, the technical scheme is characterized in that the dispatching system consists of a dispatching center, a warehouse management system and at least one unmanned forklift, the method comprises the steps of receiving task and cargo information, obtaining forklift real-time data, dynamically matching a target forklift, dynamically planning an optimal path, sending a scheduling instruction and performing exception handling. The system comprises a data interaction module, a forklift monitoring module and the like, and supports multi-vehicle cooperation and time sequence planning. The method and system are used for unmanned forklift dispatching, dispatching efficiency and flexibility can be improved, deep fusion with a warehouse management system is achieved, and operation continuity is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and logistics automation technology, and in particular to an unmanned forklift scheduling method and system based on a warehouse management system. Background Technology

[0002] With the development of Industry 4.0 and intelligent manufacturing, the warehousing and logistics industry is gradually transforming towards automation and intelligence. As the core equipment of automated warehousing, the scheduling efficiency of unmanned forklifts directly affects the overall operational efficiency of the warehouse.

[0003] In existing technologies, unmanned forklift scheduling mostly uses independent scheduling systems with limited interaction with warehouse management systems (WMS), resulting in the following problems: 1. The task allocation is not synchronized with the actual status of the warehouse, which can easily lead to conflicts between scheduling instructions and actual needs; 2. Path planning only considers the static environment and cannot cope with dynamic obstacles in the warehouse (such as temporarily stacked goods, personnel movement, etc.). 3. When multiple vehicles are coordinated and scheduled, problems such as path intersection and resource competition may easily occur; 4. The abnormal handling mechanism is not perfect, and manual intervention is required when a failure occurs, which affects the continuity of operation.

[0004] Therefore, there is a need for an unmanned forklift scheduling solution that is deeply integrated with the warehouse management system and has dynamic scheduling and intelligent collaborative capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide an unmanned forklift scheduling method and system based on a warehouse management system, so as to solve the problems of low scheduling efficiency, poor flexibility and insufficient coordination with the warehouse management system in the prior art. Detailed Implementation

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for scheduling unmanned forklifts based on a warehouse management system (WMS) is applied to a scheduling system consisting of a scheduling center, a WMS, and at least one unmanned forklift. The method includes: The system receives cargo status information and task requirements sent by the warehouse management system. The cargo status information includes cargo location coordinates, cargo weight, and inbound / outbound type. The task requirements include preset pickup location, preset unloading location, and task priority. Acquire real-time operating data for each unmanned forklift, including current location, remaining battery power, load status, and fault warning information; Based on the cargo status information, task requirements, and real-time operation data, a dynamic priority algorithm is used to match the target unmanned forklift and generate a scheduling instruction that includes path parameters, forking parameters, and task timing. The path parameters are jointly planned and generated by pre-stored warehouse map and real-time obstacle information. Send the scheduling instruction to the target unmanned forklift and receive its feedback on the task execution status; If an abnormality alert is received, the abnormality handling process is triggered: a surround-view command containing multi-machine pose parameters is generated and sent to the target unmanned forklift, the abnormal image data collected by its image acquisition unit is obtained, and the abnormality data is synchronously updated to the warehouse management system.

[0007] The matching of target unmanned forklifts based on cargo status information, task requirements, and real-time operational data includes: Filter candidate unmanned forklifts that meet preset conditions from real-time operation data. The preset conditions include remaining power ≥ minimum power required for the task and load status being either empty or matching cargo weight. Calculate the distance cost between the candidate unmanned forklift and the preset pickup location, the time cost corresponding to the task priority, and the adaptation cost corresponding to the historical execution efficiency; The weighted summation algorithm is used to calculate the comprehensive cost of each candidate unmanned forklift, and the candidate unmanned forklift with the lowest comprehensive cost is determined as the target unmanned forklift.

[0008] The process of generating the path parameters includes: Call the pre-stored warehouse map, take the current position of the target unmanned forklift as the starting point, and use the preset pickup position and preset unloading position as waypoints to generate the initial path; The system receives obstacle information sent in real time by the sensing module of the unmanned forklift, the obstacle information including obstacle coordinates and moving speed; The initial path is dynamically adjusted based on the A* algorithm to generate optimal path parameters that avoid obstacles and meet the forklift turning radius constraints.

[0009] The method also includes a task collaborative scheduling step: When there are multiple parallel task requirements, obtain the preset execution time and storage location association of each task; Tasks are grouped based on storage location relationships, with tasks in the same group corresponding to adjacent storage location areas; Assign a dedicated target unmanned forklift to each task group, and avoid path conflicts between different task groups through time-series planning.

[0010] This invention also provides an unmanned forklift dispatching system based on a warehouse management system, comprising: The data interaction module is used to establish a communication connection with the warehouse management system, receive cargo status information and task requirements, and at the same time, provide feedback on the task execution status to the warehouse management system. The forklift monitoring module is used to acquire real-time operating data of each unmanned forklift, including the current position obtained through the positioning unit, the remaining power obtained through the power sensor, and the fault warning information obtained through the fault detection unit. The task matching module is used to determine the target unmanned forklift based on a dynamic priority algorithm, combined with cargo status information, task requirements and real-time operation data. The path planning module is used to call up a pre-stored warehouse map and generate the optimal path parameters by combining real-time obstacle information; The instruction generation module is used to integrate the target unmanned forklift information, path parameters, and forking parameters, generate scheduling instructions, and send them to the target unmanned forklift. The exception handling module is used to receive exception alerts, generate surround view commands, and acquire exception image data, which is then synchronized to the warehouse management system and the forklift monitoring module.

[0011] The system also includes a map management module: The map management module includes a map creation unit and an update unit; The mapping unit is used to generate a pre-stored warehouse map containing warehouse location coordinates, driveway boundaries, and restricted areas based on the motion trajectory of the unmanned forklift and the environmental information collected by the sensing module. The update unit is used to receive storage location adjustment information sent by the warehouse management system and update the storage location coordinate data of the pre-stored warehouse map in real time.

[0012] The forklift monitoring module also includes a load detection submodule: The load detection submodule is connected to the weight sensor of the unmanned forklift and is used to acquire cargo weight data. When the weight of the goods exceeds the rated load of the target unmanned forklift, a load over-limit signal is sent to the task matching module, triggering a re-matching process.

[0013] The fork-taking parameters in the instruction generation module include fork extension length, lifting height, and fork-taking speed. These parameters are determined in the following manner: The system receives the accurate location of the goods from the sensing module of the unmanned forklift. The sensing module includes a laser detection sensor or a vision camera. The accurate location of the goods is obtained by positioning through a reflector or scanning an identification code. By combining the weight and size information of the cargo, the appropriate forklift parameters are calculated through a kinematic model.

Claims

1. A method for scheduling unmanned forklifts based on a warehouse management system, characterized in that, The method, applied to a scheduling system consisting of a scheduling center and a warehouse management system (WMS) and at least one unmanned forklift, includes: The system receives cargo status information and task requirements sent by the warehouse management system. The cargo status information includes cargo location coordinates, cargo weight, and inbound / outbound type. The task requirements include preset pickup location, preset unloading location, and task priority. Acquire real-time operating data for each unmanned forklift, including current location, remaining battery power, load status, and fault warning information; Based on the cargo status information, task requirements, and real-time operation data, a dynamic priority algorithm is used to match the target unmanned forklift and generate a scheduling instruction that includes path parameters, forking parameters, and task timing. The path parameters are jointly planned and generated by pre-stored warehouse map and real-time obstacle information. Send the scheduling instruction to the target unmanned forklift and receive its feedback on the task execution status; If an abnormality alert is received, the abnormality handling process is triggered: a surround-view command containing multi-machine pose parameters is generated and sent to the target unmanned forklift, the abnormal image data collected by its image acquisition unit is obtained, and the abnormality data is synchronously updated to the warehouse management system.

2. The method according to claim 1, characterized in that, The matching of target unmanned forklifts based on cargo status information, task requirements, and real-time operational data includes: Filter candidate unmanned forklifts that meet preset conditions from real-time operation data. The preset conditions include remaining power ≥ minimum power required for the task and load status being either empty or matching cargo weight. Calculate the distance cost between the candidate unmanned forklift and the preset pickup location, the time cost corresponding to the task priority, and the adaptation cost corresponding to the historical execution efficiency; The weighted summation algorithm is used to calculate the comprehensive cost of each candidate unmanned forklift, and the candidate unmanned forklift with the lowest comprehensive cost is determined as the target unmanned forklift.

3. The method according to claim 1, characterized in that, The process of generating the path parameters includes: Call the pre-stored warehouse map, take the current position of the target unmanned forklift as the starting point, and use the preset pickup position and preset unloading position as waypoints to generate the initial path; The system receives obstacle information sent in real time by the sensing module of the unmanned forklift, the obstacle information including obstacle coordinates and moving speed; The initial path is dynamically adjusted based on the A* algorithm to generate optimal path parameters that avoid obstacles and satisfy the forklift turning radius constraint.

4. The method according to claim 1, characterized in that, The method also includes a task collaborative scheduling step: When there are multiple parallel task requirements, obtain the preset execution time and storage location association of each task; Tasks are grouped based on storage location relationships, with tasks in the same group corresponding to adjacent storage location areas; Assign a dedicated target unmanned forklift to each task group, and avoid path conflicts between different task groups through time-series planning.

5. An unmanned forklift dispatching system based on a warehouse management system, characterized in that, include: The data interaction module is used to establish a communication connection with the warehouse management system, receive cargo status information and task requirements, and at the same time, provide feedback on the task execution status to the warehouse management system. The forklift monitoring module is used to acquire real-time operating data of each unmanned forklift, including the current position obtained through the positioning unit, the remaining power obtained through the power sensor, and the fault warning information obtained through the fault detection unit. The task matching module is used to determine the target unmanned forklift based on a dynamic priority algorithm, combined with cargo status information, task requirements and real-time operation data. The path planning module is used to call up a pre-stored warehouse map and generate the optimal path parameters by combining real-time obstacle information; The instruction generation module is used to integrate the target unmanned forklift information, path parameters, and forking parameters, generate scheduling instructions, and send them to the target unmanned forklift. The exception handling module is used to receive exception alerts, generate surround view commands, and acquire exception image data, which is then synchronized to the warehouse management system and the forklift monitoring module.

6. The system according to claim 5, characterized in that, The system also includes a map management module: The map management module includes a map creation unit and an update unit; The mapping unit is used to generate a pre-stored warehouse map containing warehouse location coordinates, driveway boundaries, and restricted areas based on the motion trajectory of the unmanned forklift and the environmental information collected by the sensing module. The update unit is used to receive storage location adjustment information sent by the warehouse management system and update the storage location coordinate data of the pre-stored warehouse map in real time.

7. The system according to claim 5, characterized in that, The forklift monitoring module also includes a load detection submodule: The load detection submodule is connected to the weight sensor of the unmanned forklift and is used to acquire cargo weight data. When the weight of the goods exceeds the rated load of the target unmanned forklift, a load over-limit signal is sent to the task matching module, triggering a re-matching process.

8. The system according to claim 5, characterized in that, The fork-taking parameters in the instruction generation module include fork extension length, lifting height, and fork-taking speed. These parameters are determined in the following manner: The system receives the accurate location of the goods from the sensing module of the unmanned forklift. The sensing module includes a laser detection sensor or a vision camera. The accurate location of the goods is obtained by positioning through a reflector or scanning an identification code. By combining the weight and size information of the cargo, the appropriate forklift parameters are calculated through a kinematic model.