160 results about "Robot scheduling" patented technology

The invention relates to the technical field of robot scheduling, in particular to a distributed robot collaborative scheduling system and method in a dynamic environment, and the system comprises an environment sensing layer which is used for collecting environment dynamic data in real time; the distributed decision-making layer comprises local task scheduling modules of a plurality of robots; the cooperative communication layer is used for realizing task state synchronization and conflict detection among the robots based on a low-delay communication protocol; the dynamic weight calculation module is used for generating a real-time optimization weight according to the task emergency degree, the robot energy consumption and the path risk factor; according to the invention, by using a completely distributed collaborative scheduling architecture, through a decentralized task distribution mechanism and a distributed consensus protocol, a single-point fault risk existing in a traditional centralized scheduling system is thoroughly eliminated, and even if a part of robot nodes have faults or communication is interrupted, the system can work normally. And the system can still run continuously through autonomous negotiation of the remaining nodes, so that the reliability of the system in a complex environment is remarkably improved.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The invention discloses a multi-robot cooperative scheduling system and method based on a distributed digital twinborn body, and relates to the technical field of intelligent control, and the system comprises a control module, the distributed digital twinborn body and a plurality of robots. The control module obtains the task request and determines an initial robot corresponding to the task request from the multiple robots based on the operation knowledge graph, the task object digital twinborn body and the robot digital twinborn body; obtaining state parameters of the initial robot based on the robot digital twin, and evaluating a robot cluster capable of executing the task request in the initial robot based on the state parameters; and determining a to-be-executed task of each target robot in the robot cluster based on a preset robot sorting scheduling algorithm. The robot scheduling system is used for solving the problem that in the prior art, an existing robot scheduling system cannot meet the requirements of multiple batches and multiple varieties of workpieces, and cooperative scheduling of multiple robots is achieved to meet the requirements of the multiple batches and multiple varieties of workpieces.

The invention relates to the technical field of AGV robot scheduling and control, and discloses an AGV robot scheduling method and system of an intelligent factory and a storage medium. The method comprises the steps of collecting bearing sensing data to identify a product height value according to a product carrying instruction and a transportation route, and if the value is higher than a reference value, generating an auxiliary scheduling instruction; next, nearby non-loaded AGVs are searched to serve as temporary robots, scheduling values are obtained in combination with task load and distance sorting of the temporary robots, and the optimal robot is selected to serve as a slave robot to be paired with the original AGV robot to jointly bear products; and finally, the master and slave robots respectively respond to the matched scheduling instructions. According to the method, through master-slave cooperation and accurate scheduling, the AGV transportation stability and efficiency are improved.

This invention relates to the field of robot scheduling technology, and more particularly to a multi-robot cloud scheduling and collaborative method and system. The method includes the following steps: acquiring building elevator group data, and dividing the building elevator service groups based on the building elevator group data to obtain building elevator service group data; acquiring robot target path data; segmenting the robot target path data and building elevator service group data into path resource segments to obtain split path data; acquiring current transfer floor data, and estimating transfer waiting time based on the current transfer floor data and split path data to obtain transfer waiting time estimation data; constructing a transfer map based on the transfer waiting time estimation data to obtain transfer map data; obtaining path conflict data based on the transfer map data; and obtaining robot scheduling data based on the path conflict data to perform multi-robot collaborative scheduling operations. This invention can improve task completion efficiency and scheduling intelligence in multi-robot cross-floor collaborative scenarios.

The invention discloses a storage robot cluster management system and method, and belongs to the technical field of robot scheduling. The system comprises a central management server and an autonomous mobile robot cluster. The central management server comprises a congestion prediction and global path planning module; the congestion prediction and global path planning module comprises a congestion prediction sub-module and an A * path planning sub-module with space-time cost; the autonomous mobile robot is internally provided with a multi-sensor sensing and communication module and a local decision module, and the off-line centralized training platform is used for optimizing the local decision module in each autonomous mobile robot through a centralized evaluation network with global information in a simulation environment. The invention provides a hybrid control architecture integrating global path planning, dynamic task allocation and local real-time decision based on multi-agent reinforcement learning, and efficient and smooth cluster collaborative operation can be realized.

The invention discloses a mobile charging robot return charging scheduling method, and particularly relates to the technical field of mobile robot scheduling management, and the method comprises the steps: collecting state information including the current position, residual electric quantity, task residual path length, battery performance and other contents; when the state meets a scheduling evaluation triggering condition, calculating a behavior urgency index and an energy competition index; inputting the two indexes into a trained learning model, and outputting a decision parameter group containing a scheduling trigger mark, a path sorting factor and a path tradeoff factor; weighing the candidate charging paths according to the decision parameter group, and selecting a comprehensive optimization result as a target path; according to the method, the scheduling quantitative judgment is realized by collecting the multi-dimensional state information and constructing the behavior urgency and energy competition index, the decision parameter group is generated based on the index result, target path screening is guided, a closed-loop mechanism from state perception and intelligent evaluation to path selection is formed, and the scheduling accuracy and the response efficiency are improved.

The invention relates to the technical field of industrial automation, in particular to a multi-robot control method and system based on the industrial Internet of Things and a medium. The method comprises the following steps: acquiring industrial warehouse channel data; generating a warehouse digital twin model based on the industrial warehouse channel data; performing multi-robot path conflict detection according to the warehouse digital twin model to obtain path conflict data; determining a robot number according to the path conflict data; performing motor winding damage detection based on the robot number to obtain motor winding damage data; performing iron core loosening analysis based on the motor winding damage data to obtain iron core loosening data; performing vibration suppression control optimization according to the iron core loosening data to obtain vibration suppression control data; and robot scheduling is carried out according to the path conflict data to obtain robot scheduling data. According to the invention, the path planning precision and fault response efficiency of the multi-robot system are improved based on the industrial automation technology, and the operation efficiency of industrial warehouse operation is improved.

The invention discloses a task pre-scheduling method and device of a robot and a storage medium, the task pre-scheduling method of the robot is applied to a task scheduling system, and the task scheduling system is used for scheduling tasks of a task machine to the robot. The method comprises the following steps: in response to a received pre-dispatch task generated for a to-be-generated task of a target task machine, recording the duration of the pre-dispatch task; the expected arrival time of the robot moving to the target task machine in the pre-dispatch task is acquired; and according to the expected arrival time and the duration time, determining a target robot from a robot candidate set corresponding to the target task machine to perform task pre-allocation processing. According to the scheme, the task execution efficiency of the robot can be improved.

The invention relates to a multi-valve assembly line robot scheduling method based on an improved MOPSO algorithm, and the method comprises the steps: firstly constructing a process dependency digraph, defining constraint conditions and decision variables, then determining a multi-objective optimization function, and carrying out the solving through an improved multi-objective particle swarm optimization algorithm, the improved multi-objective particle swarm optimization algorithm comprises the steps of generating an initial particle swarm by adopting an oxidation strategy, dynamically adjusting an inertia weight, a learning factor and a mutation probability, performing adaptive mutation on the updated particle swarm and the like. Compared with the prior art, the method can solve the defects in the prior art, remarkably improves the accuracy, robustness and practicability of industrial robot task scheduling, and achieves efficient and reliable multi-robot cooperative scheduling.

The invention discloses a container exit-oriented robot scheduling method and system, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting multi-source data through an Internet of Things sensor network, and inputting the multi-source data into a dual-depth Q network after time-space alignment to complete task pre-distribution; performing rolling time domain path optimization based on a three-dimensional space-time coordinate system, and resolving track conflicts in combination with a virtual guide point strategy; the weight of a multi-target cost function is dynamically adjusted by using a long-short-term memory network, and adaptive optimization of indexes such as transportation efficiency and energy consumption is realized; for an abnormal event, a bee colony optimization algorithm is adopted to generate a local conflict-free path, and a container door orientation optimization strategy is integrated to reduce energy consumption, so that the collaborative scheduling efficiency of the container terminal AGV cluster is improved, and the dynamic environment adaptability is enhanced.

The invention relates to the technical field of robot scheduling, in particular to a multi-robot cloud scheduling cooperation method and system. The method comprises the following steps that building elevator group data are obtained, building elevator service groups are divided according to the building elevator group data, and building elevator service group data are obtained; acquiring target path data of the robot; performing path resource section cutting on the robot target path data and the building elevator service group data to obtain split path data; acquiring current transit floor data, and performing transit waiting estimation according to the current transit floor data and the split path data to obtain transit waiting estimation data; carrying out transfer graph construction according to the transfer waiting estimation data to obtain transfer graph data; obtaining path conflict data according to the transfer graph data; and obtaining robot scheduling data according to the path conflict data so as to carry out multi-robot cooperative scheduling operation. According to the method, the task completion efficiency and the scheduling intelligence level can be improved in a multi-robot cross-floor collaborative scene.

The invention relates to the technical field of robot scheduling, in particular to a humanoid robot scheduling method in a humanoid robot training ground, and the method comprises the steps: collecting global environment data in the training ground in real time through a multi-modal sensor array, and fusing the global environment data with the obtained real-time pose information of a robot; analyzing the obtained training task queue into atomic operation data, and associating the atomic operation data with the constructed training ground resource table; based on the dynamic passage map and the training ground resource table, the resource constraint operation chain is dynamically allocated, and a scheduling path is generated after space-time joint path planning; and the generated allocation task is transmitted to the corresponding humanoid robot, and the dynamic passage map and the training ground resource table are updated based on the obtained execution data, so that the efficient and conflict-free real-time scheduling method capable of adapting to the motion characteristics of the humanoid robot is realized, the task and path conflict between the robots is avoided, and the real-time scheduling efficiency of the humanoid robot is improved. The overall utilization rate and the training efficiency of the training ground are improved.

The invention provides a multi-machine collaborative conflict resolution method and system based on medical logistics robot scheduling, and the method comprises the steps: generating a collaborative scheduling trajectory planning result based on a working environment map of multiple medical logistics robots, road network information and a received instruction; wherein the collaborative scheduling trajectory planning result is the optimal global static path of the multiple medical logistics robots; the collaborative scheduling trajectory planning result is configured to multiple medical logistics robots; and monitoring the operation data of each medical logistics robot in real time, and judging whether a conflict occurs currently or not. According to the method, full-period conflict prevention from static planning to dynamic adjustment is achieved through the synergistic effect of the prediction model and the resolution rule, and linkage conflicts caused by temporary obstacles or path offset are avoided while the high-priority material transportation efficiency is guaranteed; the method is suitable for a multi-medical logistics robot efficient, safe and intelligent cooperative scheduling scene in a complex hospital environment.

In order to solve the problem, the invention provides an intention-aware multi-robot scheduling optimization method based on a graph neural network, and the method comprises the steps: constructing an explicit intention vector of a multi-tuple structure, fusing a robot state, task information and a resource use condition, constructing an intention graph representing a task conflict and a cooperative relationship, and introducing a spectrum sparsification mechanism; then, a graph attention network and a strategy updating module are adopted to jointly train a strategy model, strategy optimization of each robot node on a graph structure is achieved, and therefore the overall task income is maximized, path conflicts and resource competition are minimized, and a universal path planning algorithm is used for dynamically constructing a conflict avoidance path; compared with the prior art, the method has higher real-time performance, expandability and environment adaptability, and the scheduling efficiency and the task completion rate of the multi-robot system in dynamic scenes such as disaster response and the like are remarkably improved.

The embodiment of the invention provides a humanoid robot scheduling method based on group game and related equipment, and belongs to the technical field of robot scheduling. The method comprises the following steps: acquiring a task demand and a list of working procedures to be executed, and sending the list of working procedures to be executed to a plurality of personal robots; the to-be-executed process list comprises a plurality of to-be-executed processes; receiving self-description information of the plurality of personal robots; analyzing according to the task requirement, the list of the working procedures needing to be executed and the self-description information of the plurality of humanoid robots, and sequentially selecting the humanoid robots meeting the requirements from the plurality of humanoid robots according to an analysis result and distributing the humanoid robots to each working procedure needing to be executed so as to determine the humanoid robot corresponding to each working procedure needing to be executed; and an execution instruction is sent to the humanoid robot corresponding to each process needing to be executed based on the list of the processes needing to be executed. According to the embodiment of the invention, the overall resource utilization rate of the cooperative group can be improved through a group game mechanism.

The invention discloses a multi-delivery robot path collaborative optimization system and a scheduling method, and aims to solve the problems of non-uniform scheduling load, unsmooth path, weak conflict avoidance capability and poor dynamic adaptability of existing multi-robot scheduling. The system comprises a central scheduling module, a distribution robot terminal, an environment sensing module and a map management module, and realizes task analysis, state monitoring, path planning and conflict coordination. According to the method, region division and task balanced distribution are completed by improving a DARP algorithm, path coverage efficiency and smoothness are optimized by fusing a BA * algorithm, a cattle farming method and a TEB algorithm, multi-type path conflicts are solved by adopting a time window reservation and distributed negotiation strategy, and dynamic adjustment under new task access, robot faults and environment sudden change is supported. According to the method, the load balance, the path optimization effect and the conflict avoidance capability of multi-robot collaborative distribution can be remarkably improved, the efficient and stable execution of the distribution task is ensured, and the method is suitable for terminal distribution scenes such as communities and office buildings.

The invention discloses an unmanned aerial vehicle patrol and security monitoring linkage management method. According to the system, urban geographic information data and key monitoring area coordinates are acquired, hierarchical grid division is realized by using a quadtree spatial index algorithm, and a patrol task distribution basic data structure is established; solving an approximate optimal solution of the patrol path of the unmanned aerial vehicle by adopting an improved greedy strategy, and performing optimization adjustment according to technical parameters and weather conditions of the unmanned aerial vehicle; the unmanned aerial vehicle collects video data in real time through high-definition camera equipment in the patrol process, target detection is conducted through a deep learning model, and potential safety hazards such as personnel gathering and vehicle abnormity are recognized; detecting violent conflicts and illegal intrusion behaviors in combination with a time sequence analysis algorithm, and generating an abnormal event report; cooperative work of the unmanned aerial vehicle, the ground robot and the command center is realized; and a patrol scheme is dynamically adjusted according to meteorological conditions, and robot scheduling is optimized through energy consumption management.

The invention discloses a to-be-served intelligent charging robot scheduling method and device based on multi-target joint optimization, and the method comprises the steps: determining all to-be-served intelligent charging robots and all to-be-charged vehicles, which meet a preset scheduling condition; based on the current state and historical scheduling information of each to-be-served intelligent charging robot, the scheduling priority of each to-be-served intelligent charging robot is evaluated to select candidate to-be-served intelligent charging robots; matching the current state of the candidate to-be-served intelligent charging robot with the charging demand of each to-be-charged vehicle, and selecting a candidate to-be-charged vehicle based on the matching degree; and constructing a multi-objective optimization function, scoring the candidate to-be-served intelligent charging robots and the candidate to-be-charged vehicles through the multi-objective optimization function, and determining an optimal to-be-served intelligent charging robot and an optimal to-be-charged vehicle based on a scoring result. Dynamic balance can be realized between the service coverage range and the energy income, and different scenes can be adapted.

The invention relates to the technical field of robot operation, in particular to a multi-vision task priority robot scheduling control method and system, and the method comprises the steps: receiving a multi-vision task request which comprises a task identifier, an initial priority and a resource demand; according to the method, the multi-dimensional task dependency graph representing task state dependency, resource consumption dependency and task category dependency among the multi-vision tasks is constructed, the'result dependency chain 'relationship of the tasks is clearly presented, and the problems of low task chain efficiency and easy interruption caused by neglecting the dependency in an existing scheduling method are solved; in combination with the real-time system resource state, the real-time scheduling priority is dynamically calculated through the initial priority, the dependency factor and the resource matching factor, the weight of the dependency factor can be improved according to the task chain interruption risk, and the weight of the resource matching factor can be improved according to the resource conflict possibility; and invalid waiting of the high-priority post-order tasks due to delay of the low-priority pre-order tasks is avoided.

The invention discloses an industrial autonomous mobile robot control method, device and equipment and a medium, is applied to a robot control system based on a hierarchical architecture, and relates to the field of automation, and the method comprises the steps: analyzing an input operation text instruction, and generating a navigation task and a grabbing task of a robot based on an obtained structured subtask sequence; calling preset laser equipment according to the navigation task to generate a three-dimensional semantic map of a target end point corresponding to the operation text instruction, and performing path planning and obstacle avoidance through the three-dimensional semantic map to obtain a target path; calling a preset image acquisition device to acquire a material image of a preset material area according to the grabbing task, and processing the material image to generate a material grabbing parameter; and converting the target path and the material grabbing parameter into a robot scheduling instruction corresponding to the robot, and controlling the robot to execute a navigation task and a grabbing task through the robot scheduling instruction. Therefore, autonomous operation of the industrial robot can be realized in combination with semantic understanding.

The application relates to the technical field of intelligent logistics, and discloses an AMR plant logistics intelligent scheduling system based on composite navigation, which comprises an information management layer, a control scheduling layer and an on-site execution layer. A vehicle-mounted controller module uses the kinematics data of an inertial measurement unit to perform time alignment and weighted fusion on the heterogeneous data of a laser radar and a bottom vision camera, solves real-time pose, and combines a navigation confidence change trend, network delay and load quality data to calculate a request safety envelope radius based on a multi-dimensional state coupling model. A robot scheduling module receives vehicle data, executes an asymmetric negotiation strategy when detecting that the request safety envelope has spatial overlap, determines the target safety envelope size of each party in conflict according to a comprehensive capability score, keeps the envelope of the disadvantaged party unchanged and forcibly compresses the envelope of the advantaged party to a limit safety radius. The application solves the safety and efficiency problems of multi-vehicle collaborative operation under complex working conditions through multi-dimensional state coupling and an asymmetric scheduling mechanism.

The invention discloses a multi-robot scheduling method and device for a warehousing system and a related medium, and the method comprises the steps: obtaining a goods allocation-robot association data set, carrying out the statistics of the number of warehousing materials, and carrying out the sorting, and obtaining warehousing goods allocation distribution data; analyzing a warehouse-out demand by using the warehouse-in goods allocation distribution data to obtain warehouse-out goods allocation distribution data; arranging waves based on the warehouse-out goods allocation distribution data to obtain a palletizing instruction set; extracting a to-be-cast tray task set according to the tray loading instruction set, sorting the to-be-cast tray task set, and generating a cast scheduling instruction set according to the priority; and carrying out platform access processing on the tray loading instruction set and the line casting instruction set, and outputting scheduling instruction data according to the tray state. According to the invention, the platform access processing is carried out on the calculated tray loading instruction set and the calculated line casting instruction set, and the scheduling instruction data is output according to the tray state, so that multiple operation robots can be scheduled and optimized uniformly, and the overall task completion time is reduced.

The application relates to the technical field of robot scheduling, in particular to a mobile robot real-time scheduling method and system based on a dynamic topological map. In the system, an emergency scheduling planning scheme management module collects the transportation state information of each mobile robot in real time through a sensor, judges the current working load state of the mobile robot, combines the topological map updating result under the corresponding transportation scene at the current time and the initial material delivery target planning route of the corresponding mobile robot, generates an emergency scheduling planning scheme of the mobile robot with the abnormal working load state at the current time, and calculates the load balance comprehensive adaptive deviation value of each emergency scheduling planning scheme. The application realizes the dynamic updating of the material delivery target planning route of the mobile robot in combination with the transportation state of the mobile robot itself, introduces the concept of the load balance comprehensive adaptive deviation value, realizes the effective screening of the best emergency scheduling planning scheme of the mobile robot, and realizes the collaborative management among different mobile robots.

The present application relates to the technical field of cleaning robot scheduling control, and particularly relates to a cleaning robot dynamic task scheduling method and system based on property work orders. The method comprises the following steps: receiving work order data stream pushed by a property work order system, analyzing work order identification, emergency identification, task type, task location and expected completion time, and generating an emergency work order task record; obtaining a current task queue of a cleaning robot and robot state information based on the record, matching a task execution time window, task location and robot state information, and generating an insertion conflict evaluation record containing a candidate insertion position and a queue disturbance evaluation record; performing local re-planning accordingly, generating a local re-planning task sequence containing an original task reservation sequence and a recovery anchor point, and issuing a scheduling instruction, writing back execution state data and updating a current task queue. The present application forms a scheduling closed loop for sudden work order insertion, original task recovery and work order state record.

The application relates to a kind of multi-robot scheduling control methods based on roundabout movement and deadlock prevention, comprising: constructing annular buffer and dynamic circulation path between picking station and storage area, and modeling finite state machine for dynamic behavior of robot, to obtain the finite state machine model corresponding to different robots;Based on the finite state machine model of robot, combined with resource allocation constraints and preset operation constraints, the multi-robot scheduling strategy is output by executing roundabout movement scheduling algorithm, to control the working state of each robot accordingly.Compared with the prior art, the application can dynamically guide the robot to move in order along the circulation path between the picking station and the storage area, and can actively prevent deadlock, effectively solving the limitations of traditional scheduling strategy in deadlock prevention, resource utilization and efficiency improvement.

The invention belongs to the technical field of robot scheduling, and particularly relates to a cleaning robot work task scheduling method and system, and the method comprises the steps: determining the global competitiveness of a cleaning task according to the area of the cleaning task and the distance between the cleaning robot and the cleaning task, determining an exclusive tendency degree according to the global competitiveness and the distance from the cleaning robot to the cleaning task, and determining a task allocation suppression factor of the cleaning robot to the cleaning task according to the exclusive tendency degrees of other cleaning robots to the cleaning task; and according to the task allocation suppression factor and the distance from the cleaning robot to the cleaning task, determining a total social gravitation vector suffered by the cleaning robot, performing vector superposition on the total social gravitation vector and a repulsive force vector to obtain a total driving force of the cleaning robot, generating a motion instruction, and scheduling the cleaning robot to perform cleaning. According to the invention, the intelligent balanced distribution of the cleaning tasks among the plurality of cleaning robots is realized, and the operation efficiency of the tippler system is improved.

The invention provides a map construction method and device, a robot scheduling method and device and a storage medium, and relates to the technical field of three-dimensional mapping and the technical field of intelligent warehousing, and the method comprises the steps: determining a global pose sequence of a robot in a mapping data collection process according to mapping data of a warehousing space collected by the robot; determining a three-dimensional model of the storage space based on the global pose sequence and the mapping data; determining semantic information of each pixel in the image data; and mapping the semantic information of each pixel to the three-dimensional model of the storage space to obtain a three-dimensional semantic map of the storage space. According to the method, the three-dimensional model of the storage space can be constructed in the mapping stage, the pixel semantic information in the image data is mapped to the three-dimensional model, the three-dimensional semantic map of the storage space is constructed, the accurate three-dimensional space scale can be provided, obstacle perception and path planning of the robot in the height direction are supported, and the robot can be conveniently and rapidly constructed. And the capability of the robot for distinguishing high-repetition scenes can be enhanced by means of semantic information, and the positioning accuracy is improved.

The invention discloses a double-wire welding strip automatic take-up device and a material disc taking and placing robot dispatching method, and belongs to the technical field of welding strip production equipment. The device comprises a box body, a solder strip take-up device I, a solder strip take-up device II and a stock bin, wherein the solder strip take-up device I and the solder strip take-up device II are arranged up and down; each welding strip take-up device is provided with two sets of symmetrical wire winding devices, each wire winding device comprises a lead screw, a wire winding device base, a driving disc, a wire shifting device, a clamping device and other components, and non-stop disc replacement of one-way winding and one-way pre-assembling can be achieved in cooperation with a rotary disc. The coiled material disc is transferred to the material bin through the robot, and taking and placing of the empty disc and the coiled material disc are completed according to a preset sequence. In a multi-device and multi-robot scene, the utilization rate of the robots is improved through scheduling optimization, and it is guaranteed that the winding device does not stop. On the premise that the occupied area is not obviously increased, the production capacity and the coil shape consistency are improved, and the device is suitable for continuous, integrated and intelligent production of the welding strips.

The embodiment of the application discloses a meal delivery robot scheduling method and system. The method comprises the following steps: determining at least one target robot from candidate robots according to a robot scheduling request and working states, current positions and delivery device attributes of the candidate robots; wherein the robot scheduling request is generated by a catering management platform according to catering order information and dish making progress, and is sent to the robot management platform; a robot meal taking request is generated for the target robot, and the robot meal taking request is fed back to the catering management platform, so as to instruct the catering management platform to determine target dishes associated with the target robot in response to the robot meal taking request. The technical solution of the application realizes data sharing between the robot management platform and the restaurant management platform, improves the automation degree and efficiency of robot scheduling, and reduces labor costs.