206 results about "Robot scheduling" patented technology

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

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 industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

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 relates to the technical field of intelligent logistics and path planning, and discloses a digital twinborn technology-based logistics robot path planning method, which comprises the steps of constructing a digital twinborn model consistent with a storage environment, identifying logistics path key nodes and obstacles, generating a plurality of candidate paths and carrying out simulation verification, and screening an optimal path according to simulation feedback, and finally issuing the path to the robot for execution. By establishing a virtual and physical two-way synchronous environment model, efficient simulation and prediction of a logistics path are realized, so that the intelligent level of robot scheduling and the stability of path advancing are improved, and the method is suitable for complex logistics scenes such as multi-robot cooperation and dynamic path optimization.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention provides a multi-agent cooperative task inference and robot scheduling system, which comprises an interaction unit used for acquiring a natural language instruction issued by a user, sending the natural language instruction to an intelligent processing unit and interacting with the intelligent processing unit, receiving an execution result of a task and body state information of a robot, and sending the received execution result to the intelligent processing unit; sending a task missing information supplementing request when the task information is missing; the intelligent processing unit is used for dynamically sensing an environment and a task state according to a user instruction, a body state and environment information fed back by the robot and stored memory, and performing dynamic planning and decision making based on a sensing result and an inverse result of a task execution result to realize task reasoning and robot scheduling; and the robot cluster executes tasks according to the action sequence and feeds back own body state information and environment information. According to the invention, through a multi-agent cooperation framework based on a large model, the robot is endowed with dynamic reasoning and active interaction capabilities, and the requirements of efficient task reasoning and cooperative execution in a dynamic environment are met.

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 robot control, and discloses a robot scheduling method, device and system. Environment data sent by a robot is received, and the environment data is environment data of an area where the robot is located; according to the environment data, performing map model construction on the area to obtain map information; obtaining local map information, positioning information and a motion path according to the map information; and sending the local map information, the positioning information and the motion path to the robot to support autonomous movement and task execution of the robot in the current area. In this way, the overall operation efficiency and stability of the system are improved.

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.

The invention provides an efficient transfer robot scheduling system for components in intelligent warehousing, and relates to the technical field of intelligent warehousing logistics, and the method comprises the steps: analyzing a transfer task instruction, extracting a task starting point coordinate, an end point coordinate and a path constraint condition, and collecting warehousing environment data in real time; the storage environment data comprises position offset state data, track boundary data and dynamic obstacle distance data; constructing a grid map model based on the position offset state data, the track boundary data and the dynamic obstacle distance data; and iteratively calculating a global final path node sequence on a magnetic stripe track by adopting a colistification algorithm fusing an oscillation factor disturbance mechanism and a self-adaptive weight strategy based on the grid map model. According to the invention, the problems of poor path flexibility and insufficient control precision in an intelligent warehousing component carrying system are solved.

The invention discloses a multi-task cooperative control robot scheduling method and system based on quantum optimization, and relates to the technical field of computers, and the method is characterized in that a structured data stream is generated according to an equipment operation parameter decision; a process constraint condition is generated through a digital twin engine; process constraint conditions are coded into feature vectors based on federated learning, and the feature vectors are mapped to a QUBO model; real-time production line topological structure hard constraints are injected, and an optimal scheduling scheme is solved through a quantum annealing algorithm; performing multi-physical field simulation verification; and dynamically updating model weight parameters of federated learning according to the deviation degree and an update gradient of a process constraint condition generated by a digital twin engine. According to the method, safe, efficient and elastic scheduling in a complex manufacturing scene is realized through a cooperative technology system of quantum optimization global solution, federated learning heterogeneous compatibility, digital twinning virtual-real verification and dynamic feedback continuous iteration.

A robot scheduling system, which relates to the technical field of logistics, and is used for solving the problem of the logistics efficiency of a warehousing system not being high. The system comprises a scheduling device and a rack (101), wherein the rack (101) corresponds to a plurality of transfer robots (102); the scheduling device is in communication connection with the plurality of transfer robots (102); and the scheduling device is configured to control, upon receiving an order instruction for instructing the transfer of goods in a target rack area, a transfer robot (102) corresponding to the target rack area to operate, the target rack area is one of a plurality of rack areas which are obtained by means of partitioning the rack (101) in the lengthwise direction and / or the depthwise direction of the rack (101) on the basis of attribute information of the rack (101) and / or a placement rule for goods on the rack (101), and one rack area at least corresponds to one transfer robot (102). Further provided are a robot scheduling method and a storage medium.

The invention provides a medical hospital guide robot scheduling method based on dynamic path planning. The method comprises the following steps: acquiring hospital guide information of a patient to be diagnosed; determining a task priority of the hospital guide information based on the hospital guide information; based on the task priority of the to-be-diagnosed patient, determining a target hospital guide robot, performing path planning processing through a preset reinforcement learning algorithm, and determining a walking path of the target hospital guide robot; and based on the walking route, controlling the target hospital guide robot to guide the to-be-diagnosed patient to the target consulting room. The hospital guide robot resource can be reasonably scheduled according to the task emergency degree, the surrounding environment information is sensed in real time through the reinforcement learning algorithm, the path is planned, the patient is guided to the destination, and the hospital guide efficiency is improved; the problems that according to an existing hospital guide robot, hospital guide robot resources cannot be reasonably dispatched according to the patient flow and the task emergency degree, surrounding environment information cannot be accurately sensed, and consequently path planning is inaccurate, and hospital guide efficiency is low are solved.

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 fire-fighting equipment availability monitoring and intelligent three-dimensional warehouse storage management system, and relates to the technical field of warehouse management, and the system comprises an intelligent three-dimensional warehouse storage module which is used for storing fire-fighting equipment and carrying out the access management of the fire-fighting equipment based on an automatic access system and a robot scheduling system. The intelligent three-dimensional warehouse storage module comprises a fire-fighting equipment pre-distribution mechanism based on dynamic storage optimization, and the fire-fighting equipment pre-distribution mechanism based on dynamic storage optimization is combined with fire risk analysis, historical fire-fighting equipment use data and fire-fighting equipment service life prediction data to dynamically adjust the storage position of fire-fighting equipment. The fire-fighting equipment availability monitoring module is designed, so that the functions of real-time monitoring and fault prediction of the fire-fighting equipment are realized, the problem of equipment failure caused by long-time non-use or equipment aging is solved, the availability and reliability of the equipment are improved, and fire emergency work is guaranteed.

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 provides an emergency energy supply scheduling method and system for a wheel-foot mobile robot, and relates to the technical field of energy supply robot scheduling, and the method comprises the steps: obtaining vehicle attribute information, vehicle position information and vehicle electric quantity information of vehicles in and out of a supply station, and building an initial matching scheme of a robot and a vehicle; dividing priorities for the vehicles in the initial matching scheme according to the purpose or the emergency degree of the vehicle attribute information, and calculating a priority penalty value, a path length, task energy consumption and a load imbalance degree in the initial matching scheme; an objective function is constructed, the objective function is optimized, constraint conditions are introduced, an optimal matching scheme is screened out from the initial matching schemes, and the constraint conditions include one-to-one service of the robot and the vehicle, the electric quantity of the robot meets the vehicle charging task requirement, and the out-station task is prior to the in-station task; and generating a dynamic path according to a matching result of the robot and the vehicle in the optimal matching scheme.

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 charging robot scheduling method and device. The method comprises the following steps: acquiring parking space data and vehicle data in a corresponding coverage area acquired by acquisition equipment deployed on each charging robot for charging a vehicle; a parking space distribution diagram is constructed according to the parking space data, and a vehicle distribution diagram is constructed according to the vehicle data; and under the condition that the idle charging robot exists, the idle charging robot is dispatched to a target position corresponding to the idle parking space according to the parking space distribution diagram and the vehicle distribution diagram. According to the scheme, under the condition that the idle charging robot exists, the idle charging robot is scheduled to the target position corresponding to the idle parking space in advance according to the parking space distribution diagram and the vehicle distribution diagram, after the vehicle is parked in the idle parking space and under the condition that the charging requirement is met, the idle charging robot can be scheduled in time, and the charging efficiency is improved. And the scheduling efficiency of the charging robot is improved.

The invention discloses a multi-robot scheduling method, a robot and a computer program product. The scheduling method is applied to a main robot, and the main robot is determined from robots of different business line types in mixed operation in a target business scene. The master robot constructs a basic map of a target area corresponding to the target business scene, synchronizes the basic map to the at least one slave robot, and combines configuration data (including business configuration data required by the master robot and the slave robot for first operation on the corresponding business line) to obtain a full map; and then, the master robot generates a scheduling resource packet based on the total map, and synchronizes the scheduling resource packet to each slave robot, so that each robot in the target area can realize collision-free collaborative operation. According to the scheduling method, the scheduling configuration of the hybrid service line robots is simplified, and the deployment period of the multiple robots is effectively shortened.

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.