92 results about "Robot scheduling" patented technology

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 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 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 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 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 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.

The invention provides a mobile robot scheduling method, electronic equipment and a computer program product. The method comprises the following steps: determining a global scheduling path of each mobile robot according to a topological graph; according to the real-time pose information of each mobile robot and the global scheduling path, a pre-locking node set of each mobile robot is determined, and the pre-locking node set is composed of nodes, through which the mobile robot does not pass, in each node contained in the global scheduling path; based on the region type corresponding to each node in the pre-locking node set, judging whether an unlockable node exists in the pre-locking node set or not; under the condition that no node which cannot be locked exists in the pre-locking node set, locking each node in the pre-locking node set to the corresponding mobile robot, and controlling the mobile robot to sequentially pass through each node in the pre-locking node set; and unlocking the locked nodes of the mobile robot according to the region types corresponding to the nodes.

This invention belongs to the field of intelligent robot inspection technology and discloses an artificial intelligence robot scheduling and inspection method. The specific steps are as follows: First, a three-dimensional scene model of the inspection area is constructed; then, the robot collects on-site data from multiple sources, and performs noise reduction, feature extraction, and standardization at the edge; next, anomalies are identified and task priorities are assigned through edge AI; subsequently, the scheduling platform combines genetic algorithms and A* algorithms to achieve multi-machine task allocation and conflict-free path planning; real-time obstacle avoidance and collaborative scheduling are performed during inspection; finally, the inspection data is fed back in a closed loop to iteratively update the scene model and scheduling strategy. Through global task balanced allocation and conflict-free path planning, path intersections, duplicate inspections, and inspection blind spots are effectively eliminated, improving robot resource utilization. From the perspective of scheduling mode and collaborative operation, it solves the problems of rigid scheduling and chaotic multi-machine cooperation in traditional systems, comprehensively improving the response speed and overall efficiency of inspection operations.

This application provides a bin management method, system, device, electronic device, and storage medium, relating to the field of robot scheduling. The bin management method includes: when preset management trigger conditions are met, selecting a target bin for allocation to a target bin handling robot, which is to be positioned; based on the target bin handling robot and the target bin's location in the target storage area, executing a sorting task for the target bin; the sorting task includes: scheduling the target bin handling robot to the target storage area, controlling the target bin handling robot to perform a retrieval action on the target bin at the target storage area, and after the retrieval action is completed, performing an action to place the target bin into the storage area. This solution provides a foundation for reducing the bin retrieval failure rate without affecting inbound and outbound operations.

This invention discloses a traffic control method and system for an inspection robot and an electric shuttle bus. The method includes: S1. collecting laser ranging data of the wheel axle cross-section of the electric shuttle bus; S2. performing large-step sliding median filtering; S3. filtering valid data segments containing axle data; S4. extracting valid data segments and performing small-step sliding median filtering on them; S5. calculating the mode of the amplitude in the valid data segments and replacing abnormal amplitudes exceeding a preset threshold with the mode; S6. identifying abrupt change points in the wheel axle profile in the valid data segments; S7. filtering abrupt change points; S8. calculating the position and forward speed of the electric shuttle bus; S9. transmitting the position and forward speed of the electric shuttle bus to the robot scheduling system; S10. making traffic control decisions based on the real-time position and speed prediction results of the electric shuttle bus and the real-time task path of the robot. This invention has advantages such as improved operational efficiency and safety.

The invention discloses a robot scheduling method, and the method comprises the steps: firstly obtaining a target task and determining candidate robots, then calculating the task cost representing the task execution resource consumption of the candidate robots in combination with the attribute information of the target task and the candidate robots, and finally selecting the target robot to execute the task according to the task cost. The method has the advantages that the scheduling decision is fully brought into the real-time specific state of the candidate robot terminal side, the resource consumption is accurately quantified through the task cost, the task is effectively prevented from being allocated to the robot with too high resource consumption or insufficient adaptability, and the operation efficiency and the resource utilization rate of the scheduling system are remarkably improved. Meanwhile, the definite step flow and quantitative evaluation ensure that the decision is scientific and operable, different scene task requirements can be met, the task execution stability and reliability are improved, and a definite basis is provided for subsequent scheduling strategy optimization.

The invention discloses a single-robot carrying intelligent scheduling method and system based on dynamic priority learning, and relates to the technical field of carrying process intelligent control. The single-robot carrying intelligent scheduling method and system based on dynamic priority learning comprises the steps that S1, receiving constraint data, movement track data and operation feedback data in the carrying process are collected and preprocessed, and a standardized carrying state data set is constructed; s2, the resource occupancy degree of the single robot is analyzed, and the carrying priority sequence corresponding to all the devices is dynamically adjusted; s3, evaluating the scheduling sorting stability, and continuously adjusting the numerical interval between the scheduling quantity values of each device; and S4, evaluating the operation load of the executable action sequence, and driving the data updating of the strategy network. The problem that an existing single-robot scheduling method is difficult to keep continuous stable balance between equipment utilization rate improvement and no-load movement distance suppression due to lack of a long-term operation data-driven self-adaptive balancing mechanism is solved.

A robot schedule creation device 1 comprises: an acquisition unit 111 that acquires behavior information of a worker H; an extraction unit 112 that extracts tasks on the basis of the behavior information of the worker H acquired by the acquisition unit 111; a selection unit 113 that selects, from the tasks extracted by the extraction unit 112, a task suitable for characteristics of a robot 3; and a creation unit 114 that creates a behavior schedule for causing the robot 3 to replace the task selected by the selection unit 113.

The invention discloses a high-density RPA robot based on a credential environment and a scheduling management system and method, relates to the technical field of RPA, and aims at solving the problems that a traditional RPA scheme is low in resource utilization rate, poor in stability and complex in operation and maintenance. The system is constructed on a credential server composed of a domestic central processing unit and a domestic operating system, and the core comprises a session management module, an intelligent resource scheduling center and a unified monitoring management console. The method comprises the steps of environment initialization, task queuing, resource scheduling, isolation session starting, operation monitoring, task cleaning and exception handling, and scheduling decision calculation and safety verification based on comprehensive load factors. Through OS native multi-user isolation, the resource utilization rate is improved by 3-10 times, the execution efficiency is improved by more than 30%, interference among instances is completely eradicated, safety compliance is met, operation and maintenance are simplified, software and hardware cost is reduced, hardware ROI is optimized, and a single creative server stably and concurrently runs multiple RPA instances.

The application discloses a kind of robot scheduling method, device, electronic equipment and storage medium.The method comprises: determining master node from each controller node corresponding to each associated robot, and determining the information processing time and the synchronization effective time of each controller node according to master node;At information processing time, each controller node obtains remote node task information associated with controller node;At synchronization effective time, through the local scheduler of each associated robot, execute the target task of each associated robot according to remote node task information.Through the technical scheme provided by the embodiment of the application, the problem that the concept and logic of the existing multi-robot scheduling are relatively complex, and it is difficult to operate without the help of offline simulation tools, and the limitation of single-controller multi-robot control is also very large can be solved, and the beneficial effects of reducing the difficulty of multi-robot scheduling and improving the efficiency of multi-robot scheduling are achieved.

The present application provides a robot control-based island-type assembly method and apparatus, a device and a storage medium. The method comprises: for each production line robot, scheduling the production line robot to operate according to an assembly route (101); when the production line robot arrives at an assembly island along the assembly route, controlling an installation robot to assemble a material matching the assembly island onto a product to be assembled carried by the production line robot (102); and if said product carried by the production line robot has been installed, when the production line robot arrives at a configured target position along the assembly route, scheduling the production line robot to leave the assembly route from the target position (103).

The invention relates to an elevator collaborative scheduling method and system based on man-machine transport capacity balance, and the method comprises the steps: receiving and analyzing a robot task state packet, and determining the task urgency degree, the task timeliness and the key resource state; building passenger transport state data are collected in real time, and current passenger flow pressure data and elevator system state data are analyzed; carrying out transport capacity balance and scheduling cost decision making, and calculating comprehensive weighted cost of different scheduling strategies based on a robot task state packet and building passenger transport state data; and according to the strategy of the lowest comprehensive weighting cost, differentiated two-way instructions are output, and control instructions are sent to the robot terminal and the elevator control system. According to the scheme provided by the invention, the elevator transport capacity can be dynamically and elastically distributed between the requirements of people and robots, transport capacity waste or local congestion is avoided, the overall vertical transport efficiency of a building is improved, and the problem that the passenger transport service quality is reduced due to improper scheduling of the robots is relieved.

This invention discloses a robot scheduling and planning platform and intelligent scheduling and planning method, relating to the fields of robot map calculation and intelligent scheduling technology. The key technical points are: it includes an operation module, an order module, a pickup module, a receiving module, and a return module; the operation module is used to interface with hardware devices requiring operation and control; the order module is used to filter executable orders and robots capable of performing tasks, and assign orders to robots capable of performing tasks; the pickup module is used to invoke the robot's voice broadcast function to send material information to a remote terminal, allowing business personnel to view the material's location through the remote terminal. This platform and planning method can divide multiple floors into zones, calculate elevator scheduling in real time based on elevator operating status, and obtain the robot's real-time location to achieve robot avoidance scheduling, enabling a large number of robots to transport orders efficiently within a limited space through reasonable intelligent route planning.

The invention provides a robot scheduling method, a sound source positioning method and equipment, and relates to the technical field of financial science and technology or the technical field of signal processing. The method comprises the following steps: determining the direction of a sound source signal through a mixed analog-to-digital array; distinguishing sound source signals of clients through a voiceprint separation technology; and performing intention recognition on the sound source signal of the client to obtain the voice intention of the client. According to the method, the sound source direction positioning accuracy and the robot scheduling accuracy can be improved.

The invention relates to the technical field of distribution scheduling, and discloses a coal sample transfer robot scheduling method based on reinforcement learning, comprising: constructing a coal sample transfer scheduling environment model including state information of a plurality of coal sample tasks, heterogeneous transport carriers and processing nodes; based on the scheduling environment model, defining a state space and an action space of a reinforcement learning agent; the state space represents the real-time state of the scheduling environment, and the action space covers an effective scheduling action combination composed of a sample type, a transportation carrier and target equipment; and designing a reward function with optimization of scheduling efficiency as a target, training the intelligent agent by adopting reinforcement learning until the strategy converges, obtaining an optimal scheduling strategy, deploying the optimal scheduling strategy to a coal sample transfer robot system, outputting a scheduling action according to a real-time environment state through a greedy strategy, and completing a coal sample transfer task. The problems of unbalanced task allocation and untimely path conflict processing are solved, and the purposes of optimizing resource allocation and improving scheduling efficiency are achieved.

The application provides a flexible automobile assembly line system and a production line operation method. The mobile robot of the flexible automobile assembly line system comprises a jacking mobile robot, a line head pre-lifting robot and a line tail pre-lifting robot. The line head pre-lifting robot, the line tail pre-lifting robot and the jacking mobile robot cooperate to lift the automobile and place the automobile body on the jacking platform of the jacking mobile robot. The robot scheduling center of the robot management system is connected with each robot on each process production line in a wireless communication mode, is used for receiving tasks / data from the process digital management platform, is also used for receiving the working state of each station and each robot and production line beat data, and is used for determining the walking or operation action of the mobile robot. Therefore, the requirements of assembling and producing different automobile types, different chassis and different models of automobiles on the same production line can be met.

The application 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; according to product carrying instructions and a transportation route, the method collects bearing sensing data to identify a product height value, generates an auxiliary scheduling instruction if the product height value is higher than a reference value, then searches for a nearby AGV without load as a temporary robot, obtains a scheduling value by combining the task amount and distance of the temporary robot and sorting, selects an optimal robot as a slave robot, and pairs the slave robot with the original AGV robot to jointly carry the product, and finally the master and slave robots respond to the matched scheduling instruction. Through master-slave cooperation and accurate scheduling, the method improves the transportation stability and efficiency of the AGV.

This invention discloses a method and system for self-organizing platooning and collaborative scheduling of multi-vehicle isomorphic robot swarms, belonging to the field of intelligent logistics. It includes: constructing a representation function of the spatiotemporal characteristics of cargo handling; robot scheduling based on position-task objective decision-making; self-organizing collaborative path planning for multi-vehicle isomorphic robots; and collaborative optimization of the platooning speed of multi-vehicle isomorphic robots. Employing the technical solution of this invention effectively improves the efficiency and flexibility of logistics warehousing, enhances the system's adaptability to complex environments, and improves the safety and stability of transporting large and bulky goods.