44 results about "Cloud robotics" patented technology

The present disclosure discloses a cloud robot system, including: a cloud. computing platform and at least one robot; wherein the cloud computing platform is used for receiving perform information sent by the at least one robot in the system; the perform information includes data, status and requests of the at least one robot; the cloud computing platform is used for processing the data and status, sending process results back to the at least one robot, and sending control instructions to corresponding robot according to the requests; the at least one robot is used for sending the perform information to the cloud computing platform, receiving process results from the cloud computing platform, and performing according to the control instructions sent from the cloud computing platform. By using the present disclosure, computing ability and storage capacity of the robots can be expanded unlimited, while the thinking ability and memory of the robots are improved. Besides, the ability of the brains of the robots can be allocated according to demand, thus lowering the cost of the robots.

The present disclosure discloses a cloud robot system, including: a cloud computing platform and at least one robot; wherein the cloud computing platform is used for receiving perform information sent by the at least one robot in the system; the perform information includes data, status and requests of the at least one robot; the cloud computing platform is used for processing the data and status, sending process results back to the at least one robot, and sending control instructions to corresponding robot according to the requests; the at least one robot is used for sending the perform information to the cloud computing platform, receiving process results from the cloud computing platform, and performing according to the control instructions sent from the cloud computing platform. By using the present disclosure, computing ability and storage capacity of the robots can be expanded unlimited, while the thinking ability and memory of the robots are improved. Besides, the ability of the brains of the robots can be allocated according to demand, thus lowering the cost of the robots.

The invention discloses a semantic map construction method based on cloud robot mixed cloud architecture, and aims to achieve a proper balance for improving object identification accuracy and shortening identification time. The technical scheme of the method is that mixed cloud consisting of a robot, a private cloud node and a public cloud node is constructed, wherein the private cloud node obtains an environment picture shot by the robot and milemeter and position data on the basis of an ROS (Read-Only-Storage) message mechanism, and SLAM (Simultaneous Location and Mapping) is used for drawing an environmental geometric map in real time on the basis of the milemeter and position data. The private cloud node carries out object identification on the basis of an environment picture, and an object which may be wrongly identified is uploaded to the public cloud node to be identified. The private cloud node maps an object category identification tag returned from the public cloud node and an SLAM map, and the corresponding position of the object category identification tag on a map finishes the construction of a semantic map. When the method is adopted, the local calculation load of the robot can be lightened, request response time is minimized, and object identification accuracy is improved.

The invention provides a cloud robot system, a robot and a robot cloud platform. A robot body comprises an acquisition module and a transmitting module; the robot cloud platform comprises a determining module, a cloud robot module and anoperator service module, wherein the acquisition module is used for acquiring environment information; the transmitting module is used for transmitting the environment information to the robot cloud platform; the determining module is used for determining the cloud robot module or the operatorservice module to process the environment information; and the determined cloud robot module or operator service module is used for processing the environment information and transmitting an instruction to the robot body. According to the cloud robot system, the robot and the robot cloud platform provided by the invention, by adding the operator service module, the robot body transmits the acquired environment information to the robot cloud platform, and then the robot cloud platform can remotely control by adopting two modes to guarantee the service quality for users, thereby improving user experience.

The invention discloses an autonomous metal development cloud robot system based on cloud computing. The autonomous metal development cloud robot system based on cloud computing comprises an image processing module, a robot autonomous metal development module, a cloud computing module, a mobile robot module, a multi-channel wireless communication module and a smart mobile terminal module, wherein the robot autonomous metal development module is realized in the cloud computing module, and data exchange is conducted between the mobile robot module and the image processing module, the cloud computing module and the smart mobile terminal module through the multi-channel wireless communication module in a wireless communication mode. According to the autonomous metal development cloud robot system based on cloud computing, in the process of realizing autonomous metal development of a robot, it is avoided that the mobile robot completes the intensive computing tasks by carrying a large number of computing resources, all the tasks are completed by the cloud computing module, and the workload for executing the intensive computing tasks of the robot can be effectively lightened; in addition, knowledge stored by the cloud computing modules can be shared by different robots.

The invention relates to the electric robot technology field and especially relates to an autonomous charging system of a cloud robot and a method thereof. The system comprises: a cloud calculating platform, a cloud robot and charging equipment. The charging equipment carries out characteristic information interaction with the cloud robot. The cloud calculating platform is used to monitor characteristic information of the cloud robot and the charging equipment, receive and process a request of the cloud robot and send a control instruction to the cloud robot. The cloud robot carries out autonomous charging according to the characteristic information interacted with the charging equipment and the control instruction sent by the cloud calculating platform. In the invention, the information can be comprehensively and conveniently collected and shared so that the cloud robot can adopt a complex outdoor environment and the autonomous charging of the cloud robot can be realized.

The present invention provides a cloud robot system and an implementation method, relating to the technical field of cloud robots. The system comprises a Rabbit MQ-based message middleware, a JSON-based message parser and an OpenStack SDK-based virtual resource automatic configuration module. Firstly, the Rabbit MQ-based message middleware receives, stores and forwards a robot request message, then the JSON-based message parser parses the request message, so as to obtain parameters for creating and configuring cloud virtual resources, and finally, the OpenStack SDK-based virtual resource automatic configuration module automatically creates and configures cloud resources like a network and a virtual machine according to the parsed parameter information, so as to enable the virtual machine and a robot to connect and communicate with each other, so that the robot is controlled according to the cloud virtual resources. Through adoption of the method and system, the request message sent by the robot can be received, the cloud virtual resources can be automatically created and configured, and the robot can use the cloud resources according to needs.

The invention provides a method for designing a big data cloud drive robot. The design thought is thin terminal (namely a robot terminal) plus fat network (namely a cloud platform). The robot terminal is only responsible for execution, and all behaviors of the robot are controlled by the cloud platform. The method mainly includes the steps that 1, an information acquisition module of the robot collects the information of the surrounding physical world and sends the information to the cloud platform through a wireless communication module of the information acquisition module; 2, the cloud platform conducts computing on obstacle distribution diagram construction, object model recognition, the sweeping mode and other tasks through resources in software resources based on data in big data resources according to a big data drive work principle, and a command executed by the robot is generated; 3, the cloud platform sends the command to the terminal through a network module, a decision system of the terminal analyzes the command, and a movement control module, a cleaning module and the like execute actions according to the command; 4, new big data are generated according to the action execution result, the data are sent back to the cloud platform, and the big data resources are updated. By means of the method, the networked and digital robot which is high in intelligence, low in energy consumption, low in cost can be designed.

The invention provides a cloud robot collaborative learning method based on hybrid enhanced intelligence. The method comprises the following steps of S1, adopting a meta-learning method of NTP (NeuralTask Programming) for dividing a large task recursion into simple sub-tasks for teaching robots; S2, achieving robot action imitative learning based on a human-computer interaction technology, and teaching the robots about skills; S3, utilizing a self-organizing incremental neural network (SOINN) to gather the scattered robot skills for sharing use. According to the method, multiple robots learnhuman skills separately, then upload learned information into a server and share the information in the server for training and adjustment. Not only can the learning time be greatly shortened, but also the diversity of tasks can be expanded. The robots conduct collaborative learning and transmit experiences to one another through a network (or a cloud robot), that is to say, the robots can learn from one another.

The invention provides a big data intelligent cleaning system and a cloud robot intelligent cleaning service platform, and belongs to the field of big data processing. The system comprises an input module, a data processing module, a storage module and a cleaning module; the input module is used for being provided for a user to input at least one type of precleaning data; the data processing module is connected with the input module, and used for conducting automatic scanning in the precleaning data to extract target fields, and analysable modules are obtained through processing; the storage module is connected with the data processing module and comprises at least one database, each database is used for storing one type of precleaning data and the corresponding analysable module, and usedfor conducting cleaning processing on the precleaning data to obtain standardized data and output matched information, and cleaning processing comprises the steps of model matching processing and voice verification processing. The big data intelligent cleaning system and the cloud robot intelligent cleaning service platform have the advantages that the cleaning cost is greatly lowered while the data cleaning efficiency is greatly improved.

The invention discloses a system and a method for navigating an indoor cloud robot. The system comprises a cloud terminal, wherein the cloud terminal is connected with a UWB base station group and a robot group respectively; the UWB base station group is configured to detect the positions of corresponding robots in the robot group in a global environment and feeding back the detected positions tothe cloud terminal; the cloud terminal is configured to send a navigation command to an appointed robot and receive the local minimum subgraph of the current position constructed by the appointed robot; the cloud terminal is configured to determine the orientation of the robot by using the local minimum subgraph of the current position constructed by the appointed robot and the position in the global environment; the cloud terminal is configured to download the pre-planning global path of the appointed robot from the current position to a navigation end point and a middle navigation point; thecloud terminal is configured to receive the local minimum subgraph of the appointed robot at the middle navigation point, optimize the posture of the current robot in combination with the position inthe global environment and then optimize the global path and adjust the middle navigation point; and the cloud terminal is configured to download the optimized global path, the posture and the middlenavigation point to the appointed robot for continuous navigation until a navigation end point is reached.

The invention provides a cloud robot distributed control system and a method based on a dynamic Agent. The system comprises a knowledge base Agent and a core Agent. The method comprises the steps of building a cloud robot ROS (Read-Only-Storage) node; deploying the knowledge base Agent; receiving a task instruction through a cloud robot, connecting the core Agent to an Internet so as to build reliable communication connection between the core Agent and the knowledge base Agent, and applying to the knowledge base Agent for downloading; receiving a download request through the knowledge base Agent, searching for a corresponding function application, downloading the corresponding function application into an operating system ROS of a local cloud robot, and regarding the corresponding function application as the dynamic Agent; activating the dynamic Agent through the core Agent, executing the task through the core Agent, and returning a task execution result to a user through the core Agent; and after the completion of the task, controlling the dynamic Agent to unload a cloud computing Agent through the core Agent, then unloading the dynamic Agent through the core Agent, and realizing dynamic unloading. According to the cloud robot distributed control system and the method based on the dynamic Agent provided by the invention, the problem that the computing capacity and the functions are limited when the cloud robot executes the complex task is solved, the on-demand loading and the dynamic unloading of the cloud robot functions are realized, and the computing resources and the storage resources of the cloud robot can be saved.

The invention discloses a cloud robot interactive method and cloud robot based on a cloud platform and the cloud platform. The method comprises the following steps that according to an obtained imageof surrounding environments, image processing is carried out, whether obstacles exist in the surrounding environments or not is judged, planned obstacle avoidance path information is received, obstacle avoidance is completed according to the planned obstacle avoidance path information, and an interaction including sending information to the cloud platform and receiving the information sent by thecloud platform can be carried out through a transmission control protocol / an internet interconnection protocol and the cloud platform. In this way, effective obstacle identification of the cloud robotand the obstacle avoidance can be realized, data transmission is stable, and the probability of packet loss is less in the process of the data transmission.

The invention discloses a cloud robot system. The system comprises a cloud server platform and an entity robot; the cloud server platform comprises a cloud robot engine module and a cloud programming engine module; the cloud programming engine module carries out signal transmission between the cloud server platform and the cloud robot engine module, and the entity robot carries out signal transmission with the cloud robot engine module in a cloud service manner; the entity robot comprises a plurality of hardware function modules; the hardware function modules follow the hardware function module standard, and due to the hardware function module standard, the hardware function module can carry out registering and adding in a cloud service manner. According to the cloud robot system, the hardware cost of the robot is reduced, the reusability and maintainability of the system are improved, rapid calling and command treatment of the robot under the familiar site are used for conducting the execution action, and richness of intelligent services of the robot is ensured.

The invention relates to a cloud robot edge computing unloading model and an unloading method thereof. According to the technical scheme provided by the invention, the method comprises the steps thatfirstly, an edge server placement model is established, then local and edge server task processing models are established, and then a game theory model is established; an initial clustering center isdetermined; clustering is performed according to the target function; and unloading is carried out by a task unloading method based on an improved game theory algorithm. On one hand, research is carried out from the placement problem of the edge server, an improved k-means algorithm is provided in a many-to-many scene of the cloud robot and the edge server, and the problems that the workload pressure of a single edge server is too large and the task processing efficiency is low are solved; and on the other hand, on the basis of edge server placement research, for the problem of partial task unloading of the heterogeneous cloud robot, fine-grained segmentation is carried out on the tasks, an improved game theory algorithm is provided, an optimal unloading strategy is researched, the energyconsumption of the cloud robot is reduced, and the task completion time is shortened.

The invention provides an AR-glasses-based cloud robot holographic evaluation system and method. The system comprises local equipment and a robot cloud platform system, wherein one set of local equipment is provided for each remote user, and data interaction among all the remote local equipment is achieved through the robot cloud platform system. The method allows two users to use the system to perform robot holographic evaluation. The AR-glasses-based cloud robot holographic evaluation system and method has the advantages that the actions, three-dimensional model modification and voices of aremote engineer can be transmitted to another engineer who communicates with the remote engineer so as to achieve holographic communication, the own-side engineers can learn about the sight, actions,holographic model operation and voices of the opposite-side engineers, the on-site robot evaluation process of robot equipment is restored to an extremely large extent, efficiency is increased, and the cycle of remote evaluation is shortened.

The invention relates to a multi-robot controller based on an edge cloud service. The multi-robot controller based on the edge cloud service is applied to a multi-robot cooperative control operation scene in a distributed network environment under an edge cloud architecture. The multi-robot controller conforms to the edge cloud technology category and is equipped with a high-performance CPU+FPGA heterogeneous acceleration framework and multiple sets of physical network interfaces, so that edge cloud robot application remote deployment is supported, and multi-robot control model calculation anddistributed collaborative control are realized. In addition, in order to realize multi-robot cooperative control, virtualization technology is adopted to uniformly control hardware resources, networkresources and storage resources of the controller, a high-speed data buffer is created for multi-robot data sharing, and thus integrated high-performance multi-robot control is realized.

The invention provides a power distribution station operation and maintenance monitoring cloud robot system and a background processing and operation task method. An operation and maintenance monitoring system, a cloud robot execution terminal and a cloud robot cloud resource library jointly form a set of complete power distribution station cloud robot system architecture, and technologies such as cloud computing and intelligent robots are fused with power distribution station operation tasks. The system is designed in the form of webpage end software, and operation and maintenance personnel of the power distribution station can enter the system on intelligent equipment connected with an intranet of the power distribution station, execute operation and maintenance tasks of the power distribution station and operate an intelligent robot to complete inspection and operation tasks. A set of complete operation and maintenance monitoring system is provided for a power distribution station cloud robot system architecture, synchronous acquisition of power distribution station environment and equipment parameters is realized, a comprehensive and visual user display interface is provided, the working efficiency of power station operation and maintenance personnel is fully improved, and reliable safety guarantee is provided for electrical equipment of a power distribution station.

The invention provides a cloud robot resource scheduling method and system based on task classification and time sequence prediction, and relates to the technical field of cloud computing resource scheduling, and the method comprises the steps: S1, obtaining a job task sequence of a cloud robot; S2, classifying the tasks according to the dependency relationship between the cloud robot operation tasks and the cloud virtual machine; S3, obtaining an estimated value of the job duration of each job task; S4, converting cloud robot operation task scheduling into a workflow scheduling problem expressed by a directed acyclic graph (DAG); S5, simplifying the DAG generated in the step S4 according to the characteristics of the job task; and S6, solving the final DAG by using various static scheduling methods. According to the method, static analysis can be finally carried out on the operation task of the cloud robot through some preprocessing means, and a better solution for the scheduling problem can be found from the global perspective.

The invention relates to a fitness guidance method based on a cloud robot, and the method comprises the steps: obtaining and storing a fitness action of a user; collecting an image of a motion environment, and constructing an environment map; establishing a double-cascade pyramid network by using a DCPLSTM algorithm, inputting an acquired user motion action image and a standard action image into the double-cascade pyramid network, and extracting skeleton points of the two images; and judging whether the movement action of the user is standard or not according to the comparison result of the skeleton points of the two images, and correcting when the movement action is not standard. The invention further provides a storage medium and a fitness guidance system based on the cloud robot. The fitness guidance method based on the cloud robot, the storage medium and the fitness guidance system can correct wrong actions of a user during fitness.

The invention provides a Docker container-based cloud robot navigation system and a working method thereof. The Docker container-based cloud robot navigation system comprises a cloud control platformand a mobile robot, wherein the mobile robot comprises a first wireless transceiver, a moving body which is used for realizing the own movement, an environmental perception assembly which is used foracquiring image data of the own surrounding environment, and a data processor which is used for drawing a regional map for the image data; the cloud control platform comprises a second wireless transceiver which communicates with the first wireless transceiver, and a server host which is used for receiving acquired data of the mobile robot through the second wireless transceiver and sending a control instruction to the data processor; the server host comprises a Docker ROS container; and the data processor is used for receiving the control instruction through the first wireless transceiver. Through the cloud control platform adopting the Docker container, the system has the advantages of being high in portability, centered on applications and automatic in construction, saving bandwidth resources, reusing assemblies, sharing mirror images and using a tool ecosystem.

The invention provides a clean cloud robot system based on Hadoop. The clean cloud robot system comprises a robot, a communication module and a Hadoop cloud server which are sequentially connected toconduct information interaction; the communication module is used for information transmission between the robot and the Hadoop cloud server; the robot is used for executing a cleaning task, collecting image information of the environment where the robot is located, distance information of objects in the environment and information of the position where the robot is located, and sending the information to the Hadoop cloud server through the communication module; the Hadoop cloud server is used for achieving real-time information collecting and transmitting and determining the actual distance between a trolley and surrounding objects, then plans the optimal path for the robot, and sends optimal path information to the robot through the communication module. The clean cloud robot system adopts a Hadoop as a cloud service architecture, the quantity of raspberry pi clusters can be increased for expanding according to the quantity of the robot, and the cost can be reduced because raspberrypi development boards is low.

The invention discloses a task scheduling method for a cloud robot, and the method comprises the steps: firstly calling a preset random coding function to carry out the random coding of a target feature attribute of the cloud robot corresponding to a target scheduling task, and obtaining a plurality of different initial feature codes; calling a preset genetic algorithm, and determining a target feature code with the highest fitness with the target scheduling task according to the initial feature code; and determining corresponding target feature attribute information according to the target feature code, and scheduling a corresponding target robot according to the target feature attribute information. According to the method, the consumption of human resources is reduced, the task scheduling efficiency is improved, the influence of personal subjective factors of workers can be effectively avoided, and the optimization of the formulated task scheduling scheme of the cloud robot is improved. The invention further discloses a task scheduling device and equipment of the cloud robot and a computer readable storage medium, which all have the above beneficial effects.

The invention discloses a method, a device, equipment and a medium for determining important cloud robot nodes. The method comprises the steps of: constructing a cloud robot set according to a cooperation relation between identifiers of cloud robot nodes and the cloud robot nodes; according to a preset formula and the cloud robot set, determining an importance degree of each cloud robot node in apreset layer, wherein the preset layer is the preset layer number of the cloud robot nodes in a K-Shell decomposition algorithm; and removing the cloud robot nodes of which the importance degrees arelower than a first preset value in the cloud robot set, and determining the important cloud robot nodes in the cloud robot set. According to the method, the device, the equipment and the medium of theinvention, the importance degree of each cloud robot node in the preset layer is determined through utilizing the preset formula and the cloud robot set, and the important cloud robot nodes of whichthe importance degrees are not lower than the first preset value is determined finally, so that targeted protection can be adopted for the important robot nodes, thereby greatly avoiding unnecessary loss caused by emergency situations.

A vehicle remote support system includes a communication system that operates over a plurality of parallel wireless network connections to provide low-latency video from vehicle to a remote support server that provides remote support to the vehicle dependent on real-time video. The vehicle includes a source that encodes multiple versions of the original video segments (e.g., one per wireless network connection) and transmits the multiple versions of the segments to a sink at the remote support server over the respective wireless connections. This redundant multi-path communication system rationally allocates network resources to the managed video streams and balances bandwidth against latency in order to avoid network congestion and safety issues associated with single-path transmissions. In other embodiments, a similar communication system that transmits video or other real-time messages between a source and a sink may be utilized in cloud robotics applications.

The invention discloses a cloud robot task assignment method that jointly considers base station selection and computing migration, including dividing a single robot r task w into five parts, dividing the completion time t of a single robot task w w Use the accumulation of the above five parts to establish a task allocation model; input the task set W of multiple robots r, the robot set R, the base station set B, and upload the corresponding configuration parameters of each task w to the cloud to the task allocation model t w ;Assign task to model t w It is sequentially decomposed into two sub-models to complete the minimization time of all tasks step by step, wherein the first sub-model is a model based on the simulated annealing algorithm; the second sub-model is a model based on the Hungarian algorithm. In the present invention, firstly, the target task is functionalized, and the time to complete all tasks is minimized. Among them, task layout, calculation migration and base station selection are three variables, and then a heuristic algorithm is proposed, which can obtain the allocation strategy of multiple tasks in a short time.