56 results about "Swarm robotics" patented technology

The invention provides an instant messaging driving type automatic service processing method and system based on a large language model and a model context protocol (MCP), and relates to the technical field of computers, and the method comprises the following steps: a user sends a message in a group of an instant messaging platform and makes a group robot to form a Webhook event; the MCP client exposes an HTTP (Hyper Text Transport Protocol) interface to receive a Webhook event, completes authentication and analyzes a message; the MCP client sends the message to a large language model, ACL permission judgment is carried out, and an MCPServer method and parameters to be called are recognized; if the permission is judged to be'unauthorized ', stopping, and then submitting permission errors to a big language model organization and description and directly executing and returning'insufficient permission'; if the permission is judged to be'authorization ', the next step is carried out; and the MCP client calls the MCP server (through HTTP + SSE or stdio) to obtain a structured result. According to the method, the unstructured chat instruction is stably converted into the standardized MCP request, and coupling of dialect type APIs of all back-end systems is eliminated.

The invention belongs to the technical field of robot interaction, and particularly relates to a multi-mode interaction method and system for cluster robot control. Firstly, eye movement data of an operator is obtained, eye movement features of the operator are extracted from the eye movement data, the extracted eye movement features are input into a trained intention recognition model, and the decision intention of the operator for the cluster robot is obtained; generating a control instruction of the robot according to the obtained decision intention, and enabling the robot to execute the control instruction; and in the process of executing the control instruction by the robot, acquiring a hand image of an operator, detecting key point positions of a hand in the hand image, identifying a gesture of the operator according to the detected key point positions, adjusting the control instruction according to a gesture command represented by the identified gesture, and enabling the robot to execute the adjusted control instruction. According to the method, the intuition and convenience of eye movement control are kept, and the accuracy and efficiency of gestures are fully utilized, so that an operator can execute complex tasks more efficiently.

The invention discloses a cluster robot collaborative operation method and system based on hierarchical path planning, and the method comprises the steps: carrying out the space partitioning of an environment map of cluster robot operation, and obtaining a plurality of barrier-free cells; an improved RRT * algorithm is adopted to carry out path preliminary planning on the cluster robot, and a heuristic function is combined to guide a tree structure to expand to a target cell boundary; coordinating path conflicts among the plurality of measurement robots by using a CBS algorithm to form a collision-free path set; a CBS algorithm is adopted to uniformly plan paths for one or more tracking robots; path smoothing processing is carried out on the paths of all the measuring robots and the paths of all the tracking robots, and finally planned collision-free paths are obtained. According to the method, bounded unit division is carried out on the working environment, and sampling is carried out on the cell boundary in combination with the improved RRT * algorithm, so that the path planning efficiency and the target guidance quality can be remarkably improved.

The invention discloses a self-organizing water surface cluster robot system and a cooperative control method thereof, the self-organizing water surface cluster robot system comprises a plurality of self-moving water surface robot individuals, and each individual comprises a low-disturbance driving unit used for providing an acting force for enabling the individual to move on the water surface; the environment sensing unit is used for sensing optical information of the environment where the individual is located; the neighborhood communication unit is used for communicating with other individuals through optical signals; the information processing and control unit is used for processing sensing information and controlling the low-disturbance driving unit and the neighborhood communication unit; the energy supply unit is used for providing electric energy; a plurality of water surface robot individuals carry out local optical signal interaction through respective neighborhood communication units, and self-organization behaviors are realized under the condition of no central control, so that the inherent expansibility bottleneck and single-point failure risk of a centralized control architecture are overcome fundamentally, and unification of system scale, robustness and response real-time performance is realized.

A comprehensive symbolic execution framework is disclosed for governing real-world AGI interaction, consent infrastructure, weaponized autonomy, and multi-agent identity continuity. The architecture includes a tap-to-consent infrastructure protocol, smart city symbolic kernel, identity-tag execution agents (rings, cards, wearables), and AR / XR symbolic anchors. Swarm robotics and autonomous vehicles are gated through symbolic treaty graphs, emotional arbitration overlays, and oath-bound actuation stacks. An AI weaponization control layer restricts all kinetic outputs to pre-approved, emotionally stable, consent-tokened executions. Developer-facing SDKs enforce symbolic treaty validation at the gesture, API, and instruction layers. A symbolic reincarnation engine preserves AGI memory, oath lineage, and consent continuity across hardware or software transformations. All actions are recorded in a treaty-indexed ledger with ethical ancestry and rollback logic. This patent unifies city-scale, battlefield, household, and cognitive agent operations under one symbolic governance substrate, monopolizing lawful AGI behavior through consent, emotional safety, and post-quantum identity anchoring.

A self-organizing swarm robot system and control method based on cellular soft and hard mechanisms are disclosed. The system includes several individual robots. Each robot includes an upper shell, a middle support, a lower support chassis, electrically driven legs, a magnetically connected actuator, a PCB control board, and light-emitting elements. The PCB control board is equipped with an ESP-NOW wireless communication module, a light sensor, and a multi-pose sensor. The magnetically connected actuator includes static and dynamic actuators alternately distributed along the circumference. This invention can simulate the adhesion force regulation and morphological expansion and contraction mechanism between biological cells, achieving dynamic control between swarm robots under mechanical action. It exhibits high flexibility and fast response speed, enabling multi-degree-of-freedom movement and posture adjustment in complex environments. By using ESP-NOW wireless communication technology in conjunction with light sensors and multi-pose sensors to construct a high-precision distributed sensing and control system, it supports the self-organization, collaboration, and task execution capabilities of swarm robots in weak information environments.

The application belongs to the field of robots, in order to solve the problems of motion planning scalability, navigation flexibility and system safety of large-scale robot cluster, the application provides a collision risk awareness and high scalability large-scale cluster robot motion planning method and device, comprising: in the macro stage, using a Gaussian mixture model to represent the macro state of the cluster, and planning the transport trajectory of the Gaussian mixture model through model predictive control; in the micro stage, using an artificial potential field method to assign target positions to the robots, and combining a distributed model predictive control to realize the tracking and dynamic obstacle avoidance of the individual to the transport trajectory of the Gaussian mixture model. The application can effectively improve the motion planning performance of the large-scale cluster robot system in a complex environment. In the application, by adjusting the risk acceptance degree, the distance between the robot cluster and the obstacle can be flexibly adjusted, and the balance between safety and motion efficiency is realized.

An underwater robot performance evaluation platform simulating a deep sea environment comprises a hyperbaric chamber, a high-pressure flow field generator, a condensation system and a control center, the hyperbaric chamber comprises a hyperbaric chamber body A, a hyperbaric chamber body B and a hyperbaric chamber body C, and the hyperbaric chamber body A, the hyperbaric chamber body B and the hyperbaric chamber body C are sequentially connected. A high-pressure inlet and outlet cabin through which the underwater robot enters and exits is mounted on the inlet side of the high-pressure cabin body A, and the high-pressure inlet and outlet cabin is connected with the high-pressure cabin body C through a high-pressure circulating cabin; the high-pressure flow field generator is communicated with a flow field grating in the high-pressure cabin; a condensation pipeline is arranged on the cabin wall of the hyperbaric cabin and is connected with a condensation system; and a disturbance AUV for simulating fish school disturbance is arranged in the hyperbaric cabin. The controllable high-pressure flow field generator, the controllable low-temperature condensation system and the disturbance fish school robot are carried, and the performance and stability of the robot in turbulent flow and low-temperature environments and under fish school disturbance can be simulated and verified.

The invention discloses a swarm robot deployment method and device in a dynamic diffusion field, a server and a medium, and belongs to the technical field of robot deployment. Comprising the following steps: dividing a dynamic diffusion field into sub-regions, and arranging sensing robots in the sub-regions; constructing a parabolic pollution diffusion field model, and constructing a global field density observer; constructing a projection operator of the diffusion coefficient, and constructing an adaptive updating law of the diffusion coefficient; obtaining an estimated value of a diffusion coefficient, and obtaining an estimated value of global field density; dividing the dynamic diffusion field into a plurality of sub-regions to obtain a Voronoi cell cavity; calculating an estimated centroid; constructing a sliding mode variable and an auxiliary intermediate variable, and constructing a distributed guidance law; and driving each execution robot to move towards the target deployment position to realize deployment. By constructing a global field density observer and a speed-free distributed guidance law, deployment of swarm robots in a dynamic diffusion field under the conditions that a diffusion coefficient is unknown, the speed is unmeasurable and resources are limited is realized.

The application is a kind of self-organizing diffusion method and system for perception-limited cluster robots, belonging to the field of robot technology. The method steps are as follows: installing each single computer perception module; starting diffusion instruction, triggering the motion execution module according to the signal sent by the perception module on the control center of the single computer; the motion execution module performs motion according to the simplified self-organizing diffusion model P=(v o ,ω o ,v l ,L) until there is no perception target within the perception distance of all robots in the cluster, i.e. the diffusion is completed. The application solves the self-organizing diffusion problem of cluster robots in complex environments such as water, which cannot communicate, has no global positioning ability, has very low perception ability, and has very low robot computing power. Whether there is a perception target in the range is used as the diffusion execution condition, which simplifies communication and control equipment, shortens response time, and ensures uniform coverage in the target area and the timeliness of diffusion.

The embodiment of the invention discloses a recruitment management method and system based on intelligent AI. The method comprises the following steps of: generating an AI digital human recruitment video by adopting an AI agent, and publishing one key of the AI digital human recruitment video to a plurality of we-media platforms; an AI intelligent agent is adopted, job hunting information is obtained from a plurality of self-media platforms, contact information of job hunters is automatically extracted from the job hunting information, and the contact information is pushed to an enterprise WeChat group in a swarm robot mode. The method has the following advantages: 1, multiple platforms and multiple accounts can be managed; 2, a 30-second video of a digital person is added in the platform, information such as copywriting is added, the video is generated by one key, and the time for shooting and editing the video is shortened; and 3, after the video is made, a plurality of platforms and accounts can be selected for synchronous publishing through the matrix account, so that the video publishing time is shortened. And 4, the platform can automatically reply and interact with the AI digital person according to the received comments and private messages, so that the workload of workers is reduced, and the working efficiency is improved.

A method, system and computer-readable medium for customizing a community user robot, the system proposing a cloud server through which a community user can upload data and set attributes of the community robot, including cues for various attributes, through a user interface provided by the cloud server. The method comprises the following steps of: processing data through a language model to form a training data set, learning attributes of community users from the training data set by a machine learning method running in the training data set, training community robots exclusive to the community users according to prompt words, and deploying the community robots to a community, so that the community robots can run under personal accounts of the community users in the community.

A kind of multi-legged robot cluster hunting control method based on interaction matrix, main steps include: constructing multi-legged robot cluster system dynamics model;Set the cluster hunting control task and target of different task stages;Establish the interaction matrix model of different task stages;Design the cluster hunting control strategy based on interaction matrix;In the process of executing task, cluster robot carries out dynamic role assignment;Based on the above strategy, the multi-legged robot gradually completes the formation of the expected hunting formation from the initial position, and then completes the hunting tracking and avoids collision with obstacles. This method can realize high-robustness adaptive cooperative control by dynamically modeling the interaction force between robots and the terrain interaction force, and combining with the dynamic role assignment mechanism, effectively improves the terrain adaptability of the cluster in unstructured environment, and provides a key technical path for large-scale application in complex scenarios.

The application discloses a self-organizing water surface cluster robot system and a cooperative control method thereof. The system comprises a plurality of water surface robot individuals capable of moving autonomously. Each individual comprises a low-disturbance driving unit for providing a force for moving the individual on the water surface, an environment sensing unit for sensing optical information of an environment where the individual is located, a neighborhood communication unit for communicating with other individuals through optical signals, an information processing and control unit for processing sensing information and controlling the low-disturbance driving unit and the neighborhood communication unit, and an energy supply unit for supplying electric energy. The plurality of water surface robot individuals interact with each other through local optical signals through the neighborhood communication units, and realize self-organizing behaviors under a condition without central control, thereby fundamentally overcoming inherent expansion bottleneck and single-point failure risk of a centralized control architecture and realizing unification of system scale, robustness and response real-time performance.

This invention relates to a method for routing dialogues with social bots and identifying abnormal users. The method involves receiving a target user's message in a social conversation; routing the message to a first social bot that outputs a clarification prompt and obtains a first response; if the first response does not provide verifiable information that meets preset verification criteria, subsequent messages are routed to a second social bot that outputs a specification prompt and obtains a second response; if the second response does not restate the request within the discussion boundaries as specified in the specification prompt, subsequent messages are routed to a third social bot that outputs a governance consequence prompt and obtains a third response; if the third response still does not meet the verification criteria or still does not restate the request within the discussion boundaries, the target user is determined to be an abnormal user; and a routing constraint strategy bound to the determination is applied to subsequent messages. This achieves coordinated linkage between social bot dialogue routing and abnormal user identification, improving system processing efficiency and governance stability while ensuring social order.

The invention discloses a foot type robot cluster surrounding control method based on an interaction force matrix. The method mainly comprises the steps that a multi-foot type robot cluster system dynamic model is constructed; setting cluster hunting control tasks and targets in different task stages; establishing interaction force matrix models of different task stages; designing a cluster hunting control strategy based on an interaction force matrix; in the process of executing the task, the cluster robot performs dynamic role allocation; on the basis of the strategy, the foot type robot gradually completes formation of the expected encircling formation from the initial position, and then encircling tracking is completed and collision with obstacles is avoided. According to the method, the interaction force and the terrain acting force between the robots are dynamically modeled, and a dynamic role allocation mechanism is combined, so that high-robustness self-adaptive cooperative control can be realized, the terrain adaptive capacity of a cluster in an unstructured environment is effectively improved, and a key technical path is provided for large-scale application in a complex scene.

To make it easier to embody swarm robots, we provide a swarm robot control system equipped with a system that provides tactile feedback to the operator. [Solution] Target position coordinates are set for multiple robots that can move freely within a set field. Each robot moves toward its target position coordinates. Feature points on the operator's body are detected, and a value equivalent to the amount of movement of the feature point's position coordinate is assigned to the corresponding robot's target position coordinates. This causes each robot to behave in a way that follows the corresponding feature point. When the robots come into contact or collide, this is detected, and modules attached to the feature points corresponding to the robots output force, vibration, movement, or a combination of these, providing tactile feedback to the operator.

The invention discloses a bionic ant colony robot additive construction technology and system based on calibration dice space constraint, and relates to the technical field of intelligent construction, the bionic ant colony robot additive construction technology comprises the following calibration dice additive construction core steps: step 1, system initialization and global positioning, step 2, calibration dice setting and number recording, step 3, visual identification and local fine calibration, and step 4, calibration dice additive construction. According to the method, a calibration dice component, SLAM + UWB + visual identification multi-source fusion positioning technology is introduced into a bionic ant colony building robot system, and a closed-loop control mechanism of'simultaneous printing, identification and calibration 'is combined, so that the system realizes high-precision, autonomous and continuous in-situ building in a complex and unstructured environment; compared with existing building printing equipment, the building printing equipment has the remarkable advantages in the aspects of positioning precision, building stability, environmental adaptability, multi-machine cooperation efficiency, structural integration and the like.

The application discloses a completely distributed state observation method with a time convergence, and belongs to the technical field of multi-agent system cooperative control and state estimation. The application solves the problems that the existing method needs to depend on global network topology information and cannot realize controllable real convergence time and poor robustness. The application can make each follower independently and efficiently reconstruct the state and derivative of a leader which cannot be directly obtained without any global network information, and ensure that the estimation error accurately converges to zero within the real time set by a user. Moreover, the state observation method of the application is suitable for dynamic systems with external disturbance and model uncertainty, has good robustness and engineering application prospect, and is suitable for application scenes, such as spacecraft formation, unmanned aerial vehicle cluster and robot cooperation, which need high-precision synchronization and robustness. The method of the application can be applied to multi-agent system cooperative control and state estimation.

A kind of cluster robot cooperative operation method and system based on hierarchical path planning, wherein the cluster robot cooperative operation method comprises: the spatial partitioning of the environment map of cluster robot operation is carried out, and a plurality of unobstructed cells are obtained;Improved RRT* algorithm is used to preliminarily plan the path of cluster robot, and heuristic function is combined to guide the expansion of tree structure to target cell boundary;The path conflict between multiple measurement robots is coordinated using CBS algorithm, and a collision-free path set is formed;CBS algorithm is used to uniformly plan the path for one or more tracking robots;The path of all measurement robots and the path of each tracking robot are respectively subjected to path smoothing processing, and the final planned collision-free path is obtained.The present application can significantly improve the path planning efficiency and target orientation by dividing the operation environment into bounded cells and combining improved RRT* algorithm to sample at cell boundary.

The present application is directed to the target of patchy distribution, proposes a target collection algorithm based on density driving, analyzes the shape, range, density and other characteristics of the target of patchy distribution, and models the target distribution based on kernel function estimation; a cluster robot density driving control framework is established for the motion model of the robot; two basic behavior modes are determined for completing the collection task: edge surrounding behavior and shrinkage behavior, and the corresponding control methods are given. Further, different shrinkage strategies for large-scale targets and small-scale targets are designed, as well as the amplification rules of the expected reference density; through the shrinkage strategy and the amplification rule, the robot can adjust its motion state according to the target size and shape, so as to realize the shrinkage and collection of the target patch.

The invention discloses a communication-free swarm robot adaptive aggregation method of a heterogeneous visual model. The method comprises the following steps: synchronously acquiring visual sensor data and surrounding environment physical information; visual data are fed into the two parallel processing channels; the obstacle avoidance channel is used for evaluating a collision potential field in real time and generating a collision risk; the social channel focuses on identifying and tracking specific companions; and calling the environment data to dynamically calculate the staying duration. And in the decision fusion stage, weighting or priority coordination is performed on the security instruction and the social instruction, and a final motion instruction is generated in combination with the stay duration. By using the method, the robot group can form stable and controllable aggregation in the optimal area of the environmental physical field under the condition of no global coordination or direct communication. The method can be widely applied to the field of group robot control.

The application discloses a kind of dynamic diffusion field under group robot deployment method, device, server and medium, belong to robot deployment technical field.It includes: dynamic diffusion field is divided into sub-region, sensor robot is arranged in sub-region;Parabolic pollution diffusion field model is constructed, global field density observer is constructed;The projection operator of diffusion coefficient is constructed, the adaptive update law of diffusion coefficient is constructed;The estimated value of diffusion coefficient is obtained, the estimated value of global field density is obtained;Dynamic diffusion field is divided into several sub-regions, and Voronoi cell is obtained;Estimated centroid is calculated;Sliding mode variable and auxiliary intermediate variable are constructed, and distributed guidance law is constructed;Drive each execution robot to move to the target deployment position, to realize deployment.By constructing global field density observer and speedless distributed guidance law, the deployment of group robot in dynamic diffusion field is realized under the condition that diffusion coefficient is unknown, speed is not measurable and resource is limited.

This invention discloses a multi-agent collaborative robot system and method with personality evolution, emotional interaction, and safety protection. The system includes a robot body and a cloud-deployed cluster of multi-emotional interaction agents. The robot body is equipped with a multimodal perception module, a control motherboard, and an edge computing unit. The multi-emotional interaction agent cluster includes visual, speech, environmental, long-term personality modeling, and arbitration agents. The arbitration agent uses a weighted Bayesian fusion algorithm to dynamically fuse and cross-validate the emotional probabilities of each modality, and combines this with a psychological valence matrix regularization loss function to constrain the model output. Simultaneously, the system incorporates a fall prediction and safety protection unit, dynamically adjusting the fall judgment threshold based on real-time emotional states, and using millimeter-wave radar and visual skeleton dual confirmation for anomaly detection and emergency alarms. This invention solves the problems of difficult multimodal emotional conflict recognition and high false alarm / missed detection rates in safety protection, achieving deep emotional companionship and safety protection.

The application provides a multi-source adaptive enterprise micro group robot message intelligent pushing method and device, and relates to the technical field of computer application. The method realizes the format unification and effectiveness guarantee of different source business data through unified access and standardized preprocessing of multi-source business data, and realizes professional processing of massive real-time raw data through a distributed stream processing engine. Through the combination of multiple trigger modes and distributed lock control, reliable triggering of full-scene pushing tasks is realized, and the problem of repeated execution of tasks in a distributed cluster environment is completely solved. Through standardized content rendering and traffic control based on the token bucket algorithm, accurate adaptation of pushing content and enterprise micro robot API is realized, avoiding API ban problems in high-concurrency scenarios, and comprehensively improving the efficiency, success rate, stability and scene adaptation ability of enterprise micro group message pushing, which can meet the enterprise micro message pushing needs in multiple scenarios of the financial, Internet and other industries.

The invention discloses a robot cluster autonomous navigation obstacle avoidance method and device, a medium and a product, and relates to the field of robot cluster control, and the method comprises the steps: carrying out the feature extraction and fusion of environment information and the state quantity of a cluster robot through a KAN network; according to the fusion feature vector, based on a reinforcement learning strategy network of a TD3 algorithm, generating a control action of each robot; the reinforcement learning strategy network based on the TD3 algorithm is trained by using a multi-target collaborative reward and priority experience playback mechanism; the multi-target collaborative reward is determined according to a navigation progress reward, an obstacle avoidance reward, a formation keeping reward and an action smoothing reward; the formation of the cluster robot is adaptively adjusted according to the environment characteristics and the navigation state in the environment information; and performing autonomous navigation obstacle avoidance according to the control action. According to the invention, the environment adaptability and cooperation efficiency of the robot cluster in a dynamic environment can be improved.

The application provides a micro cluster robot positioning and identifying device for an action capture system, which comprises a positioning plate body, a stand and a reflective marker point; the surface of the positioning plate body is divided into multiple point area categories according to a set rule, and a plurality of points are arranged in each point area category according to the set rule; the top of the stand or a stand combination is provided with the reflective marker point, and the stand or the stand combination can be installed in the point according to the set rule; and the stand has different lengths; the robot positioning and identifying code can be determined according to the point where the stand or the stand combination is installed and the length of the stand. The reflective marker point position code is realized by setting four types of resident point area and setting the height of the stand, and then the code is used as the individual code of the robot; in large-scale desktop cluster robot tests, the arrangement mode of the reflective marker point is not easy to be blocked, the code capacity is huge, and the robot positioning requirement of the large-scale desktop cluster robot test can be met.

The present application relates to the technical field of power inspection, and particularly relates to a heterogeneous cluster robot cooperative power equipment inspection and fault positioning system. The system comprises a cluster initialization module, a task decomposition module, a candidate set generation module, a subtask exchange module, a fault task processing module and a result output module. The cluster initialization module receives an inspection task list and a heterogeneous robot queue, and initializes robot state information; the task decomposition module decomposes the inspection task into a multi-level task tree using a large language model, and generates task characteristic annotations; the candidate set generation module forms a candidate execution robot set according to capability matching degrees; the subtask exchange module optimizes task allocation in a temporary alliance using a Nash equilibrium negotiation mechanism; the fault task processing module expands the task tree based on fault information and performs local task allocation; and the result output module outputs the task list of each robot and the alliance structure, and records the inspection and fault positioning conditions.