93 results about "Behavior Trees" patented technology

According to the agent construction method and the multi-agent cooperation method provided by the invention, the agent parameter configuration file is acquired, and the agent basic information, the ECA rule, the behavior tree path index and the performance function parameter configuration are loaded from the agent parameter configuration file; the knowledge hypergraph is adopted to aggregate agent basic information, ECA rules, behavior tree path indexes and performance function parameter configuration, and a dynamic knowledge network with semantic association is obtained; and after a prompt word used for calling the large language model is dynamically generated based on the dynamic knowledge network, the large language model is driven to generate an executable code, and when the executable code passes verification, an agent corresponding to the agent parameter configuration file is obtained. And then, aiming at a received user instruction, performing task processing by adopting the constructed multi-agent. Therefore, the autonomous decision-making capability and the dynamic adaptability of the intelligent agent are enhanced, and higher flexibility and expansibility are achieved.

The invention provides a tunnel drilling and blasting slag removal vehicle autonomous path planning method and device based on a Naligation 2 behavior tree scheduling framework, and the method comprises the following steps: S1, carrying out the data collection and map construction of a tunnel scene after drilling and blasting; s2, identifying a blasting residue area and generating a target point; s3, constructing a behavior tree scheduling structure and planning a path; and S4, performing path search, execution and feedback evaluation based on a path planning algorithm. According to the method, multi-source sensor mapping and deep learning semantic segmentation technologies are combined, high-precision recognition and traffic area extraction of residual obstacles in an unstructured tunnel environment after drilling and blasting are performed are performed, complex scenes faced by slag removal path planning are fully considered, a behavior tree scheduling mechanism is adopted to fuse path planning, target scheduling and an anomaly recovery strategy, and the path planning efficiency is improved. And a path execution system of real-time response and fault-tolerant capability is further constructed, and the operation continuity and robustness of the robot in a complex operation environment are improved.

The invention discloses a behavior tree code automatic generation method based on a large language model. The method comprises the following steps: 1, acquiring a behavior tree model of an XML file format defined by a user and prompt information input by the user; 2, analyzing the obtained XML file, and providing a generation rule of a behavior tree code; step 3; the analyzed XML file and prompt information input by a user are combined with a behavior tree code generation rule, and a task specific prompt is generated; 4, combining the task specific prompt with the general code generation prompt to generate a complete prompt; 5, a proper LLM is selected, complete prompt information is input, and the LLM generates codes according to prompts; 6, running the generated code in a safe environment to ensure the safety of the code and the correctness of an output format; and 7, displaying an execution result on a visual interface, auditing the result by a user, and modifying a generated code. The method has the characteristics of high universality, high flexibility and high interpretability.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent training method and device based on a large language model, equipment and a medium, and the method comprises the steps: obtaining role behavior tendency information of agents in a multi-agent cluster; constructing a role behavior tree according to the role behavior tendency information through the first large language model; pre-training the intelligent agent according to the role behavior tree so as to initialize a role behavior strategy and obtain a pre-trained intelligent agent; obtaining a first soft role embedding vector according to the situation abstract through a first large language model, and generating a reward function according to the situation abstract and the first soft role embedding vector; acquiring first local observation information; and performing reinforcement learning training on the pre-trained agent according to the first local observation information, the first soft role embedding vector and the reward function so as to perform differentiation on the role behavior strategy. According to the method, the training efficiency can be improved, and the role division effect, the dynamic cooperation capability and the strategy interpretability of the multi-agent are enhanced.

The invention discloses a software performance test system, method and device in a high-concurrency scene, and relates to the field of software testing, the system comprises a scene intelligent construction module used for generating a concurrency scene through a behavior tree modeling and time sequence prediction algorithm according to service demand parameters and historical traffic data; the distributed test environment scheduling module is used for constructing a distributed test environment by adopting a container arrangement technology; the virtual user load generation module is used for simulating concurrent user requests by adopting a coroutine pool technology; the real-time performance data acquisition module is used for acquiring multi-source performance data of the tested software in real time; the intelligent performance analysis module is used for calculating a core performance index according to the multi-source performance data and carrying out anomaly detection and trend prediction; and the performance bottleneck positioning module is used for mining a dependency relationship among the core performance indexes and generating a bottleneck positioning report and an optimization suggestion document. According to the method, the accuracy, the efficiency and the stability of software performance testing in a high-concurrency scene are improved.

The invention relates to the technical field of robot autonomous decision-making, and discloses an underwater robot behavior decision-making method based on a behavior tree, which describes and controls the specific behavior of an underwater robot based on the behavior tree, organizes and manages complex behavior logic through a tree structure, and analyzes a specific task to determine the behavior of the underwater robot. And decomposing into behavior sub-trees, and combining control nodes and action nodes in the behavior sub-trees to complete a specific logic switching function. Compared with a traditional finite-state machine, the method has the advantages of modularization, expandability and high readability, the coupling degree between modules in the sub-tree is low, the sub-tree can be transplanted to multiple positions of the behavior tree, the problem that the workload of the reuse state of the finite-state machine is large is effectively solved, and the method has good practical application value.

The invention discloses a puzzle solution game adaptive design method and system based on ecological balance, and the method specifically comprises the steps: receiving ecological scene data selected by a player, carrying out the evaluation processing of the capability level of the player through combining with a player capability evaluation model, and generating an evaluation result; dynamically generating hierarchical ecological problems according to an evaluation result, and synchronously activating Agent behavior tree loaders of corresponding hierarchies to form an Agent modeling system; simulating a biological interaction network through an Agent modeling system, inputting real-time operation data of the biological interaction network into an LSTM double-time-sequence prediction engine for prediction processing, and generating an ecological evolution graph; and generating a dynamic visual interface according to the ecological evolution map, and triggering a difficulty regulator when detecting that the ecological imbalance index exceeds a difficulty threshold value of the current player. Complex characteristics of an ecological system are effectively simulated, more challenging and interesting puzzle solving game experience is provided for players, and meanwhile understanding and protection awareness of the players to the ecological system can be improved.

Various embodiments of the teachings herein include a workflow creation method. Some examples include: receiving an operation performed by a user on a GUI constructing a functional block model; receiving an operation performed by the user including nesting a behavior tree into a second functional block, wherein the behavior tree defines an operation for a first workcell, and the functional block model defines an operation for a second workcell; in response to a first functional block in the functional block model triggering an input event of the second functional block, then the second functional block triggers an execution signal to a root node in the behavior tree at a preset frequency, so the behavior tree starts execution from the root node and outputs an execution result; the second functional block triggers a corresponding output event according to the execution result; and generating an instance of the functional block model.

The invention discloses a multi-satellite cooperative task planning method based on a behavior tree. S101, the ground planning layer carries out modeling description of satellite tasks by combing satellite task requirements and sends a behavior tree task template to the high-orbit satellite layer; s102, after receiving the task, the high-orbit satellite layer plans the task according to the behavior tree task template; task decomposition is carried out to obtain meta-task sub-trees (tasks capable of being executed by a single satellite), specific satellite planning is carried out, satellites conforming to task constraints are planned to bear task execution, and the task sub-behavior trees are issued to a low-orbit execution layer; and S103, after receiving the task sub-behavior tree, the low-orbit execution layer binds a function according to a mechanism of the behavior tree and constructs code execution so as to realize execution of a specific task. According to the method, high flexibility, expandability and execution efficiency of task planning are remarkably improved, and the defects of a traditional method in the aspects of complex task decomposition, multi-satellite coordination and real-time response capability are effectively overcome.

The invention provides a motion path planning method for a desktop wheeled robot, which comprises the following steps: when a target task is received, loading a behavior tree corresponding to the target task, the behavior tree comprising a tree logic structure composed of a control flow node, a condition node and an action node; periodically executing the behavior tree through a behavior tree engine so as to determine an execution flow according to a logic structure of the behavior tree; when the execution process triggers a specific path planning action node, a task context corresponding to a current task stage is generated, and the task context is multi-dimensional structured data containing target description and planning constraints; dynamically selecting at least one target motion planning algorithm from a plurality of preset motion planning algorithms based on the task context; and generating a current motion path instruction by using the target motion planning algorithm, and controlling the desktop wheeled robot to execute. The method has the technical effect that the planning strategy can be dynamically adjusted according to the task context.

The present application belongs to the technical field of decision-making behavior modeling and intelligent simulation, and relates to a multi-agent cooperative confrontation deduction method and system based on a hierarchical behavior tree control framework. The method comprises: constructing an editable behavior tree, wherein the parameters of the action nodes can be edited; constructing a multi-level intelligent decision-making model based on the editable behavior tree; and using the multi-level intelligent decision-making model to perform cooperative confrontation deduction of multi-agents. The present application can solve the difference between human and machine thinking, significantly improve the interpretability and artificial intervention ability of strategies, realize parameter configuration combining dynamic and static, greatly enhance the adaptability to dynamic unstructured environment, break through the bottleneck of large-scale agent cooperation, and realize multi-scale efficient management and control of "high layer-middle layer-low layer".

The embodiment of the invention discloses a strategy updating method and system for an intelligent agent. The method comprises the following steps: extracting environment state characteristics and obtaining basic behaviors of the intelligent agent; constructing a behavior tree and a strategy model based on the environment state characteristics and the basic behaviors of the intelligent agent; training the strategy model based on interaction data generated by interaction between the intelligent agent and the environment to obtain an optimization strategy; and updating the behavior tree weight based on the optimization strategy so as to map the optimization strategy to the priority of the behavior node. According to the embodiment of the invention, the behavior tree and the strategy model are constructed according to the environment state characteristics and the basic behaviors of the intelligent agent, after the strategy model is trained and updated to obtain the optimization strategy, the weight of the behavior tree is updated by the optimization strategy, and the optimization strategy is mapped to the priority of the behavior node in the behavior tree. The behavior triggered by the behavior tree of the intelligent agent can be continuously optimized through feedback generated by actual interaction of the intelligent agent and the environment, and the adaptability of the intelligent agent to the complex environment is improved.

The application provides a scheduling processing method based on a behavior tree. The method is applied to a robot navigation system, and the method comprises the following steps: in the process of the asynchronous task, a behavior tree engine responds to receiving a first scheduling instruction, and instructs an asynchronous action node matched with the first scheduling instruction in the behavior tree to perform a first scheduling operation corresponding to the first scheduling instruction; the execution state of the asynchronous action node is monitored; and when the execution state of all asynchronous action nodes performing the first scheduling operation is completed, the behavior tree engine responds to receiving a second scheduling instruction, and instructs an asynchronous action node matched with the second scheduling instruction in the behavior tree to perform a second scheduling operation corresponding to the second scheduling instruction. The application can ensure that the asynchronous action node has completed the current operation before performing a new operation, and the method can reduce the instability risk caused by task conflicts.

The invention provides a robot control method, terminal equipment and a medium. The method comprises the steps that a task set is acquired; generating a candidate action model set according to the task set, verifying the completeness of the candidate action model set, and generating a planning context; sampling a control strategy corresponding to the candidate action model from a control strategy space by using a second pre-training model based on the visual observation data and the planning context of the environment; executing the control strategy, generating an execution context, and verifying the consistency of the control strategy; when the verification result is inconsistent, correcting the candidate action model by using a second pre-training model in combination with the planning context and the execution context until a behavior tree system meeting the requirements of completeness and consistency is generated; and analyzing the behavior tree system, converting the behavior tree nodes into a driving instruction of the robot, and driving the robot to execute operation according to the driving instruction so as to complete the task set. According to the invention, the accuracy of behavior tree system construction can be improved.

A behavior tree and situation diagram linkage experimental hierarchical review processing method, the method comprises the following steps: in the review interaction interface, the behavior tree corresponding to the confrontation simulation experiment is displayed, and the review situation diagram corresponding to the confrontation simulation experiment is displayed; wherein the behavior tree is used to construct the confrontation simulation experiment in the simulation environment; the behavior tree includes multiple types of nodes based on its tree structure; when detecting that a user triggers an operation on any type of target node of the behavior tree, the basic attribute information corresponding to the target node is graphically displayed in the review situation diagram; the basic attribute information of different types of nodes is used to display different levels of review information of the confrontation simulation experiment. The present application is used for quickly reviewing the experiment, effectively solves the problems of low efficiency and weak hierarchical review of experiment review, and improves the efficiency of experiment personnel mastering the experiment content.

The application discloses a kind of based on behavior tree and VLA's dual-arm robot task assembly method, specifically includes: when task starts, behavior tree is executed from root node, signal is sent to child node by fixed frequency;Proposition received by parsing perception module is extracted target object and its spatial relationship by conditional node, and three-dimensional information and attribute are obtained using visual recognition algorithm, to judge whether meet the requirement;Action node generates initial path according to motion planning module, trajectory optimization is carried out according to the dynamics constraint of manipulator, and deviation and disturbance in motion process are monitored in real time, timely adjustment is ensured to complete task, wherein user instruction and scene image information are integrated, joint angle and end effector instruction are generated by means of pre-trained multi-modal VLA model, guide dual-arm efficient cooperation, until successfully complete assembly operation.

This invention relates to the field of quadruped robot navigation technology and discloses an intelligent navigation system for quadruped robots that combines behavior tree and visual recognition. The system includes modules for path processing, turn prediction, behavior tree decision-making, obstruction judgment, visual recognition and language evaluation, and navigation decision-making. The path processing module integrates multi-sensor data to acquire pose and gait state; the turn prediction module calculates safe turning speed and trigger distance based on a gait phase sliding window; the behavior tree decision-making module dynamically adjusts node priorities according to trigger intensity, and the switching timing is controlled by a gait phase arbiter; the obstruction judgment module generates path obstruction flags; the visual recognition module adopts a two-level architecture, calling a visual language model to evaluate the landing area for static traversable obstacles; and the navigation decision-making module integrates landing semantic confidence and outputs commands to cross, detour, wait, or return. This invention enables quadruped robots to achieve proactive predictive navigation and intelligent obstacle avoidance decision-making in complex industrial environments.

Apparatus and method for controlling robot to improve reusability and compatibility of action tree SOLUTION: The at least one processor is configured to acquire a standard behavior tree satisfying a target standard from a behavior tree based on a behavior tree abstraction model applied to interpretation of the behavior tree, store a robot scenario paired with the standard behavior tree and the standard behavior tree in the memory, and verify validity of the standard behavior tree based on nodes included in the standard behavior tree and edges between the nodes included in the standard behavior tree.SELECTED DRAWING: Figure 1

The application discloses a method and device for controlling a virtual role in a game and electronic equipment, wherein the method comprises: in response to monitoring occurrence of a game event, obtaining a current role level of a first virtual role, the first virtual role being a virtual role controlled by an intelligent agent in a game session, the role level changing along with a game process; according to the current role level, calling a first behavior tree, the first behavior tree being a behavior tree corresponding to the current role level in a plurality of behavior trees preset for the intelligent agent, the behavior tree comprising a plurality of behaviors and trigger conditions corresponding to the behaviors; in response to determining that the game event meets a trigger condition of a first behavior, controlling the first virtual role to perform the first behavior, the first behavior being a behavior in the plurality of behaviors included in the first behavior tree and matching the game event. Therefore, the method for controlling a virtual role in a game provided by the embodiment of the application can improve the depth of interactive experience.

A method is provided , which includes: receiving a material bill including names of parts involved in a current assembly; obtaining a mechanical drawing of each part in the material bill according to the names of parts in the material bill; establishing multiple candidate pairings based on obtained mechanical drawings, where each of the multiple candidate pairings is composed of mechanical drawings of two parts; matching each of the multiple candidate pairings with each of pre-established regular pairings , and regarding candidate pairings each of which matches a regular paring as target candidate pairings; for each target candidate pairing, adding assembly process information of a regular pairing matching the target candidate pairing to the target candidate pairing; based on the assembly process information of all target candidate pairings, constructing a requirement behavior tree corresponding to a whole assembly process of the current assembly according to behavior tree syntax.

The invention relates to an agent action sequence generation method based on a hierarchical decision-making framework, which comprises the following steps of: establishing a behavior sequence generation model comprising a decision-making tree layer, a state machine layer and a behavior tree layer, and integrating current environment situation information as input data of the behavior sequence generation model; in the decision-making tree layer, a judgment condition is set, the decision-making tree carries out comparison and judgment according to current environment situation information input in real time and the judgment condition, overall situation judgment is completed, and different strategy adjustment instructions are generated and input into corresponding state machines; in the state machine layer, a state machine receives a strategy adjustment instruction output by the decision tree and performs state conversion according to a preset state conversion rule, and an output result is used for selecting behavior tree models in different states; and according to the converted state of the state machine, activating the corresponding behavior tree model in the behavior tree layer, executing the activated behavior tree model according to the structure and the rule of the activated behavior tree model, and generating a specific agent action sequence.

The invention provides a behavior tree dynamic construction method, system and device and a medium, and the method comprises the steps: obtaining original data from a voice sensor, an image sensor and a touch sensor, carrying out the normalization and event abstract semantic processing of the original data, and obtaining a semantic event object; analyzing a corresponding relation type according to the event object, and constructing an event semantic relation graph which is constructed by adopting a predefined rule and large model mixed mode; defining a mapping rule from an event semantic relation graph to behavior tree nodes, and converting a relation type in the semantic relation graph into a behavior tree node type, including setting sequence nodes corresponding to a causal relation and setting condition nodes corresponding to a condition relation; analyzing the event semantic relation graph into a behavior tree structure according to a mapping rule, and constructing a behavior tree; and recording an execution result in the operation process of the behavior tree, and adjusting the event confidence coefficient according to the execution result to optimize the dynamic construction of the behavior tree in the next round. According to the method, the expandable behavior tree meeting the personalized expression of the accompanying robot can be created, so that the accompanying experience of the robot is improved.

The invention discloses a multi-agent cluster behavior tree generation method based on deep reinforcement learning, and the method comprises the following steps: constructing a double-layer deep reinforcement learning network in which a scheduling layer and an execution layer are combined, and the double-layer deep reinforcement learning network comprises a scheduling layer strategy network which is used for learning a global task distribution strategy and a multi-agent collaborative decision; the execution layer strategy network is used for learning a local control strategy of a specific behavior of the single agent; performing collaborative optimization training on the scheduling layer strategy network and the execution layer strategy network to generate a stable convergence agent strategy model; making a decision based on the agent strategy model to obtain a high-confidence state-action pair set; and constructing a scheduling layer behavior tree and an execution layer behavior tree based on the high-confidence state-action pair set. The method is applied to the field of cluster control, the multi-agent collaborative behavior tree which is compact in structure and easy to deploy can be rapidly generated under dynamic changes of tasks and environments, and the method has good execution efficiency and engineering availability.

A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.

The invention discloses an LLM-based system-level demand modeling and analysis method and platform, and belongs to the field of demand engineering and system modeling. The invention provides an LLM (Logical Language Model)-driven automatic demand behavior model construction and conflict detection method aiming at the problem that accurate modeling and consistency verification of natural language system-level software demands are difficult. According to the method, control flow logic in a demand text is recursively analyzed by simulating an expert stratified analysis process, and a behavior model is automatically generated. The modeling process comprises the steps of recognizing atomic activities, extracting a hierarchical relation and constructing a behavior model. On the basis, a system-level behavior tree is constructed based on preposition and postposition conditions of behavior nodes, a plurality of demand models are comprehensively analyzed, and behavior consistency analysis and conflict detection on the system level are achieved. According to the method, automatic expansion from the demand-level behavior model to the system-level behavior model can be realized, and an effective technical scheme is provided for automatic modeling and analysis of system-level demands.

A method and a system for controlling behaviors of a plurality of robots in a cloud environment are disclosed. In one embodiment, a robot control method for a robot control server implemented by at least one computer device included in a cloud environment may comprise the steps of: acquiring a command generated by a behavior tree engine from the behavior tree engine for generating a command for each robot according to a behavior tree algorithm by using a behavior tree generated for a service and a blackboard that stores information about the state of at least one robot; transmitting the acquired command to a corresponding robot; receiving information about a performance result of the robot for the transmitted command and information about the state of the robot; transmitting the information about the performance result to a service system associated with the service; and storing the information about the state of the robot on the blackboard in association with the robot.

The application discloses an intelligent cockpit task control method based on a behavior tree arrangement and multi-source arbitration, and the method comprises the following steps: a task compiler performs behavior tree conversion according to a task configuration file, and completes behavior tree building; a shadow verification module performs simulation verification on the behavior tree, and the behavior tree that passes the verification enters a gray-to-normal process; a trigger arbitrator acquires N trigger signals in a vehicle, when K trigger signals in the N trigger signals all satisfy a trigger condition, the trigger arbitrator generates a trigger proof with a time stamp and an event abstract; a behavior tree engine locks the context of this execution according to the trigger proof, activates action nodes corresponding to the trigger signals, and then injects trigger parameters at the trigger time in each action node to generate CAN signal instructions; and an execution scheduler controls corresponding vehicle-mounted physical hardware to run according to the CAN signal instructions. Effects: the method can solve trigger jitter through multi-source arbitration, solve execution safety through behavior tree injection, and solve online risk through a shadow mode.

The invention discloses a behavior tree generation method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that an initial cue word corresponding to a virtual object and a target game environment where the virtual object is located are obtained; inputting the initial cue word into a pre-trained decision model to generate a first behavior tree corresponding to the virtual object; deploying the first behavior tree to a target game environment to obtain game match data; inputting the game playing data into a pre-trained cognitive model, and generating data description information corresponding to the game playing data; and optimizing the first behavior tree based on the data description information to obtain a target behavior tree of the virtual object in the target game environment. By implementing the technical scheme of the invention, automatic generation and optimization of the game virtual object behavior tree can be realized based on collaborative closed-loop iteration of the decision model and the cognitive model, and the intelligent level and the generation efficiency of the game virtual object are improved.

Various embodiments of the teachings herein include methods for constructing a requirement behavior tree. An example includes: receiving a material bill including names of parts involved in a current assembly; obtaining a mechanical drawing of each part in the material bill; establishing multiple candidate pairings based on obtained mechanical drawings, wherein each of the multiple candidate pairings is composed of mechanical drawings of two parts; matching each of the multiple candidate pairings with each of pre-established regular pairings and regarding candidate pairings each of which matches a regular paring as target candidate pairings; for each target candidate pairing, adding assembly process information of a regular pairing matching the target candidate pairing to the target candidate pairing; and based on the assembly process information of all target candidate pairings, constructing a requirement behavior tree corresponding to a whole assembly process of the current assembly according to behavior tree syntax.