136 results about "Behavior Trees" patented technology

The invention provides a hydraulic generator inner bore inspection robot based on a dynamic behavior tree and an intelligent decision and operation method thereof, and the robot comprises an upper mechanical arm end executor, a six-axis mechanical arm, an intermediate control system, a mobile platform, a multi-physical quantity fusion detection system, and a central control platform. The method is specially designed for the complex dynamic shielding environment of the inner bore of the hydro-generator. According to the method, by constructing a three-level behavior tree architecture composed of a task decision-making layer, a scene adaptation layer and an execution control layer, inspection requirements are decomposed into environment perception, task execution and resource constraint events, and the events are dynamically mapped to preset behavior nodes. Weight factors are adjusted in real time based on a multi-dimensional evaluation system, and functions of autonomous obstacle avoidance, narrow space operation and anomaly detection are realized in combination with hardware characteristics of the robot. The problems that a traditional inspection method is poor in adaptability and low in efficiency in a dynamic shielding environment are solved, and the intelligent level and safety of hydraulic generator inner bore inspection are remarkably improved.

The embodiment of the invention provides an industrial robot autonomous assembly task planning method based on a multi-agent large model, and belongs to the technical field of automatic assembly. The method comprises the steps of obtaining assembly task data of an assembly object, wherein the assembly task data comprises a task instruction for assembling the assembly object and an assembly manual; analyzing the task instruction and the assembly manual to obtain task execution logic; performing real-time acquisition on an assembly environment when the assembly object is assembled to obtain a target image; multi-modal sensing is conducted on the target image, a sensing result is obtained, and the sensing result comprises the position of at least one assembly of the assembly object in the assembly environment; and converting the task execution logic and the perception result into a planning domain definition language format, and performing assembly task planning through the behavior tree to obtain a target assembly task. According to the embodiment of the invention, the assembly efficiency and precision of the industrial robot are improved.

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 provides a multi-agent construction method and device and related equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: according to a first target instruction input by a first user, obtaining a plurality of sub-tasks corresponding to the first target instruction and a logic relation between the sub-tasks; obtaining at least one target agent from an agent capability library based on the first target instruction, wherein the target agent is an agent matched with the subtask in the agent capability library; determining a behavior tree structure according to the plurality of subtasks and the logical relationship, wherein the behavior tree structure is used for mapping a connection relationship between the at least one target agent; and adjusting a connection relationship between the at least one target agent based on the behavior tree structure to obtain a target multi-agent. The ability to understand complex business contexts and user intentions is enhanced, and accurate construction of a multi-agent system is realized.

A method for generating a behaviour tree for controlling a robot device. The method includes: combining a plurality of predetermined behaviour trees and background knowledge into a behaviour tree knowledge graph, representing the behaviour tree knowledge graph in a latent space; extracting, from a prompt describing a desired behaviour of the robot device, a prompt representation graph; supplementing the prompt representation graph according to relations specified by the behaviour tree knowledge graph; selecting a sub graphs of the behaviour tree knowledge graph depending on a similarity to the supplemented prompt representation graph; and generating the behaviour tree for controlling the robot device by adjusting the selected sub graph according to knowledge from the prompt and the behaviour tree knowledge graph.

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 provides a robot control method in combination with a behavior tree framework. The method comprises the steps that S1, an instruction is received; s2, analyzing an instruction through a pre-trained large language model used for executing a behavior tree framework generation task, and then selecting a preset sub-task to generate a corresponding behavior tree framework; s3, the large language model sends key information obtained through analysis from the instruction to the corresponding subtasks, and the key information is used for guiding specific execution of the subtasks; s4, controlling the robot to execute subtasks in sequence according to the behavior tree framework and the key information; the framework of the behavior tree realizes the convenience of man-machine collaboration, and tasks can be adjusted and executed more naturally according to the requirements of human beings; the life support task is intelligently scheduled and executed through the behavior tree framework, manual intervention is reduced, the automation of the task is improved, and the execution efficiency and the task completion precision of the robot are improved.

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 relates to an automatic driving collaborative decision-making method and device based on big language model scene analysis and a medium, and the method comprises the steps: S1, carrying out the pruning operation of a future behavior tree of a current participant, which is obtained through a neural network model based on an OPM process, through employing a big language model according to the current scene information and the intention of the participant, generating a future behavior tree of the vehicle in the current scene, and giving an efficiency constraint and a safety constraint required by the next decision; s2, according to the efficiency constraint and the safety constraint, screening a future trajectory generated according to the current state of the own vehicle, sending an optimal control instruction corresponding to an optimal own vehicle trajectory obtained after screening to the vehicle-mounted control terminal, executing the optimal control instruction, and updating the state space of the OPM process after executing the optimal control instruction; and S3, circularly executing the steps S1 to S2 until the current conflict scene is passed. Compared with the prior art, the method introduces the intention of the participant as an important factor of the automatic driving decision, and improves the passing efficiency, safety and reliability of automatic driving in a complex scene.

The invention relates to a CGF-oriented typical normal form behavior model conversion method and device. The method comprises the following steps: receiving a conversion instruction input by a user; when a first conversion instruction is received, adopting a preset first conversion algorithm to respectively map each task flow of the finite-state machine and a state and transfer relationship in each task flow into a sub-tree of a behavior tree and a hierarchical node structure of each sub-tree, and outputting a behavior tree description file; when a second conversion instruction is received, disassembling the tree structure of the behavior tree into a state transition network of a finite-state machine by adopting a preset second conversion algorithm, and outputting a finite-state machine description file; and according to the description file output after receiving the conversion instruction, generating a corresponding code framework and integrating the code framework to a CGF simulation platform to output a deployable CGF entity model file. By adopting the method, the efficiency, reusability and application flexibility of CGF modeling can be improved.

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 provides a method for intelligent legal assistance and a multi-agent system, the method including: constructing a dynamically updated legal knowledge system; constructing a case diversion agent, a legal application agent, a similar case retrieval agent and a risk warning agent based on the dynamically updated legal knowledge system, and realizing a multi-agent collaborative mechanism through a collaborative structure based on message passing and state sharing; constructing a preference mapping relationship model through case complexity assessment and quality assessment based on the multi-agent collaborative mechanism, and realizing multi-case relationship perception preference mapping; based on multi-case relationship perception preference mapping, combining a large language model with Prolog-based knowledge management technology to construct a planning tree PLANTOR framework, generate a robot-oriented knowledge base and planning program, and convert them into a behavior tree; according to the PLANTOR framework, realize multi-agent reasoning chain alignment through the joint optimization of transmission optimization and embedding optimization, and complete legal decision-making assistance. The above scheme effectively improves the accuracy and efficiency of legal processing.

The present invention discloses a behavior tree-based unmanned vehicle action modeling and control method. The method constructs an unmanned vehicle action model according to a visual behavior tree. When the unmanned vehicle action model is used for simulation, when a target enters the sensor detection range, a target detection node transmits brief intelligence. A target identification node identifies the target model and payload to obtain intelligence information. A target threat analysis node analyzes the threat level of the target based on the identification results of the target identification node and provides treatment recommendations. A target tracking node is used to implement mobile tracking or non-mobile tracking based on environmental conditions, concealment capabilities, and target detection capabilities. When mobile tracking is implemented, the target tracking node controls the speed and direction of the unmanned vehicle to achieve target tracking. A cluster collaborative control node is used to achieve collaborative control between the individual unmanned vehicle entities in the unmanned vehicle cluster. The present invention implements the application of behavior trees in an unmanned vehicle simulation system.

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".

This application provides a robot control method, apparatus, device, robot, medium, and product, belonging to the field of robotics technology. The method includes: generating a behavior tree based on task intent information corresponding to a user instruction; the task intent information represents the user's intention to instruct the robot to perform an operation task, the tree nodes of the behavior tree represent the subtasks of the operation task to be performed by the robot, and the node information of the tree nodes represents the execution conditions of the subtasks corresponding to the tree nodes; controlling the robot to perform the operation task based on the behavior tree; receiving task execution status information fed back by the robot during the execution of the operation task; and dynamically adjusting the behavior tree to obtain an updated behavior tree when the task execution status information represents an execution anomaly in the operation task, and controlling the robot to continue performing the operation task based on the updated behavior tree. This application enables the robot to operate efficiently in dynamically changing and complex scenarios.

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.