146 results about "Multirobot systems" patented technology

The invention provides an industrial multi-robot intelligent collaborative planning method based on deep learning. The industrial multi-robot intelligent collaborative planning method comprises six parts including environment modeling, feature extraction, task allocation, trajectory planning, control instruction generation and rule distillation. The method comprises the following steps: acquiring multi-robot environment information by constructing a probability grid map and a topological structure, and extracting state and task features to form a comprehensive feature matrix; training an optimal task allocation strategy by adopting deep reinforcement learning, and combining CVAE and CEM joint modeling to optimize trajectory generation; an adaptive impedance controller based on MADDPG is further designed, and dynamic adjustment of interaction parameters is achieved; and finally, the control strategy is converted into a decision rule set through knowledge distillation, the control interpretability is improved, and the deployment complexity is reduced. According to the invention, the task cooperation efficiency and the control stability of the multi-robot system in a complex industrial environment can be effectively improved.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention relates to a multi-robot system global flexible preset performance control method under an unknown state and a DoS attack, and belongs to the technical field of multi-robot system control. Aiming at the problems in the prior art that the communication topology is unstable due to attack, the initial error constraint is strict, and the input saturation conflicts with the performance, the following scheme is provided: dynamically reconstructing the attacked communication topology based on a bipartite consensus layering algorithm; designing a global error constraint mechanism fusing the scale function and the tunnel type performance function; constructing a balance control input and performance boundary of the input saturation self-adaptive auxiliary system; and estimating an unknown state and compensating an observation error by adopting a state observer. The method has the technical effects that the DoS attack resistance of the system is enhanced, the global preset performance control of any initial error is realized, the contradiction between input saturation and tracking performance is dynamically coordinated, and the multi-robot collaborative operation under the unbalanced topology is supported.

The invention relates to the technical field of information, and discloses a multi-machine collaborative operation control method and system based on a common inductance calculation control technology, and the method comprises the steps: obtaining task granularity distribution and a resource demand matrix, and carrying out the classification of the task granularity distribution and the resource demand matrix through a classification algorithm, and obtaining an initial task segmentation feature set; based on the optimal segmentation point set, a preliminary task allocation scheme is generated in combination with node calculation heterogeneity, and a task segmentation feature set is dynamically updated through a real-time sensing technology; a real-time task state set is generated through a group information sharing mechanism, a task allocation scheme is optimized through a dynamic programming algorithm, and it is ensured that tasks are reasonably allocated according to priorities and node resources; and the cooperation state matrix is updated through a real-time communication protocol, so that the accuracy and timeliness of a multi-node cooperation execution result are ensured. According to the invention, task allocation can be dynamically adjusted in a complex environment, resource utilization is optimized, the cooperation efficiency of a multi-robot system is improved, and the method can be widely applied to the fields of industrial production, warehouse logistics, disaster rescue and the like.

The invention discloses a multi-robot probability trajectory generation method based on distributed model predictive control, and relates to the technical field of multi-robot systems, and the method constructs collision avoidance constraints based on a safety corridor with time perception. Uncertainty introduced by state estimation noise and motion interference is fully considered in construction of collision avoidance constraint, probability collision avoidance constraint is converted into deterministic constraint of mean value and covariance of robot states, and robustness of collision avoidance is ensured; meanwhile, in order to solve the problem of deadlock possibly occurring in a multi-robot system, a warning tape mechanism is introduced into probability constraint for avoiding collision and is combined with a right hand rule, and robot trajectory planning is dynamically adjusted; finally, the probabilistic collision constraint avoiding and deadlock preventing mechanisms are integrated into a distributed model predictive control framework, so that a locally optimal collision-free trajectory is generated.

The invention provides a multi-robot collaborative exploration method based on intention reasoning and related equipment, and the method comprises the steps: obtaining the sensing data of each robot, constructing a local observation map of each robot according to the sensing data of each robot, and estimating the pose information of each robot, obtaining a global observation map of each robot, and fusing the plurality of global observation maps to obtain a global fusion map; each robot extracts a spatial feature map from the global observation map, pre-estimates own detection intention information according to exploration information shared by other robots, and obtains a global exploration target according to fusion of the exploration intention information and the spatial feature map; and each robot generates a target exploration path for the corresponding global exploration target based on the global fusion map, and explores according to the target exploration path, so that information sharing among multiple robots is realized, and the exploration capability of a multi-robot system in a complex environment is improved.

The invention discloses a multi-machine system number-real consistency error collaborative prediction method and system based on multi-agent reinforcement learning, and belongs to the technical field of multi-robot systems and artificial intelligence. According to the method, each robot is modeled as an intelligent agent, a centralized training distributed execution framework is adopted, and collaborative prediction of errors is achieved. Each agent collects own, neighbor and environment state information in real time, multi-source information integration is carried out through feature embedding and gating fusion, the influence of different information sources on errors is dynamically evaluated by adopting a layered attention mechanism, and propagation and coupling rules of the errors in a cluster are effectively modeled. In the training stage, the centralized commentator network is utilized to evaluate the global prediction performance, the cooperative optimization of each executor network is guided, and the prediction precision is improved. According to the method, active and collaborative prediction of the multi-machine system number-real consistency error is realized, and a key technical support is provided for error feed-forward compensation and system reliability guarantee.

The invention discloses a multi-robot collaborative job scheduling optimization method based on a graph neural network. The method comprises the following steps: S1, constructing a collaborative scheduling graph comprising robot nodes, task nodes and meeting point nodes; s2, initializing a feature vector of each node; s3, node feature aggregation and scheduling representation vector generation are executed through the graph neural network; s4, an improved Co-DPSIPP algorithm is adopted to calculate a handover score of a meeting point node, and optimization is carried out based on task completion time, energy consumption change and communication load; s5, selecting an optimal handover mode according to the handover score, and updating the scheduling graph; s6, executing task redistribution and path planning based on the updated scheduling graph; and S7, repeatedly executing reasoning and scheduling control until all tasks are completed, and outputting a scheduling scheme and a path sequence. The scheduling efficiency and path planning of the multi-robot system are remarkably improved, and the method is widely applied to automatic production and robot cooperation tasks.

The invention discloses a multi-robot fixed time cooperative hunting method and system based on a distributed time-varying optimization algorithm. The method comprises the following steps: establishing a dynamic model and a communication topological graph of a multi-robot system; the method comprises the following steps: converting a fixed time distributed hunting problem of a multi-robot system into a fixed time distributed time-varying optimization problem, and constructing a global objective function; designing a fixed time distributed gradient estimator, and enabling each robot to estimate gradient information of a system global objective function within fixed time in a distributed mode; designing a self-adaptive zero-order neural network which is used for approximating an inverse matrix of a Hessian matrix; and designing a fixed-time distributed time-varying optimization algorithm for the multi-robot system by adopting the gradient information of the global objective function estimated by the distributed gradient estimator in the step 3 and the inverse matrix of the Hessian matrix approximated by the adaptive zero-order neural network in the step 4, so that each robot encircles the dynamic target in the fixed time in a distributed mode. According to the invention, the multi-robot system is ensured to surround the moving target in a formation form within a fixed time.

The invention discloses a multi-robot collaborative surrounding and sinking method and system based on reinforcement learning, and relates to the technical field of robot collaborative control. The method comprises the steps that a multi-robot surrounding and sinking task model is established according to a target surrounding and sinking task, a chasing robot carries out surrounding and sinking on a target robot, and the limited degree of the target robot is quantified by utilizing a reachable safe area; based on a clustering algorithm, self-adaptive dynamic grouping is carried out on the pursuing robots in the enclosing and sinking task model; and based on the grouping condition of the pursuing robots, mining the environment observation information by using the multi-robot surrounding and sinking decision network, and optimizing the action decision of the pursuing robots according to the environment observation information. According to the method, technical means such as a self-adaptive grouping mechanism and strategy network optimization design are combined, efficient, stable and intelligent surrounding and sinking decision construction is achieved, and the task execution capacity of a multi-robot system in a complex environment can be remarkably improved.

The invention discloses a distributed task scheduling system and method for multi-robot collaborative drawing, and the system employs a distributed architecture of a master control server and a plurality of robot clients, and achieves the decoupling of drawing task generation and execution through a task queue. The master control server receives the task request, asynchronously generates a robot control instruction from the image data through a built-in task calculation module, and maintains a robot state management module; and the robot client registers the unique identifier and keeps communication with the server. During scheduling, the server actively queries the state management module, selects an available robot to issue an instruction and atomically updates the state of the available robot to be unavailable; and after the task is completed, the client feeds back a receipt, and the server updates the state to be available. According to the method, the problems of high concurrency, repetition prevention and loss prevention of task scheduling in a multi-robot system are solved, and efficient and reliable parallel execution of non-standardized artistic creation tasks and elastic expansion of the system are realized.

The invention relates to the technical field of multi-robot tasks, and discloses a hybrid multi-robot cooperation method and system driven by a large language model. The method comprises the steps that a natural language task instruction input by a user is received, semantic analysis is conducted through a task decomposition large language model, and a task set containing a plurality of structured sub-tasks is generated; a plurality of robots adopt a decentralized negotiation mechanism to respond and compete for each subtask, and a task allocation result when a single robot is used as an execution main body is determined; when the skill demand of the subtask exceeds the skill space of any single robot, triggering a collaborative task team construction process based on a decentralized language negotiation mechanism; and each execution main body executes the allocated sub-task according to the negotiation result. According to the method, the understanding ability and execution flexibility of the multi-robot system for the natural language task can be remarkably enhanced, and the cooperation efficiency and task completion quality of the multi-robot system in a complex task scene are improved.

The invention discloses a distributed dynamic task allocation method and device, equipment and a storage medium, and relates to the technical field of multi-robot control, the method is applied to a multi-robot system, and the method comprises the following steps: obtaining communication state information of each mobile robot, and determining minimum communication topological graph information by adopting a preset distributed minimum spanning tree algorithm; inputting the system parameters and the minimum communication topological graph information into a preset k-WTA network calculation formula to obtain activation signals corresponding to the mobile robots; and obtaining position information of the to-be-tracked target equipment and the mobile robots, and performing task allocation on the mobile robots according to the position information and the activation signals. According to the method, the complex communication topology in the multi-robot system is simplified through the minimum spanning tree algorithm, and calculation is performed based on the minimum communication topological graph information, so that the consumption of calculation resources is reduced, and the task allocation efficiency of each mobile robot is improved.

The invention discloses a multi-robot cooperative positioning and elastic formation control method based on a distributed Kalman consistency filtering (DKCF) algorithm. Aiming at the problems of insufficient sensor noise suppression, poor robustness under network attack and the like in the prior art, a dynamic model containing process noise and a sensor model of measurement noise are constructed, and a consistency state estimation method of weighted fusion observation data is provided. According to the method, multi-modal redundant sensor information is fused, and an improved DKCF is combined, so that cooperative state estimation between robots is realized, and the positioning precision in a noise environment is effectively improved; an elastic positioning framework under denial of service attack is established, and the robustness to network attack is enhanced while the formation precision is ensured through the joint design of distributed cooperative position estimation and a formation controller. According to the method, high positioning precision and a stable formation form can still be kept in the face of network attacks of denial of service, and the safety cooperation performance of a multi-robot system in a complex scene is remarkably improved.

The invention relates to the technical field of industrial automation, in particular to a multi-robot control method and system based on the industrial Internet of Things and a medium. The method comprises the following steps: acquiring industrial warehouse channel data; generating a warehouse digital twin model based on the industrial warehouse channel data; performing multi-robot path conflict detection according to the warehouse digital twin model to obtain path conflict data; determining a robot number according to the path conflict data; performing motor winding damage detection based on the robot number to obtain motor winding damage data; performing iron core loosening analysis based on the motor winding damage data to obtain iron core loosening data; performing vibration suppression control optimization according to the iron core loosening data to obtain vibration suppression control data; and robot scheduling is carried out according to the path conflict data to obtain robot scheduling data. According to the invention, the path planning precision and fault response efficiency of the multi-robot system are improved based on the industrial automation technology, and the operation efficiency of industrial warehouse operation is improved.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

The invention discloses a multi-robot safety control method and system based on a large language model, and the method comprises the steps: receiving first information through a first large language model, and generating a reference planner meeting the task requirements; if the reference planner output by the first large language model has compiling errors, the multi-robot distributed safety control system automatically captures error information and transmits the error information to the second large language model, the second large language model checks and modifies codes, it is ensured that the codes can be correctly executed, and the modified correct codes are input into the reference planner; and according to the control signal obtained by the reference planner, all the robots of the multi-robot system are controlled to work cooperatively through the control signal to complete a specified task and safely reach a specified target point. According to the invention, the stability and safety of all robots in the robot system can be ensured in a complex environment.

The invention relates to the technical field of robot fault recognition, and provides a multi-robot consistency Byzantine fault detection method. The method comprises the steps that observation motion information of each target robot in the multi-robot system at the current moment is acquired; encrypting each piece of observation motion information to obtain encrypted information of each piece of observation motion information, and uploading all the encrypted information to the block chain; decrypting and integrating all encrypted information at the current moment on the block chain to obtain a state data frame at the current moment; and when the state data frames are conflict data frames, determining the number of supporters and the number of counters of each target robot according to the conflict data frames, and determining a Byzantine fault robot from all the target robots based on the number of supporters and the number of counters of all the target robots. According to the method, the Byzantine fault identification reliability can be improved.

The invention discloses a large-model-driven intelligent multi-robot area search method and system, and the method comprises the steps: constructing a coarse and fine dual-resolution area search grid map, and constructing a layered coupling decision information model based on the coarse and fine dual-resolution area search grid map; a decision information model with rich levels is constructed for the multi-robot system from the global macroscopic and microscopic angles, and the multi-robot system can be helped to make efficient and intelligent decisions; a high-level intelligent decision-making module is constructed to perform local optimal state judgment and guide the robot to quickly approach an unsearched area by utilizing the strong thrust capability of the large model, so that the overall search efficiency of the multi-robot system is improved; the designed low-level motion decision-making module makes full use of guidance information provided by a large model in the high-level intelligent decision-making module, and combines the advantages that a biological inspiration neural network model does not depend on environmental prior information and does not need training, so that the multi-robot system is efficiently driven to quickly complete a given area search task.

The invention relates to the technical field of data processing, and discloses an AMR transmission path data comprehensive processing method and system. The method comprises the following steps: processing environment sensing data through a data fusion algorithm to obtain an environment state matrix for recording information of an obstacle, a dynamic target and a passable area; robot state information and the environment state matrix are combined to obtain a joint state space integrating global and local information; training the joint state space through a multi-agent reinforcement learning algorithm to obtain a collaborative decision model composed of an independent strategy network and a shared evaluation network; processing the control instruction through a trajectory prediction model to obtain a collaborative path set storing robot path information; and analyzing the collaborative path set through a conflict detection algorithm, and triggering a scheduling strategy to obtain a path optimization result when a conflict is detected. According to the invention, the cooperative efficiency of the multi-robot system and the intelligent level of path planning are obviously improved.

In order to solve the problem, the invention provides an intention-aware multi-robot scheduling optimization method based on a graph neural network, and the method comprises the steps: constructing an explicit intention vector of a multi-tuple structure, fusing a robot state, task information and a resource use condition, constructing an intention graph representing a task conflict and a cooperative relationship, and introducing a spectrum sparsification mechanism; then, a graph attention network and a strategy updating module are adopted to jointly train a strategy model, strategy optimization of each robot node on a graph structure is achieved, and therefore the overall task income is maximized, path conflicts and resource competition are minimized, and a universal path planning algorithm is used for dynamically constructing a conflict avoidance path; compared with the prior art, the method has higher real-time performance, expandability and environment adaptability, and the scheduling efficiency and the task completion rate of the multi-robot system in dynamic scenes such as disaster response and the like are remarkably improved.

The invention relates to the technical field of multi-robot collaboration, and provides a multi-robot collaboration method and system and a multi-robot system. The multi-robot cooperation method based on service state triggering comprises the following steps: continuously sensing an environment state and user state information through a multi-modal sensor to construct a service state vector for quantitatively describing an interaction condition among a user, an environment and a robot; responding to the condition that the deviation degree of the service state vector and the expected service state meets a cooperative triggering condition, generating a cooperative demand for indicating a target service state, and broadcasting the cooperative demand to other robots; a cooperative structure which negotiates with other robots based on the cooperative demand to determine the cooperative demand, wherein the cooperative structure comprises cooperative robots participating in execution and role allocation of the cooperative robots; and continuously monitoring a service state vector when the cooperative robot executes the corresponding behavior based on the role allocation.

The invention discloses a multi-robot collaborative operation and task allocation system and method, relates to the technical field of robot operation, and is used for solving the problems that in a traditional method, task allocation efficiency is low, resources are wasted, especially in a multi-robot system, tasks cannot be accurately allocated, dynamic adaptability is lacked, task and environment changes cannot be coped, and the task allocation efficiency is low. An optimal decision strategy is obtained step by step; and a single learning algorithm has defects. An auction algorithm is used for solving the task allocation problem of the robot, the work progress and the space matching degree of the robot are considered, a Q learning algorithm is used for determining whether to switch to a Sarsa learning algorithm or not according to the task completion condition of the robot in combination with environment interaction and a continuous optimization strategy, and the task allocation efficiency is improved. The design of the flexible switching strategy makes up for the defects of single reinforcement learning strategy and lack of flexibility and adaptability in the prior art, so that the robot can dynamically adjust the optimization algorithm according to the actual situation, and the task completion quality and efficiency are improved.

The invention discloses a multi-robot path planning method and system based on hierarchical control, and the method comprises the steps: obtaining model information and basic parameters of each robot in the system, and constructing a real map of a working environment through an SLAM algorithm; merging grids of the constructed real map, converting a fine grid map into a coarse-grained grid map, and considering the overall moving direction and the target position of the robot to obtain global path input of the navigation system; inputting and issuing the global path to each robot, and capturing global path information of other robots by subscribing paths of other robots among the robots; the robot perceives local environment information in the local map and obtains surrounding obstacle conditions of other robots; according to a global path result of the MAPF algorithm, a local path planning method is adopted to generate a local path, and avoidance between the robots is realized; and a reliable solution is provided for efficient cooperation of a multi-robot system.

The invention relates to the technical field of industrial automation, and discloses a multi-robot collaborative operation method for new energy street lamp production, and the method comprises the steps: firstly, carrying out the function, time sequence and spatial dimension decomposition of a production order, and generating a structured task package; secondly, based on a dual optimization target of maximizing global production efficiency and robot load balancing, the task package is distributed to the optimal robot unit through an intelligent optimization algorithm; thirdly, planning a conflict-free cooperative motion track for each robot through a space-time coordination algorithm, and judging and solving potential motion conflicts based on the traffic priority; and finally, through synchronization strategies such as master-slave control or distributed negotiation and in combination with closed-loop adjustment of sensor data, high-precision collaborative execution of the robot is realized. The invention further comprises a real-time monitoring and hierarchical exception handling mechanism based on digital twinning. According to the method, the collaborative operation efficiency, the flexibility degree and the operation stability of the multi-robot system can be remarkably improved.

The invention discloses a multi-robot path planning method in a complex environment based on MQAHA, and belongs to the technical field of multi-robot path planning. According to the method, firstly, an optimal point set initialization strategy based on the number theory is adopted, an initial population which is evenly distributed is constructed, and diversity and coverage of a search starting point are remarkably improved; secondly, proposing an elite-guided variation migration foraging strategy, fusing direction guidance of a current optimal solution and a disturbance mechanism of a random individual, and effectively enhancing local development capability and global escape performance; furthermore, a learning-driven strategy self-adaptive selection mechanism is introduced, the foraging behavior probability is dynamically adjusted according to the population state, efficient balance of exploration and development is achieved, and therefore the premature convergence problem of the algorithm is systematically relieved. According to the method, the multi-robot cooperative motion path can be quickly generated in a complex obstacle environment, static and dynamic obstacles are effectively avoided while the total path length is reduced, and the motion efficiency and operation safety of a multi-robot system are remarkably improved.

The invention discloses an intelligent mobile mechanical arm composite robot system and method controlled by the Internet of Things, and belongs to the field of application of intelligent robots and the Internet of Things. Aiming at the problems of disjunction between global scheduling and local security, confrontation congestion of robots, difficulty in optimizing data dispersion, difficulty in equipment migration and the like in the existing multi-robot system collaboration, a cloud-side-end collaboration architecture is provided, wherein a cloud side is responsible for task allocation and time sequence arrangement; the edge end is responsible for rolling execution and safety control; and the robots perform mutual avoidance, road occupation and relay based on collaborative semantics. The system comprises a cloud job arrangement module, an edge execution control module, a cooperative communication module and a data closed loop module. According to the method, conflict congestion is reduced through a complete collaborative semantic mechanism, a data-driven strategy optimization closed loop is established, decoupling of the method and equipment is realized, and the operation efficiency, safety and mobility of multiple robots in a complex scene are remarkably improved.

The application relates to a robot cluster control method, which collects local environment data through a robot cluster and sends the data to an edge terminal, while the edge terminal acquires global environment data. Based on deep analysis and processing of the global and local environment data, the edge terminal can accurately identify dynamic targets in the environment and their detailed dynamic information. Based on this information, the edge terminal determines a robot task and selects a target robot most suitable for executing the task from the robot cluster, avoiding blind allocation of the task. Further, the edge terminal generates task execution information for the target robot based on the robot task, so that the target robot can act according to the instruction. The application greatly improves the global planning capability of the multi-robot system, so that the robot cluster can quickly and efficiently respond in a complex environment, realize close collaborative work, and significantly improve the ability and execution efficiency of the multi-robot system in dealing with complex tasks.

The invention relates to the technical field of path planning, and particularly discloses a multi-robot path planning method based on a fusion algorithm, and the method comprises the steps: 1, obtaining environment data through a sensor, analyzing and integrating the environment data, and constructing an environment model in combination with task features; 2, decomposing the whole task into sub-tasks, and dynamically allocating the tasks to different robots according to geographic positions and priorities; 3, performing path planning by adopting a multi-robot path planning algorithm based on task priority, and optimizing the path by using a machine learning algorithm; step 4, adopting a hierarchical multi-sensor information fusion algorithm to enable the robot to track a path center line and to rapidly and effectively avoid obstacles; according to the method, the moving safety and efficiency of the robot in a complex environment are improved, the dynamic adaptive capacity and cooperation capacity of the robot are enhanced, and a solid foundation is provided for popularization of a multi-robot system in practical application.

This invention discloses a method and system for multi-robot collaborative operation in oil and gas fields based on a large language model, relating to the fields of robot collaboration and intelligent planning. The method acquires oil and gas field operation task information, pre-sets task construction rules, uses a large model to parse task instructions and form a hierarchical task tree, and then writes oil and gas field operation constraint information to form a constrained hierarchical task tree. Simultaneously, it acquires robot capability information and constructs a skill library, matches and selects execution robots, and thus determines the multi-robot collaborative operation arrangement to complete the oil and gas field operation task. The system includes a task acquisition module, a task rule pre-setting module, a task tree generation module, a constraint writing module, an execution subject determination module, and a collaborative execution module. This invention, through the collaborative design of the method and system, enhances the understanding and collaborative stability of multi-robot systems for complex tasks, improving their collaborative efficiency and task completion quality in oil and gas field scenarios.