38 results about "Real-time path planning" patented technology

The invention provides an intelligent tactile stick interaction method and system based on multi-modal perception and edge calculation, and relates to the technical field of intelligent auxiliary equipment. The method comprises the following steps: acquiring environment and position data through a camera, a GPS and an inertial measurement unit; performing multi-modal data fusion and deep learning identification by using an edge calculation module to generate a dynamic environment map; performing real-time path planning and obstacle avoidance based on a map and a destination; analyzing a user instruction through voice recognition and interactively adjusting a path; haptic and auditory bimodal feedback is provided through a vibration module and a voice synthesis module according to a navigation result; and when an emergency obstacle is detected or a user actively alarms, a local and remote alarm mechanism is triggered. The system correspondingly comprises an intelligent tactile stick terminal and a cloud server. According to the invention, accurate and real-time perception and intelligent navigation of the environment are realized, visual and natural interaction experience is provided, an effective emergency help channel is established, and the safety, independence and convenience of travel of visually impaired people are significantly improved.

The invention discloses a dynamic obstacle avoidance and path optimization method based on a body intelligence robot, and relates to the technical field of robot path planning, and the method comprises the following steps: collecting map data and task constraint data; performing path planning analysis on the map data and the task constraint data based on a set path optimization algorithm to generate initial planning path data; according to the intelligent robot dynamic obstacle avoidance and path optimization method, a robot actual driving path prediction model is constructed, and a target function is constructed for the deviation between the robot actual driving path prediction model and the real-time path planning data of the next time period to carry out minimization analysis; and the real-time path planning data of the next time period is optimized, so that the robot runs according to the optimized real-time path planning data to obtain an actual running path which fits the original real-time path planning data, and the running error of the robot is reduced.

The invention relates to a real-time path planning method and system based on conflict resolution, equipment and a medium. The method comprises the following steps: preprocessing original observation data to obtain an evidence unit of each sensor; calculating a total conflict amount and a sensor pair contribution matrix based on an evidence unit, updating the credibility of each sensor and selecting a fusion operator to obtain fusion evidence and a conflict degree after fusion; a short-term time sequence is generated according to the fusion evidence and the conflict degree after fusion, the expected occupation probability and the expected conflict trend of each grid unit are predicted, and a time sequence conflict map is obtained; according to the time sequence conflict map, conflict-level adjacent grid unit aggregation fuzziness and area-level conflict characteristics are carried out; and inputting the fusion evidence, the conflict fluctuation degree, the expected stable time and the region-level conflict feature as cost and constraint into a real-time local path planner to obtain a control decision. By adopting the method, the problem of path decision oscillation caused by multi-source sensor conflicts can be relieved.

The invention discloses a real-time processing method and system for robot dog environment perception and decision, and relates to the field of robot dogs. Carrying out multi-source data fusion and semantic segmentation based on deep learning; carrying out semantic enhanced 3D point cloud construction and target detection; real-time path planning and obstacle avoidance decision making in a dynamic environment; and carrying out adaptive motion control and system fault-tolerant processing. According to the invention, through four core technologies of multi-modal fusion, semantic enhancement, real-time calculation optimization and intelligent decision making, the robustness and real-time performance of multi-modal sensor fusion are significantly improved, and the precision of environmental perception is improved; calculation delay is greatly reduced, edge computing power distribution is optimized, and high-real-time decision making is achieved; the intelligent obstacle avoidance and path planning capability in the dynamic environment is enhanced, the intelligent decision-making capability in the dynamic environment is improved, the reliability and practicability of the system are improved, and an efficient, stable and low-cost solution is provided for large-scale application of the system in the fields of industry, security and logistics.

The invention discloses a multi-sensor data fusion method for an intelligent transfer device of power equipment, and the method comprises the steps: carrying out the dynamic weight fusion of multi-source data through building a measurement model of a plurality of visual sensors, and generating a high-precision measurement sequence; in combination with an improved adaptive Kalman filtering algorithm, process noise and a measurement noise covariance matrix are estimated in real time, and the stability and the anti-interference capability of state estimation are optimized. And based on the fused high-precision state information, a real-time path planning instruction is generated, and the transfer device is controlled to complete obstacle avoidance and millimeter-level docking of the cabinet body. The problems that traditional manual transfer efficiency is low, the positioning error is large, and a single sensor is prone to electromagnetic interference are solved, the navigation precision and operation safety of heavy equipment in the complex electromagnetic environment are remarkably improved, and the method is suitable for intelligent transfer scenes of equipment such as transformer substation circuit breaker handcarts and the like.

The invention discloses an AI path planning method and system based on deep reinforcement imitation learning, and the method comprises the steps: carrying out the weighted fusion through introducing a state incidence matrix, and constructing an environment state space with time sequence correlation; on the basis of a generative adversarial imitation learning framework, adversarial training of a strategy generation network and a strategy discrimination network is carried out by utilizing a pre-trained expert strategy database; a master-slave distributed architecture is adopted to train a plurality of strategy network copies in parallel, and global parameters of the strategy generation network and the strategy discrimination network are optimized through an aggregation gradient and parameter soft update mechanism; and deploying the lightweight strategy generation network, outputting an action instruction according to a real-time environment state, and obtaining a real-time path planning result through a dynamic path correction mechanism and online performance fine adjustment. The problem that the robustness, the safety and the efficiency of path planning of the robot in a complex environment are difficult to meet actual requirements is solved.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention relates to a high-precision assembly path planning method for an artificial intelligence cooperative industrial robot, and the method comprises the steps: replacing the path planning of a high-dimensional configuration space of an assembly system with the multi-group path planning of a low-dimensional configuration space of an industrial robot under the condition that the mutual collision of the industrial robots is not considered; and then iteratively searching and detecting conflicts among the candidate paths, and eliminating the conflicts among the candidate paths by utilizing low-cost candidate path replacement and new candidate path generation so as to obtain collision-free candidate paths among industrial robots, and combining the collision-free candidate paths to obtain a plurality of groups of collision-free assembly system path planning results. And selecting the path planning result with the optimal cost from the path planning results of the assembly system as the final path planning result of the assembly system. Through the design, dimension reduction in the path planning process is achieved, the path planning calculation efficiency is improved, and real-time path planning of a complex industrial robot assembly system is better met.

The application discloses a kind of path planning systems of dike breach rescue equipment, comprising: environment perception module, for obtaining environmental data and position information, constructs rescue environment digital model;Path planning model is used to determine path planning based on rescue environment digital model;Path planning model is strategy network and safety prediction network coupling reward function, fusion reliability safety index component intelligent agent decision model, is trained based on PPO algorithm and is constructed generation;Wherein, strategy network is used to obtain shared features, to safety prediction network output action mean, according to total reward determines the path trajectory of equipment target;Interaction module is used to output real-time path planning result, risk probability and safety classification.According to the above technical scheme, the dynamic coupling of geological bearing capacity, water flow power, boat bridge stability and equipment dynamics is comprehensively considered, and global safety constraint is formed;Ensure the transportation safety of equipment in complex environment, greatly improve the efficiency of rescue.

The invention relates to the field of safety science and emergency management, and provides a real-time path planning method and device for personnel evacuation in a building fire scene, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a building fire map in real time, for any sampling moment, constructing a sampling evacuation path based on a preset algorithm, and forming a real-time evacuation path according to the change of the sampling evacuation path along with time; the preset algorithm comprises the following steps: constructing a preliminary evacuation path according to a preset path planning algorithm; constructing initial pheromone distribution of the ant colony algorithm according to the initial evacuation path, wherein the pheromone concentration on the initial evacuation path in the initial pheromone distribution is higher than the pheromone concentration of a common grid; and constructing a sampling evacuation path according to the initial pheromone distribution and an ant colony algorithm. The method has the technical effects of improving the ant colony algorithm search speed, improving the convergence speed, improving the reliability of the path planning result, and improving the personnel evacuation efficiency and the personnel evacuation safety.

The embodiment of the invention provides a mobile robot sound source tracking method, a target mobile robot, equipment and a medium, and belongs to the technical field of robot intelligent control, the method is applied to the target mobile robot, and the method comprises the following steps: collecting and positioning a multi-channel audio signal to obtain an estimated navigation point; performing weighted average processing on the estimated navigation point and the historical navigation point to obtain a target navigation point, performing global and local path fusion planning on the environment states of the target navigation point and the target mobile robot, outputting a fusion control instruction, and performing navigation control according to the fusion control instruction. According to the embodiment of the invention, the estimated navigation point is generated by collecting and positioning the audio signal, and weighted average processing is performed by combining the historical navigation point, so that the sound source can be positioned only according to the sound source signal, and real-time path planning and control instruction generation are performed by further combining the environment state and the target navigation point; operation of the mobile robot can be ensured in a graph-free environment, and the tracking effect of the mobile robot is improved.

The invention relates to an automatic driving dynamic path planning method and system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting real-time path planning information; transmitting the real-time path planning information to a path planning model, wherein the path planning model outputs an optimal path analysis result; generating a specific path planning scheme based on the optimal path analysis result, and transmitting the specific path planning scheme to an execution mechanism of the automatic driving system; and the execution mechanism is used for controlling the vehicle to run according to the specific path planning scheme. By adopting the method, automatic driving path planning can be realized by means of a big data model.

The invention relates to an unmanned aerial vehicle intelligent path planning method and system based on visual signals, and belongs to the field of path planning. The method comprises the following steps: optimizing a neural network through progressive pruning; fuzzy coordinates and scene types are provided for the unmanned aerial vehicle, the unmanned aerial vehicle flies to a fuzzy coordinate area, and when the unmanned aerial vehicle is close to the fuzzy coordinate area, the unmanned aerial vehicle is switched to a pure vision mode to perform environment perception through a neural network, and a three-dimensional semantic map is constructed; performing real-time path planning based on the three-dimensional semantic map to obtain a flight path; the method comprises the following steps: performing autonomous flight along a flight path, when approaching an operation target, performing autonomous operation to obtain real-time monitoring data, storing the real-time monitoring data in airborne storage equipment, and returning the real-time monitoring data after returning. And an unmanned aerial vehicle intelligent path planning system with high robustness, high autonomy and high task adaptability is constructed.

The invention discloses a real-time path planning method and system for a fixed-wing unmanned aerial vehicle in simulated flight and electronic equipment. The real-time path planning method comprises the following steps: S1, importing discrete elevation data of a task area; s2, modifying the current route of the unmanned aerial vehicle based on the security constraint, and calculating discrete points between the current point of the unmanned aerial vehicle on the current route and the latest route point; s3, the discrete points are initialized; and S4, planning an optimal path of the unmanned aerial vehicle by using an algorithm and the initialized discrete points. According to the method, the bidirectional A * algorithm is adopted to synchronously initiate search from the starting point and the target point, and the invalid search range in a complex three-dimensional space is greatly reduced.

The invention discloses an automatic warehousing system for intelligent warehousing, and relates to the technical field of intelligent warehousing. The system comprises a warehouse management system, a transfer robot, a warehousing temporary storage area and a warehouse area. The system realizes automatic warehousing through the following steps: an operator inputs article information in the warehouse management system and uploads a warehousing task; the system retrieves the article information, judges whether the article is a new warehousing article or fills up an existing article, and pushes a task to the transfer robot; the transfer robot goes to the storage area according to the planned path to take vacant or non-full storage boxes; after an operator puts articles into the storage box, the articles are conveyed to the output table through the conveying table; and the transfer robot takes the goods and returns to the goods shelf. According to the system, through priority management, real-time path planning, positioning identification navigation and a multi-robot cooperative obstacle avoidance mechanism, the warehousing process is optimized, and the automation level and efficiency of warehouse management are improved.

The application discloses a multi-agent dynamic real-time path planning method and device based on signal timing logic, and relates to the technical field of control methods. The method comprises the following steps: acquiring multi-robot path planning task information and a preset space-time constraint task demand; constructing a multi-robot task specification based on signal timing logic, and defining a corresponding robustness evaluation function; constructing a comprehensive path cost function based on the robustness evaluation function; introducing the path cost function into a path expansion and reconnection process based on a real-time rapid expansion random tree algorithm, updating a search tree structure, and generating a candidate path meeting environment constraints and timing constraints; planning paths for robots in sequence according to a preset multi-robot cooperative planning strategy, taking the path information of the robots that have been planned as dynamic obstacles and performing progressive cooperative obstacle avoidance, and outputting a conflict-free trajectory adaptive to a dynamic environment. The application can solve the path planning problem in a multi-agent scene.

The invention relates to the technical field of cargo transportation scheduling, in particular to an intelligent vehicle dispatching method and system based on cargo transportation, and the method comprises the steps: firstly, integrating delivery orders, vehicles and warehouse geographic information, automatically recognizing and merging abnormal orders of the same contact person at different addresses, and forming a unified delivery task; secondly, dynamically evaluating the daily transport capacity load based on historical data, and intelligently recommending an alternative delivery date for an order exceeding a threshold value; and finally, screening orders according to region limitation, taking the order with the maximum weight as a path anchor point, performing real-time path planning in combination with the vehicle region proficiency and the loading capacity, and generating an optimal vehicle dispatching scheme after ensuring that constraint conditions such as a working time window are met. Through intelligent data integration, dynamic load evaluation and adaptive path planning, automation and precision of cargo transportation and vehicle dispatching are realized, the distribution efficiency and the resource utilization rate are effectively improved, the manual dispatching cost and the error risk are reduced, and the flexibility and practicability of the system are enhanced.

The invention relates to the technical field of unmanned aerial vehicle inspection of ultrahigh-voltage lines. The invention discloses an ultrahigh-voltage line unmanned aerial vehicle inspection path optimization method fusing dynamic obstacle prediction and a multi-target chaotic particle swarm. The method is characterized by comprising the following steps: 1) constructing an ultrahigh-voltage line inspection environment obstacle space model; 2) routing inspection path obstacle avoidance design based on an improved particle swarm algorithm; 3) real-time path planning and track adjustment: updating the obstacle space model; path planning is optimized through a heuristic function; in the flight process, if a collision risk is detected or the unmanned aerial vehicle enters a dangerous area, the position and the speed are adjusted through a trajectory adjustment formula according to real-time obstacle information and an unmanned aerial vehicle kinetic model and are fed back to a control system for execution, and a strategy is iteratively updated. According to the method, the unmanned aerial vehicle can accurately avoid obstacles in the inspection process, the optimality and adaptability of the inspection path are improved, and the intelligent inspection requirement of the ultrahigh-voltage line is met.

The application discloses a kind of Web end real-time path planning method, system, storage medium and equipment, it is related to path planning technical field, the method includes: in the driving process of vehicle, traffic data and environmental change information in the application scene of Web end are collected in real time and are converted into the standard data format for algorithm processing;Path initial planning is carried out using hybrid algorithm, and the initial path is obtained;The actual driving information of path is obtained with the user feedback of environmental perception device, the actual driving information of path is compared with initial path, whether there is difference in actual path in the actual driving information of path is judged;If yes, according to the real-time correction of the recommended path in navigation system according to preset proofreading rule, to obtain target navigation path for sharing to other vehicles;Wherein, hybrid algorithm is the combination algorithm of genetic algorithm and ant colony algorithm.The application realizes the real-time optimization of distribution path, efficiently responds to various road condition changes and satisfies customer individualized demand.

The invention relates to the technical field of route path planning, discloses a self-adaptive route path generation method for line fault driving, and aims to improve the safety and efficiency of a route. According to the method, historical route operation data are collected and preprocessed, a route fault prediction model is constructed, and the fault occurrence probability and type on a future route are predicted. And acquiring aircraft performance parameters and environmental condition data in real time, inputting the prediction model to acquire fault information, comparing the fault information with the real-time running state data of the route, and marking a fault deviation point. And modeling the route planning process into a Markov decision process, and constructing a route decision model. And performing real-time path planning based on the trained decision model, and selecting an optimal route adjustment action according to the current aircraft state and environmental conditions. According to the method, line faults can be sensed in real time, the route path can be adaptively adjusted, dynamic changes are effectively handled, and the safety and efficiency of route operation are improved.

The invention provides a switch cabinet live detection method and system based on a humanoid robot, and relates to the technical field of power grids. The target position and the detection item are automatically obtained by analyzing the detection task instruction, and real-time path planning and obstacle avoidance are performed by fusing multi-sensor data, so that the robot can autonomously move to an operation point in an optimal path, and time consumption of reciprocating walking and positioning of personnel is avoided; by controlling the humanoid mechanical arm to automatically switch probes according to detection types and execute standardized detection actions, tedious operations of manually holding, moving and replacing instruments are thoroughly replaced, and repeated measurement caused by misoperation is avoided; real-time fusion analysis and cross validation are performed on multi-source data through a built-in algorithm, and a detection result containing a fault mode is automatically generated, so that real-time intelligent live detection of the switch cabinet is realized, the technical problem of low efficiency of live detection of the switch cabinet is solved, and the detection efficiency of live detection of the switch cabinet is improved.

The invention discloses a path planning system for dike breach rescue equipment, and the system comprises an environment sensing module which is used for obtaining environment data and position information, and constructing a rescue environment digital model; the path planning model is used for determining path planning based on the rescue environment digital model; the path planning model is formed by coupling a reward function of a strategy network and a security prediction network, fusing a reliability security index to form an agent decision-making model, and performing training construction based on a PPO algorithm; wherein the policy network is used for acquiring shared features, outputting an action mean value to the security prediction network, and determining a path trajectory of an equipment target according to a total reward; and the interaction module is used for outputting the real-time path planning result, the risk probability and the security classification. According to the technical scheme, dynamic coupling of geological bearing capacity, water flow power, pontoon bridge stability and equipment dynamics is comprehensively considered, and global safety constraints are formed; the transportation safety of the equipment in a complex environment is guaranteed, and the rescue efficiency is greatly improved.

The application relates to the technical field of intelligent storage, and relates to a four-way shuttle vehicle storage method and system based on dynamic access, which comprises the following steps: confirming a shuttle vehicle storage environment based on a shuttle vehicle storage instruction, obtaining a set of goods location information based on a dynamic inventory sensing unit and a storage warehouse, obtaining a plurality of target goods locations based on order demand items and the set of goods location information, calculating a plurality of comprehensive scores of the goods locations based on the plurality of target goods locations, confirming an access location based on the order demand items, obtaining a nearest shuttle vehicle based on the access location, performing path planning based on the nearest shuttle vehicle and the optimal goods location, performing conflict detection on the shortest path based on a real-time path planning unit, storing and taking the goods based on the optimal planning path, and updating the set of goods location information based on the stored and taken goods. The application can improve the efficiency, accuracy and intelligent level of four-way shuttle vehicle storage.

A unified mapping apparatus for real-time path planning and global localization for a mobility device such as an autonomous or self-guiding robot or drone is provided. The unified mapping apparatus comprises a static mapping module and a dynamic mapping module. The primary function of the static mapping module is to scan the mobility device's surrounding environment static geometric for the eventual goal of global path planning. The static objects scanned and identified by the static mapping module are used for global localization. The primary function of the dynamic mapping module is to track moving objects in mobility device's surrounding environment, and predict such objects' movements. With the prediction of dynamic objects' movements, real-time path planning can be improved substantially.

The invention discloses a real-time path planning system and method for a mechanical arm in an unstructured environment, and the method comprises the steps: constructing a large-scale parallel simulation training scene based on a GPU physical engine, and creating a parallel simulation system comprising thousands of mechanical arm examples through an example cloning technology; the method comprises the following steps: realizing extraction and dimension reduction of three-dimensional environment features based on a variational auto-encoder technology, and compressing a high-dimensional depth image into a low-dimensional implicit feature vector; a time sequence deep reinforcement learning algorithm fusing course learning and a self-attention mechanism is adopted, and a smooth and stable real-time path planning strategy is generated through fusion of a historical observation sequence and a mechanical arm dynamic state. According to the method, the path planning success rate of 95.2% is achieved in the unstructured environment containing 3-5 dynamic obstacles, the average planning time is 0.12 s, and the autonomous operation capacity of the mechanical arm in complex scenes such as agricultural picking, mine rescue and electric power overhaul is remarkably improved.

The invention discloses an intelligent vehicle navigation method and device for multi-source information fusion in an urban environment, and the method comprises the steps: collecting and fusing multi-source heterogeneous traffic information in real time, comprising traffic state data, satellite navigation information, ground sensor data, road camera recognition results, vehicle-mounted sensing system data, traffic broadcast information and V2X communication data; the method comprises the following steps: standardizing multi-source heterogeneous traffic information, constructing a dynamic weighted road network diagram, and adaptively calculating the comprehensive weight of each edge; performing real-time path planning in the dynamic weighted road network map by adopting a path search algorithm based on graph neural network optimization; when a V2X emergency message or sensor data sudden change is detected, triggering hierarchical re-planning; and outputting the optimized navigation path and controlling the vehicle to run. According to the method, the multi-source heterogeneous traffic information is integrated, the dynamic weighted road network map is constructed, and the map neural network is adopted to optimize the path search algorithm, so that efficient and reliable intelligent vehicle navigation is realized.

This application provides a magnetically guided painless enema intelligent control system, relating to the field of medical device technology. The system includes an environmental sensing unit, a data processing unit, a path planning unit, and a control execution unit. The environmental sensing unit acquires information through a monocular camera, IMU, and force sensor; the data processing unit integrates direct monocular SLAM with a depth estimation network to achieve real-time monocular dense mapping and uses federated filtering for multi-sensor fusion localization; the path planning unit performs real-time path planning based on the dense map and uses an improved RRT algorithm with integrated probe mobility constraints; the control execution unit generates dynamic magnetic traction for the in-body probe using an external five-axis robotic arm and electromagnetic coils, and uses an active disturbance rejection controller to achieve precise pose tracking. This application enables safe and compliant movement of the enema tube and precise drug delivery during the enema process, solving the comfort and safety issues of left-sided colonic drug delivery in patients with active enteritis, and improving patient compliance.

1. Name of the designed product: unmanned aerial vehicle (3D laser radar SLAM). 2. Use of the designed product: unmanned aerial vehicle mainly used for high-precision navigation, obstacle avoidance, environment perception and real-time path planning. 3. Design points of the designed product: shape. 4. Pictures or photos that best show the design points: perspective view.

The invention belongs to the technical field of path planning, and particularly discloses a robot real-time path planning method and system and a program product, global path planning is carried out in parallel by adopting an A * algorithm, and an optimal global path is selected through cost evaluation to carry out robot global path navigation. Secondly, collecting environment parameters in real time in the global path navigation process of the robot to perform dynamic environment modeling, and performing real-time local path planning by utilizing a dynamic environment model and adopting a pre-trained PPO algorithm so as to control the robot to perform real-time local path adjustment by deciding local actions; finally, the robot is guided to efficiently and safely arrive at the global path end point. According to the method, the high efficiency of the traditional A * algorithm and the excellent decision-making ability of the reinforcement learning algorithm can be effectively combined, so that efficient, safe and real-time robot dynamic path planning is carried out, and rapid search and dynamic optimization of the real-time path of the robot under global path planning are realized.

The invention provides a tunnel TBM path adjustment real-time planning method based on an intelligent algorithm. The tunnel TBM path adjustment real-time planning method comprises five steps of data acquisition, data calculation, deviation judgment and decision making, neural network prediction and correction strategy generation. The data acquisition comprises tunnel section data acquisition and attitude parameters of the shield tunneling machine; the data calculation comprises point cloud denoising, point cloud registration, data fusion, axis reconstruction, deviation index calculation and result output; the deviation judgment and decision-making decides whether deviation correction is carried out or not; neural network prediction is based on LSTM + CNN hybrid model processing time-space sequence data, and future offset is predicted; and the deviation correction strategy converts a prediction result into an executable control instruction through reinforcement learning. According to the method, real-time path planning and deviation rectification are carried out through combination of multi-modal data fusion and a hybrid intelligent algorithm, three-dimensional laser scanning and TBM data, the tunneling path is measured and adjusted in real time after tunneling is completed each time, the problem of path planning lag of a traditional method under complex working conditions is solved, and the tunnel construction precision and efficiency are improved.