272 results about "Topological map" patented technology

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

According to the invention, based on a low-line-number road side laser radar, real-time extraction and optimization of multi-vehicle tracks are researched, and a track completion method and system combined with a road topological map are provided for solving the problem of track interruption caused by mutual shielding of vehicle targets in a complex environment. According to the method, track completion is carried out by obtaining a full-time motion mode, context information and environment characteristics of a vehicle in a scene and combining a road topological map. Specifically, the topological map is coded by adopting a gating loop unit and a map attention network, and missing trajectory data is generated by utilizing a diffusion probability model based on trajectory context and map condition constraints. By integrating point cloud data acquired by a roadside laser radar, a vehicle track is complemented, and a high-precision continuous track conforming to road constraints, traffic rules and vehicle kinematics characteristics is generated. The trajectory completion method has important significance in improving the intelligent traffic decision-making level and promoting the application of the intelligent driving technology.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention relates to the technical field of photovoltaic equipment cleaning, and discloses an autonomous path planning system and method based on a photovoltaic cleaning robot. The system comprises an environment sensing module which is used for collecting three-dimensional point cloud data on the surface of a photovoltaic panel and reducing noise; the path planning module is used for constructing a topological map and optimizing a global path; the obstacle detection module is used for identifying obstacles and extracting physical attributes of the obstacles; the dynamic adjustment module is used for analyzing path conflicts and dynamically re-planning; and the execution control module generates a robot motion instruction. According to the method, through cooperative work of the modules, efficient path planning and cleaning work of the photovoltaic cleaning robot in a complex environment are achieved. Compared with a traditional cleaning mode, the cleaning efficiency and quality are improved, the cost is reduced, the adaptability of equipment to a complex environment is enhanced, and powerful technical support is provided for development of the photovoltaic industry.

The invention relates to the technical field of path planning, and discloses a robot mechanical arm path planning method and system for curtain wall cleaning. According to the method, collected data are fused through a depth camera, a laser radar and an inertial measurement unit, a dynamic environment topological map is constructed, and accurate environment perception is achieved. A self-adaptive grid method and an improved spiral traversal algorithm are adopted, a global traversal path covering a cleaning area is generated, the joint motion trail of the mechanical arm is optimized in a layered mode, and motion accuracy and stability are ensured. And meanwhile, the dynamic control module monitors obstacles in real time, the joint track is adjusted online through local track re-planning, it is guaranteed that cleaning operation is safe and continuous, the cleaning pressure is adjusted in real time, and it is guaranteed that the cleaning quality is stable and consistent.

The invention provides a visual language navigation method and system based on collaborative alignment and adaptive fusion, and belongs to the field of artificial intelligence and computer vision. The method comprises the following steps: acquiring a natural language instruction, a panoramic view, navigation history and dynamic topological map data, and extracting instruction features, visual features, navigation history features and map node features; performing feature enhancement pre-alignment on the instruction features and the visual features, inputting the instruction features and the visual features into an encoder for deep fusion, and generating global multi-modal fusion features and auxiliary decision signals; inputting the global multi-modal fusion feature and the auxiliary decision signal into a coarse and fine granularity combined expert network, and outputting a final predicted navigation action; wherein a predicted value is obtained by using an expert network; and constructing a multi-source uncertainty vector to generate a fusion weight, and fusing a predicted value to obtain a final predicted navigation action. The problem of modal gaps in a traditional method is effectively solved, and the understanding accuracy of complex instructions and the stability and success rate of navigation are improved.

The invention relates to the technical field of warehouse logistics equipment, and discloses an omni-directional intelligent mobile forklift control system. The system comprises a dynamic path planning module, an omnidirectional motion control module, a load stability monitoring module, an environment adaptive adjustment module and an intelligent obstacle avoidance decision module. The dynamic path planning module collects storage environment multi-dimensional spatial data and constructs a dynamic environment topological map; the omni-directional motion control module analyzes driving wheel parameters based on the map and generates an omni-directional motion composite motion instruction set; the load stability monitoring module collects a fork pressure distribution matrix and a cargo gravity center offset according to an instruction set, and calculates a stability compensation parameter; the environment adaptive adjustment module fuses illumination and slope data to generate a motion control constraint condition; and the intelligent obstacle avoidance decision module predicts a dynamic obstacle trajectory, outputs an obstacle avoidance path re-planning instruction and updates a map. The system can meet the requirements of modern intelligent warehousing.

The invention relates to the technical field of laser processing, and discloses a laser processing device and method suitable for TGV forming, and the method comprises the steps: S1, a glass substrate is scanned in a confocal manner, a three-dimensional topological graph is obtained, and the three-dimensional topological graph comprises the three-dimensional coordinate, the thickness gradient and the positioning mark of the glass substrate; s2, based on the three-dimensional topological graph, a through hole body is formed through ultraviolet femtosecond laser etching, and intermediate infrared laser is synchronously and dynamically controlled to etch the side wall of the through hole body to generate a taper through hole with a preset taper angle; and S3, the incident angle of the ultraviolet femtosecond laser is adjusted on the side wall of the taper through hole according to the three-dimensional topological graph, and a plurality of heat dissipation grooves are etched through the ultraviolet femtosecond laser. According to the laser processing device and method suitable for TGV forming, signal integrity optimization and active heat dissipation functions are achieved in the same through hole, the application range of the TGV is widened, and the stability of the TGV during use is improved.

The invention provides an agent visual language navigation method and system based on task completion prediction. The method comprises the step of constructing a dual-drive structure composed of a self-adaptive mixed pooling mechanism and a task completion analysis module. Firstly, in the visual information processing process, a dynamic weight distribution strategy is adopted to carry out multi-scale adaptive mixed pooling on panoramic features, so that the fusion effect of local and global semantic information is optimized, and the retention capability and semantic integrity of navigation historical information in a dynamic topological map are improved. And then, inspired by a human navigation cognitive behavior mechanism, a task completion analysis module is designed and introduced, and the task execution progress is dynamically estimated based on the recognition condition of a key landmark in a navigation path, so that an intelligent agent is driven to preferentially select a key path node and invalid exploration is reduced. And finally, realizing efficient understanding and execution of the natural language instruction by the intelligent agent through a multi-round cyclic cross-modal reasoning and action prediction mechanism.

The invention provides a simulation and data combined driving coal mine fire disaster dynamic disaster avoidance route planning method, and belongs to the technical field of coal disaster prevention. The technical problems that lag is caused by traditional timing updating, multiple disaster avoidance paths cannot be planned for people in different areas at the same time, and the risk that people cannot pass through a single path is caused are solved. According to the technical scheme, S1, the position of a fire source is positioned; s2, distinguishing a safe area, an affected area and a high-danger area; s3, monitoring a roadway damage condition and updating the topological graph; s4, establishing a fire hazard and toxic and harmful substance spreading prediction model; s5, predicting the change trend dynamic of each factor influencing escape; s6, outputting optimal paths of different areas; and S7, sending the optimal path. The method has the beneficial effects that a plurality of disaster avoidance paths can be planned for personnel in different areas, the passing speed and weight of each roadway are updated in real time when a disaster occurs, the disaster avoidance paths are dynamically adjusted, and the escape success rate of miners is improved.

The invention provides a wireless signal detection and positioning method and system, and relates to the technical field of wireless communication, and the method comprises the steps: collecting signal characteristic parameters through a plurality of monitoring devices, constructing a three-dimensional time-frequency space matrix, carrying out envelope modulation to construct a primary function, carrying out iterative projection to obtain a reconstructed signal, and extracting a characteristic fingerprint through a high-order statistical moment; constructing a topological graph according to geographic distribution to calculate information entropy, and performing dynamic clustering fusion; a scene type is identified and a location is estimated in combination with historical data. The wireless signal detection accuracy and the positioning precision can be improved, and the method adapts to a complex electromagnetic environment.

The invention relates to the technical field of network security, and discloses a protection system for network security authentication. The system comprises an identity verification module, a behavior analysis module, a threat detection module, a dynamic encryption module and a response processing module. The identity verification module fuses the multi-modal biological characteristics and dynamic token verification to generate a data set; the behavior analysis module extracts behavior time sequence characteristics by using a space-time convolutional neural network; the threat detection module constructs a topological graph based on the graph attention network to detect abnormity; the dynamic encryption module encrypts communication data by adopting a quantum key distribution and chaos sequence algorithm; and the response processing module generates a response instruction set for safety protection according to reinforcement learning. According to the method, the identity verification accuracy is effectively improved, abnormal behaviors and threats are accurately detected, dynamic encryption and intelligent response are realized, and the network security protection level is remarkably improved.

The invention relates to the technical field of robot navigation, and discloses an automatic path-finding intelligent robot and a path-finding method thereof. According to the method, multi-dimensional spatial data including obstacle distribution, ground features and dynamic target motion information is acquired through an environment sensing device, spatial discretization processing is performed on the data, and a topological map which has a hierarchical structure and includes a node connection relation and a region passing weight is generated. Then, according to the node connection relation of the topological map, a path planning model based on manifold learning is established, and an optimal path candidate set is determined by calculating the manifold distance between adjacent nodes; then extracting a key node sequence in the candidate set, dynamically optimizing a path in combination with a region passing weight, and generating a preliminary navigation track; and finally, inputting the initial track into a motion control model, carrying out smooth processing according to kinematics constraint of the robot, and outputting a final execution path. According to the invention, the robot can efficiently find the way in a complex environment.

The invention discloses a safety risk management and control system for a chemical industry park, particularly relates to the technical field of risk diffusion prediction, and is used for solving the problem that in the prior art, an emergency instruction and a risk propagation path are dynamically mismatched, and cross-regional chain reaction cannot be cooperatively blocked. Environmental parameters and equipment state data are obtained through a data acquisition module, and a fluid correction module extracts a continuous coherent bar code of turbulent vortex to generate a path correction coefficient; the coupling modeling module is combined with a multi-physics field coupling equation to generate dynamic space-time propagation parameters, and the path prediction module predicts a diffusion direction and an influence area according to vector superposition of the parameters and a three-dimensional topological map; the instruction optimization module adjusts the priority and the trigger range of a control instruction, and the execution control module organizes emergency equipment to block a diffusion path according to a preset logic; through dynamic modeling and path matching optimization, time-space cooperation of emergency response and control execution is realized, and the accident diffusion risk of the chemical industry park is significantly reduced.

The invention discloses a visual language navigation method and system based on a monocular camera and a language instruction, and the method comprises the steps: S1, feature field construction: employing a 3DGS to construct a 3DGS feature field by employing visual information obtained by a monocular RGB-D camera; s2, implicit partial complementation: inferring the representation of a missing region through a context feature relationship to generate a complete feature map; s3, waypoint prediction: generating an aerial view feature map based on the 3DGS feature field, and predicting nearby navigable waypoints through a waypoint predictor; s4, based on active perception of uncertainty, when the robot is uncertain in navigation decision, more visual information is acquired and missing visual information is supplemented by rotating a camera; and S5, constructing a topological map based on the constructed panoramic feature map and the predicted path points, and carrying out navigation decision making by using a panoramic visual language navigation planning model in combination with a language instruction. The problem that monocular vision information is incomplete is effectively solved, and the success rate and efficiency of navigation are remarkably improved.

The invention provides a method and a system for predicting idle parking spaces of a parking lot. The method comprises the steps that ramp curvature parameters extracted from topological structure data of the special-shaped parking lot and historical vehicle turning radius data are fused, and a spatial-temporal topological graph of the special-shaped parking lot is generated; aggregating the parking space occupancy state data of all the special-shaped parking lots and the special-shaped parking lot space-time topological graph through a federal transfer learning framework to obtain target feature extraction model parameters; combining the time change characteristics of the channel flow of the special-shaped parking lot in the spatial-temporal topological graph of all the special-shaped parking lots and the target characteristic extraction model parameters, and generating a dynamic parking space distribution strategy; and mapping the dynamic parking space distribution strategy to a real-time navigation path of the special-shaped parking lot space-time topological graph, so that the to-be-parked vehicle arrives at the idle parking space through the real-time navigation path. According to the invention, the dynamic parking space distribution strategy is generated by fusing the topological structure and historical data of the special-shaped parking lot and real-time navigation is carried out, so that the parking space utilization rate and the parking efficiency are optimized.

The invention discloses an unmanned aerial vehicle automatic homeward voyage method based on a global topological map and a graph search algorithm, which comprises a global passable area topological connected graph module, a homeward voyage path search module, a homeward voyage path optimization module and a homeward voyage path error processing module. According to the method, global path searching is carried out in combination with a Dijkstra algorithm, path smoothness, flight time and obstacle avoidance are optimized by using a B spline curve, the precision and speed of path planning are effectively improved, and meanwhile, the system can cope with changes in the environment by updating a topological connected graph and optimizing the path in real time, so that the path planning efficiency is improved. And secondly, the path safety and the flight stability are both considered, and an obstacle distance penalty term is introduced, so that the flight path can avoid the obstacle, and the collision risk is reduced.

The invention discloses a complex large environment unmanned aerial vehicle autonomous exploration method, system and device and a medium, and relates to the field of path planning and intelligent navigation, the method comprises the following steps: constructing and updating a local occupation grid map based on image information and laser radar point cloud, carrying out semantic segmentation, and establishing a topology subgraph based on a semantic segmentation result; constructing a topological map based on the updated locally occupied grid map and the topological sub-graph by adopting a space internally tangent ball segmentation mode; boundary updating and viewpoint sampling are carried out in the topological map, and global planning is carried out based on the updated boundary and viewpoints obtained through sampling; in the global planning process, whether the unmanned aerial vehicle arrives at a global planning target is determined in real time, so that the unmanned aerial vehicle can realize autonomous exploration of a complex large environment. According to the method, the problem that a cluster aircraft or ground station map is difficult to share due to huge map data volume is solved, navigation and obstacle avoidance can be carried out under the condition that only a local occupied grid map is maintained, and the updating capability of an exploration boundary is reserved.

The invention provides an AGV obstacle avoidance path planning method and system for intelligent storage, and relates to the technical field of path planning. Comprising the steps of collecting warehousing data; constructing a map reflecting the topological structure of the storage space by adopting a topological map; obtaining an initial path by using a pruning optimized Dijkstra algorithm according to a set starting point and an end point; setting a repulsive force factor and a safe buffer area to optimize the initial path to obtain a secondary optimization path; and judging whether the environment is changed or not, and if so, recalculating a new obstacle avoidance path according to the secondary optimization path. According to the method, the initial path is constructed through the pruning optimized Dijkstra algorithm, the repulsive force factor and the safety buffer area are set to optimize the initial path, the overall efficiency and safety of warehouse cargo carrying are improved, and the adaptability of a warehouse system to complex environment changes is enhanced.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention discloses a delivery task cooperative processing method, device and equipment based on a quadruped robot and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a delivery task through an upper computer, combining the real-time dynamic information of an office scene, and employing an improved binary particle swarm optimization algorithm to optimize the task distribution of multiple robots, and efficient collaborative distribution in an intelligent office environment is realized. The robot receives a distribution scheme sent by the upper computer, a multi-level navigation strategy is started by utilizing environmental perception data, a topological map and a D * Lite algorithm are fused to carry out path planning, and dynamic obstacle avoidance and efficient distribution are ensured. The target path planning information is monitored in real time through the upper computer, dynamic analysis is carried out through the scene perception algorithm, the scene relevance result is obtained, the collaborative distribution strategy is executed to complete the distribution task, the task response speed and the distribution efficiency are improved, interference to the office environment is reduced, and the work efficiency is improved. And automation and intelligentization of robot distribution in an intelligent office scene are realized.

The invention discloses a robot automatic mapping method and related equipment in an unknown open environment, and the method comprises the steps: obtaining sensor data, and constructing a local map according to the sensor data; topologizing the local map to obtain a collision-free topological graph of the local map; performing sparse processing on the collision-free topological graph to obtain a sparse topological graph; and inputting the obtained sparse topological graph into a strategy network model based on deep reinforcement learning, and outputting a strategy for controlling the robot to move so as to move the robot. According to the method, the environment mapping problem is modeled into a graph structure, spatial dependency in the environment is modeled through the relation between the nodes, and effective learning of the robot on the relation between different areas in the complex environment is achieved. In addition, the attention mechanism helps the robot to preferentially select the area with the high potential return in the autonomous exploration process by dynamically adjusting the attention weight of each area, and therefore the exploration efficiency is improved. The robot can be widely applied to the technical field of robots.

The invention provides an automobile wire harness short circuit fault detection method, system and device and a medium, and the method comprises the steps: extracting the transient characteristic quantity of a current signal of each monitoring node, and obtaining the amplitude attenuation characteristics of the current signal of each monitoring node in a wire harness propagation process based on the mutual information intensity inversion of the transient characteristic quantity between adjacent monitoring nodes; further identifying an abnormal wire harness path which is obviously influenced by common-mode interference based on the fluctuation difference degree of the amplitude of the common-mode current between adjacent monitoring nodes in spatial distribution, and constructing a common-mode interference topological graph which represents the abnormal state of the wire harness in combination with the response intensity of the common-mode current; and performing fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topological map to obtain the fault membership degree of each monitoring node, and performing short-circuit fault correlation positioning on the target automobile wire harness through all the fault membership degrees. By adopting the scheme of the invention, the associated positioning of the short-circuit fault of the automobile wire harness under the common-mode interference condition can be realized.

The invention discloses a blogger live video AR glasses scenery labeling method and labeling system, and the method specifically comprises the steps: dynamically adjusting a knowledge graph retrieval strategy of a tourism live scene through a hot topic set and an emotion classification result, and obtaining a dynamic retrieval result; the method comprises the following steps: acquiring blogger head posture data based on an IMU (Inertial Measurement Unit) of AR (Augmented Reality) glasses, and constructing a 3D spatial topological map of a tourism live scene through an SLAM (Simultaneous Localization and Mapping) algorithm Spatial registration is carried out on the dynamic retrieval result and the 3D spatial topological map, and a scene enhancement labeling layer is generated through a NeRF algorithm; and displaying the video stream and the scene enhancement annotation layer in an overlapping manner, and dynamically adjusting the annotation visibility by adopting a self-adaptive transparency algorithm. According to the method, the video stream and the interaction data stream are collected in real time, dynamic annotation of the scene content of the AR glasses is achieved, live broadcast scene changes and audience interaction requirements can be responded in time, and the real-time performance and accuracy of live broadcast annotation are improved.

The invention discloses a semantic auxiliary vision SLAM loopback detection method based on topological graph matching, and belongs to the field of computer vision. The implementation method comprises the following steps of: screening out candidate key frames without a common-view relationship by judging whether the common-view relationship exists between a current frame and each frame in a historical key frame sequence or not; and constructing a semantic topological graph, namely constructing nodes and undirected weighted edges in the topological graph and calculating node descriptors, and realizing topological expression of the key frame image by constructing the semantic topological graph and calculating a feature descriptor for each node in the topological graph. And calculating an affinity matrix, constructing the affinity matrix between the topological graphs by combining the similarity between the weights of the node descriptors and the edges in the topological graph of the current key frame and the topological graph of the candidate key frame, and calculating an incidence matrix between the nodes of the topological graphs at the same time. And calculating the topological graph similarity through the affinity matrix and the incidence matrix. And judging whether loopback detection succeeds or not by judging whether the topological graph similarity reaches a loopback detection threshold value or not.

The invention relates to the technical field of mine environment positioning, in particular to a mine scene self-adaptive repositioning method based on multi-modal perception, which comprises the following steps of: S1, acquiring information, and acquiring visual images, spatial features, wireless signal features and inertial motion data of an internal environment of a mine by using a multi-modal sensor; s2, extracting information, namely extracting the feature data obtained in the step S1, forming a dynamic topological map, and marking temporary obstacle information at the same time; s3, information processing: adaptively adjusting data fusion weights of different sensors according to the information extracted in the step S2 based on a dynamic environment sensing result; and S4, information updating: combining particle filtering and a graph optimization algorithm to realize robust pose estimation and real-time map updating. According to the invention, the failure risk of a single sensor is reduced through multi-mode complementation, and the positioning error is reduced by more than 40%; and through semantic auxiliary matching, the relocation time consumption is reduced.

The invention belongs to the technical field of artificial intelligence and robot autonomous navigation, and discloses an open vocabulary topology semantic graph construction and navigation method and system for an unknown environment. A topological map composed of nodes and edges is constructed in an online incremental mode, the nodes represent key positions in the environment, and the edges represent passable connection between the positions; for each node in the topological map, coding the corresponding visual observation image into a semantic feature vector by using a pre-trained visual language model; performing graph neural network fusion on the semantic feature vectors, executing cross-modal alignment with a natural language instruction, and generating global map representation; and based on the global map representation, generating a navigation action, and driving an intelligent agent to move towards the target. According to the method, an environment representation which deeply fuses spatial topology and semantic information is constructed, and intelligent decision making is carried out according to the environment representation.

The invention discloses an autonomous exploration method and system for a mobile robot in an unknown environment, and the method comprises the steps: firstly scanning the environment in real time to obtain the positioning and point location information of the robot, and updating a 3D grid map represented by a three-dimensional space; secondly, boundary detection and candidate viewpoint sampling are carried out on a map, and viewpoint weights are calculated to screen extension nodes; carrying out path planning by adopting a Hybrid A * algorithm, and maintaining a path into a topological map; path tracking is carried out by adopting a pure path tracking method based on a ray distance, the speed is adjusted in combination with a PID algorithm, and the accuracy and real-time performance of path tracking are improved. According to the method, the completeness of expanded viewpoint selection is improved, repeated exploration of the same area is reduced, the path tracking precision is improved, and the technical problem of autonomous exploration of the robot in a large-scale unknown environment is effectively solved.

The invention provides an AGV scheduling method and related equipment integrating path planning and traffic control, the method comprises the steps of obtaining task information, vehicle dynamic factors and topological map data of an AGV vehicle, the vehicle dynamic factors comprising a cargo state, a storage location state, spinning prohibition and point location prohibition; based on the task information, the vehicle dynamic factors and the topological map data, a depth deterministic strategy gradient network is adopted to optimize the weight, and an improved A star algorithm formed by the sum of basic cost and real-time congestion cost is adopted to plan the driving path of the AGV; continuously monitoring the running state of the AGV cluster in the process that the AGV runs based on the planned path; and conflict prediction is carried out based on the operation state of the AGV cluster, and traffic control is carried out on the AGV cluster by using a preset conflict prevention mechanism and a heuristic deadlock removing strategy. According to the method, vehicle dynamic factors are considered, an A star algorithm is optimized, traffic control is configured, and the AGV scheduling efficiency is improved.