1916 results about "Path generation" patented technology

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

Provided is a method of generating a personalized learning path based on multi-course knowledge graph merging and a system thereof, in particular to the technical field of personalized learning path generation. The method includes: constructing a personalized knowledge base to obtain a personalized knowledge graph of all courses that students need to learn in the current academic year; using an entity alignment technology to realize the personalized knowledge graph merging of all courses, and automatically generating a personalized knowledge graph of multi-course merging; generating a subsequent learning path according to a current learning progress of users and the personalized knowledge graph of multi-course merging; and evaluating the generated learning path in terms of link prediction accuracy.

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

The invention provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention relates to the technical field of building reconnaissance, in particular to a high-precision reconnaissance lofting intelligent positioning system and method based on multi-source data fusion, and the system comprises a data collection module, a fusion processing module, an intelligent positioning calculation module, a lofting path generation module, a wireless communication module and a visual terminal module. The data acquisition module synchronously acquires multi-source data; the data fusion processing module carries out space-time alignment and feature level fusion and detects the state of the sensor; the intelligent positioning resolving module outputs three-dimensional coordinates by adopting various algorithms; the lofting path generation module generates an optimized path according to the BIM model; the wireless communication module realizes high-speed data transmission and equipment communication; the visual terminal module provides a visual interaction interface. Multi-source data fusion is utilized, the positioning precision is improved, the reliability of the system is guaranteed through sensor failure detection, the system adapts to the complex environment, the lofting path is optimized, the overall performance of the system is enhanced, and an efficient and accurate solution is provided for building investigation lofting.

The invention discloses a BIM model feature extraction-based unmanned aerial vehicle intelligent inspection path generation method for the interior of a building. The method comprises the steps of extracting a spatial geometric feature set, a spatial topological feature set and a semantic feature set from original BIM model data; constructing a basic space graph representing a space structure and a semantic enhancement graph representing a function relationship based on the feature set, and generating a multi-level semantic graph; task targets and dependency relationships are extracted from the inspection task description and the multi-level semantic graph, and a task weight matrix is generated; generating an optimized path set based on the multi-level semantic graph and the task weight matrix; performing multi-dimensional scoring and real-time optimization on the optimized path set to generate an emergency adjustment strategy; and finally verifying the inspection path. According to the method, through multi-level feature extraction and multi-level semantic graph construction, accurate understanding of the building space is realized, and the intelligent level and reliability of routing inspection path planning are improved.

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention relates to the technical field of intelligent agent interpretable retrieval path generation, in particular to an intelligent agent interpretable retrieval path generation system and a verification method. The method comprises the following steps: acquiring agent associated knowledge retrieval data through associated knowledge retrieval, and constructing a node multi-path initial structure based on the data; determining a path node causal reasoning chain and node semantic interpretation information, and generating structured interpretable path data; detecting explainable path deviation by using the structured path data, implementing path structure simulation correction based on a deviation identification result to obtain path structure simulation correction data, and combining the correction data; carrying out interpretable retrieval path strategy optimization to generate optimized path retrieval strategy data, and carrying out intelligent agent interpretable retrieval path updating processing on the structured path data to obtain an updated intelligent agent interpretable retrieval path; retrieval path generation can be explained through the intelligent agent, so that path retrieval is more accurate.

The invention discloses a cleaning path planning method and a cleaning device, and belongs to the technical field of automatic control, and the method comprises the steps: collecting the point cloud data of a to-be-cleaned region, constructing an initial composite map, setting a perception marking method, dividing the point cloud data into sub-regions, and marking a sudden change region after the sub-regions are matched with historical map features. Locally updating the composite map by using map optimization; constructing a safety buffer zone at the edge of the to-be-cleaned area, setting a buffer adjustment method, performing trajectory prediction on the abrupt change area, and dynamically adjusting a safety buffer distance according to a prediction result; calling a basic cleaning path, generating a layered path, setting a planning adjustment method, updating the layered path according to the abrupt change area in the cleaning process, calculating a pollution index of the to-be-cleaned area, constructing a time sequence prediction model, pre-judging a pollution diffusion trend, and performing dynamic adjustment; and after the to-be-cleaned area is cleaned, decision iteration is carried out to adapt to user habits.

The invention relates to the technical field of learning path recommendation, and discloses a learning path intelligent recommendation system based on user behavior big data analysis, and the system comprises a behavior data collection module which is used for collecting behavior data of a learner on a learning platform; the behavior inertia modeling module is used for extracting behavior inertia parameters of the learner based on the behavior data; the task capability graph construction module is used for constructing a task capability graph; calculating a grasp score according to the test score, and extracting a sub-graph map; and the recommendation path generation module is used for generating an optimal learning recommendation path according to the path recommendation engine, and selecting the optimal learning recommendation path as a recommendation result based on the path adaptation degree score. According to the method, personalized, rhythm-adaptive and resource-optimized learning path recommendation is dynamically generated on the basis of user behavior big data analysis in combination with post capability requirements.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention relates to the technical field of path planning, and discloses a carton stacking path planning method and system based on artificial intelligence and a medium. The method comprises the steps that heterogeneous carton images are collected through a multi-angle camera and fused to generate a digital twinborn model; the analysis model predicts mechanical characteristics of the carton; optimizing an interlayer occlusion relation to form a stacking blueprint; planning an avoidance path to generate an execution instruction; monitoring deviation and establishing an abnormal scheme library; and evaluating an optimized path strategy in combination with cargo flow characteristics. According to the method, adaptive stacking strategies are provided for different cargo flow scenes, so that the space utilization rate, the structural stability and the operation efficiency of stacking are improved.

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention discloses an intelligent optimized navigation method based on dynamic people flow and user interest, which comprises the following steps: acquiring people flow density, interest data and time sequence data by a system through a sensor, predicting future people flow density by utilizing an ARIMA model, and marking a congestion risk area. And a personalized optimal visiting path is generated through a Dijkstra algorithm in combination with user interest portraits and venue item characteristics. In the path generation process, the system comprehensively evaluates the project coverage rate, the time limit and the congestion avoidance condition of the path to ensure that the recommended path can meet the user demand to the maximum extent, and dynamically adjusts the path planning weight, shunts crowds and avoids congestion. And if the actual visiting path of the user deviates or the real-time density in the venue changes, the system can quickly update the path and recommend the path again, so that the efficient and safe visiting experience is ensured. Through the method, the stadium can effectively optimize people flow management, improve the experience of visitors, and provide scientific support and decision basis for operation management.

The invention discloses an unmanned forklift dynamic task scheduling method and system based on deep reinforcement learning of a cold chain warehouse, and belongs to the field of unmanned forklift scheduling. The method comprises the steps that a sensor is arranged in a target cold chain warehouse, environment data of the target cold chain warehouse is collected, and the environment data of the target cold chain warehouse is acquired; constructing a digital twinborn model of the target cold chain warehouse at the cloud based on a digital twinborn technology; a dynamic environment state of the target cold chain warehouse is constructed according to data fed back by the sensor, an initial unmanned forklift track is generated based on a path generation algorithm, the initial unmanned forklift track is optimized by using a multi-feature coupled space-time joint path planning algorithm deployed at a cloud end, and an optimal path and scheduling plan are generated; and the cloud end issues the instruction to the edge gateway, and the edge gateway decomposes the instruction and issues the decomposed instruction to each unmanned forklift. According to the invention, the optimization effect of the scheduling scheme is improved.

The invention relates to the related technical field of data governance analysis, in particular to an intelligent data governance system based on behavior path analysis, and the system comprises a path generation module which generates a data flow path through a graph neural network according to a data behavior log, and draws up a behavior path graph; the first risk propagation path set determination module and the second risk propagation path set determination module respectively use graph diffusion and characteristic parameter marks to determine risk paths so as to operate a blocking mechanism to carry out data access management. The technical problems that data access depends on single rule matching and single-point anomaly detection, global correlation analysis on data operation behaviors is lacked, and complex attacks cannot be effectively detected are solved, the behavior path map is generated through the graph neural network, the influence range of abnormal operation is quantified by using the graph diffusion model, and the data access efficiency is improved. The technical effects of predicting the potential path of sensitive data leakage in advance, dynamically adjusting the confidence, improving the risk node identification accuracy, carrying out real-time blocking treatment and guaranteeing the data access security are achieved.

The invention provides an unmanned aerial vehicle flight path planning method based on air-space linkage, and the method comprises the steps: carrying out the multi-time-domain air-space data fusion, double-layer asynchronous iterative planning, multi-constraint joint optimization, distributed computing resource scheduling, task perception path generation, and other innovative technologies; the problems of limited environment perception capability, high calculation complexity, low resource allocation efficiency, insufficient real-time track adjustment capability and the like in the prior art are solved, and the flight performance and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention relates to the technical field of logistics supply, and discloses a multi-source data fusion-based e-commerce supply chain anti-break decision-making system, which comprises a heterogeneous graph network construction and risk prediction module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module and a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a time sequence sensitive graph attention network is used to predict a goods shortage risk propagation path; the elastic recovery path generation module is used for modeling supply chain recovery into a sequence decision problem on a graph based on the constructed heterogeneous graph network, and generating a plurality of elastic recovery paths through a variational inference method; according to the method, the heterogeneous graph network is constructed and the time-sequence-sensitive graph attention network is realized, so that the system can accurately capture the cascade effect and the risk propagation path in the complex supply network, the prediction accuracy of the cargo shortage risk propagation path is improved, and false report and missing report are greatly reduced.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention discloses a fault diagnosis method and device for a server, and relates to the technical field of electric digital data processing, and the method comprises the steps: collecting the actual operation data of a target server, obtaining the configuration information and historical fault data, and detecting whether the target server breaks down or not; when a fault occurs, inputting the configuration information, the actual operation data and the historical fault data into a pre-constructed fault path generation model so as to output a corresponding diagnosis path; and determining a diagnosis rule, a diagnosis label and a fault type when the target server is diagnosed according to the diagnosis path to generate a current fault diagnosis result, thereby solving the technical problems that the flow is solidified and single, the dynamic adjustment cannot be realized, the flexibility and pertinence are lacked, the diagnosis efficiency is low and the diagnosis precision is poor, and achieving the purposes of shortening the diagnosis time and improving the diagnosis efficiency. The server energy consumption and maintenance cost are reduced, the fault positioning accuracy is improved, the fault solving time is shortened, and the technical effect of automatic fault diagnosis is achieved.

The invention relates to the technical field of online education path optimization, and discloses an online education learning path optimization system based on big data and intelligent analysis, and the system comprises a data collection module which obtains basic data of a user; the user portrait generation module is used for generating a user portrait; the path generation module is used for generating an initial path candidate set according to the user portrait and screening the initial path candidate set to obtain a first learning path; the learning effect analysis module is used for obtaining a learning effect analysis result according to the historical learning performance data, the first learning data and a preset learning obstacle evaluation model; and the optimization module judges whether an obstacle exists or not, if yes, a plurality of nodes are added / reduced in the first learning path to obtain an optimized learning path, and otherwise, the first learning path is kept unchanged. The first learning path is adjusted according to the user portrait and the learning effect analysis result, personalized optimization is performed by integrating the learning effect and the learning habit, and improvement of the learning efficiency is facilitated.

The invention relates to a cutting path generation system based on visual positioning, cutting equipment and a cutting method. The system comprises a scanning camera control module, an image acquisition and recognition module, an image processing module, a cutting path planning module and a cutting control module. According to the system, by combining a Laplace algorithm and an LBP and GLCM texture extraction technology, the boundary recognition problem under the condition that a pattern and a background are in the same color or low contrast is solved; the path smoothness and the cutting quality are improved by adopting contour optimization and curvature compensation algorithms, the cutting sequence is optimized by combining weighting functions of the area, complexity and overlapping degree, and material deformation is reduced. And motor driving is dynamically adjusted through PID control, and stable control over the speed of the tool bit is achieved. The method has the advantages of being high in recognition precision, high in adaptability, intelligent in path planning and stable in cutting process, the automatic cutting efficiency and the finished product quality of complex patterns or same-color materials are remarkably improved, and the overall cutting precision fluctuation is controlled within + / -2%.

The invention provides an unmanned aerial vehicle autonomous inspection method and system for a transformer substation. The core content of the unmanned aerial vehicle autonomous inspection method comprises seven steps of task pre-planning, flight path generation, BIM model generation, edge point extraction, inspection path generation, inspection point planning and inspection task execution. The steps and links are combined with various advanced technologies such as LiDAR scanning, SLAM technology, reinforcement learning, genetic algorithm, ant colony algorithm and edge detection, efficient, intelligent and safe inspection of the transformer substation is realized, and powerful support is provided for operation and maintenance management of the transformer substation. Wherein the obstacle avoidance path is autonomously planned through the reinforcement learning dynamic training obstacle avoidance model, dynamic obstacle avoidance and path optimization are realized, and the flight risk is reduced. And autonomous flight and task execution in a complex environment are supported. And real-time mapping and positioning of the unmanned aerial vehicle are realized by using the SLAM technology, and the preprocessing dependence is reduced.

The invention discloses an improved hybrid A star path planning method for an automatic driving vehicle, and relates to the technical field of automatic driving, laser radar sensing and unmanned vehicle navigation. According to the method, on the basis of inheriting the advantages of high global search efficiency, strong kinematics feasibility and the like of a hybrid A star algorithm, a multi-factor cost function is designed, a trajectory smooth constraint is introduced, and the feasibility and execution quality of a path are improved from two aspects of cost evaluation and path construction. In order to further enhance the smoothness and the dynamic controllability of the local path, the initial path is optimized and adjusted by fusing a time elastic band (TEB) algorithm in the method, so that the continuity and the stability of the path in a complex dynamic environment are improved. The method is suitable for a high-precision path planning task in an automatic driving system, and the comprehensive requirements of a path generation result in the aspects of reliability and environmental adaptability can be well met.

The invention discloses a mechanical arm path planning method based on Transform and a diffusion model, and belongs to the technical field of robots, and the method comprises the following steps: collecting environment information to generate a reference path, and constructing a training data set; a conditional diffusion Transform prediction network is constructed, features are extracted, and multi-modal path prediction is realized; gaussian noise is applied to the reference path, and denoising training is carried out on the conditional diffusion Transform prediction network; in combination with a diffusion model and a cost guidance mechanism, optimizing a noise path sampled from Gaussian distribution until a smooth collision-free path is generated; the candidate paths are evaluated, and a mechanical arm joint control instruction is generated; and the mechanical arm executes the planning track and carries out real-time sensing and online re-planning. According to the method, environment perception, Transform coding and diffusion generation are organically combined, a plurality of feasible tracks which are smooth and capable of avoiding obstacles are rapidly generated in a complex obstacle scene, and the method has good generalization ability and can adapt to different scenes and dimension changes.

The invention discloses an underwater robot submarine cable inspection method and system based on two-dimensional image sonar, and relates to the field of submarine cable inspection. The existing submarine cable inspection method has the problems of insufficient detection precision, lack of autonomy, dynamic obstacle avoidance defect and the like. The method comprises the steps of submarine cable feature recognition model construction, underwater robot sensing system configuration, image processing, path planning, obstacle avoidance path generation and detouring. According to the technical scheme, the underwater robot carries the electromagnetic induction sensor, the two-dimensional image sonar and the ultra-short baseline positioning device, and an image processing technology and a path planning algorithm are combined, so that efficient inspection of the submarine cable is realized; when the robot encounters an obstacle, the robot can generate an obstacle point cloud through image processing in combination with prior obstacle information, and performs obstacle avoidance in combination with a local path planning algorithm, so that safe and smooth proceeding of an inspection task is ensured; the detection precision is effectively improved, automatic and efficient inspection is realized, and a powerful guarantee is provided for long-term stable operation of the submarine cable.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.