1158 results about "Sensor fusion" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle autonomous obstacle avoidance decision-making method and system based on multi-source sensor fusion, and the method comprises the steps: achieving the time-space synchronization of a laser radar, a visual camera and a millimeter-wave radar through timestamp alignment and coordinate mapping, and constructing a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting the confidence coefficient weight of the sensor in combination with the environment illumination intensity and the barrier surface material; and adopting a reinforcement learning model to generate an incremental obstacle avoidance strategy, and triggering a grading response instruction according to the risk assessment grade. The problem of fusion errors caused by spatial-temporal asynchronization of multi-source sensor data is solved, and the real-time obstacle avoidance success rate of dynamic obstacles is increased.

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

The invention relates to the technical field of software systems, and particularly discloses a vehicle matching and positioning system and method based on comprehensive characteristics of a vehicle and a container, and the system comprises a multi-mode perception and intelligent identification module, a dynamic task matching and scheduling optimization module, an intelligent decision and automatic execution module, and a user interaction and operation module. According to the system, the states of a container and a container truck are sensed in a fusion mode through multiple sensors, cross-modal data feature learning is carried out through a Transform-VIM self-attention mechanism, and high-precision container recognition in a complex environment is achieved; meanwhile, based on multi-dimensional feature matching and a Hungary optimization algorithm, a dynamic task matching mechanism is constructed, and it is ensured that the container trucks and the containers are in accurate butt joint; the system optimizes a scheduling strategy through reinforcement learning, dynamically adjusts an operation process, and is linked with automatic equipment, so that intelligent and efficient port container transportation management is finally realized, the risks of wrong loading, neglected loading and operation delay are effectively reduced, and the overall throughput and operation efficiency of a port are improved.

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention relates to the technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention provides a road monitoring multi-mode sensing method and a road monitoring multi-mode sensing system adapting to a dynamic environment, and belongs to the field of road monitoring. Performing multi-modal weighted fusion to obtain a feature set after weighted fusion; performing classification processing on the fusion features by using a multi-layer perceptron network; generating a comprehensive evaluation result; dynamically adjusting the working priority of each sensor; alignment is carried out in time and space; automatically adjusting a data fusion strategy; and carrying out data cooperative processing by utilizing edge computing and cloud computing to generate a local model generated by the edge computing and a global model and an optimization result generated by the cloud computing. According to the method, the defects of a traditional road monitoring system in the aspects of environmental adaptability, sensor fusion, calculation efficiency and compatibility are overcome, the overall performance and reliability of the system in a complex and changeable environment are remarkably improved, and the method has wide application prospects especially in the fields of automatic driving and intelligent traffic.

The invention belongs to the technical field of artificial intelligence and intelligent sensing, particularly relates to a multi-source sensor fusion sensing system based on adaptive noise suppression, and aims to solve the problems of noise interference, modal mismatch and insufficient robustness in multi-source sensor fusion in a complex dynamic environment. The system comprises a front-end preprocessing module, an adaptive noise suppression engine, a multi-modal feature alignment unit, a credibility-driven fusion reasoning core and a closed-loop feedback optimization mechanism. Through real-time noise modeling and dynamic weight adjustment, high-precision alignment and fusion of multi-source signals are realized, and the sensing stability and real-time performance in an extreme scene are significantly improved.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The present invention relates to a warning system and in particular to a warning system that is movable to a monitoring zone. The invention has been developed primarily for use for early notification of danger by approaching vehicles to a monitored zone such as an emergency in an emergency lane of a roadway. The warning system of the invention comprises at least two forms of detectors for detecting and tracking vehicles and obtaining characteristics of the tracked vehicle relative to the monitored zone a determinator for receiving the characteristics of the tracked vehicle relative to the monitored zone and assessing the expected relative impact of the vehicle on the monitored zone and an alarm system for providing an alarm action according to a predetermined danger of the assessed expected relative impact of the vehicle to the monitored zone.

The invention discloses a multi-sensor fusion heat pump full life cycle AI maintenance early warning system, and relates to the technical field of new energy utilization, and the early warning system comprises a data collection module which obtains operation parameters in a heat pump full life cycle based on a sensor array, and constructs a data set after preprocessing the parameters; the operation parameters comprise temperature, pressure, flow and micro vibration; the data fusion module is used for extracting trend correlation characteristics and parameter coupling characteristics from temperature, pressure and flow parameters by adopting a dynamic sliding window adaptive to a working condition, and preserving core nonlinear information through KPCA dimension reduction; the micro-vibration signal extraction comprises frequency domain and time domain features. According to the method, features are extracted through a working condition adaptive dynamic sliding window, then through cross-space mapping and a life cycle-working condition double-attention mechanism, the analysis and early warning module depends on a core feature mapping library and a two-dimensional dynamic baseline, through instantaneous and accumulated deviation double judgment, abnormal accurate recognition and stage division are achieved, and early warning perspectiveness is high.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support enhanced sensor fusion techniques. In a first aspect, a method of includes receiving point cloud data for two or more frames from a radar device and generating scene flow parameter data based on the point cloud data. The method also includes generating voxel position adjustment data based on the scene flow parameter data, and generating feature concatenation information associated with two or more sensors based on the voxel position adjustment data and feature information associated with the two or more sensors. The method further includes performing feature detection and tracking based on the feature concatenation information to generate tracking information for one or more objects, and outputting the tracking information. Other aspects and features are also claimed and described.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

The invention discloses a centimeter-level positioning and control system for a low-altitude unmanned aerial vehicle, relates to the technical field of high-precision positioning, and solves the problems that firstly, centimeter-level positioning precision is difficult to realize; secondly, a safe flight path is difficult to generate and adjust in a complex environment; thirdly, it is difficult to optimize the global optimal path by comprehensively considering multiple factors such as energy consumption, obstacles and geo-fences; and finally, the technical problem that it is difficult to generate a control instruction and execute the control instruction under centimeter-level positioning in combination with the obstacle avoidance sensitivity, the hovering stability coefficient and real-time environment perception is solved. According to the invention, centimeter-level real-time positioning is realized through multi-source sensor fusion and Kalman filtering; constructing a geofence and a three-dimensional obstacle avoidance path, and combining obstacle detection to prevent boundary-crossing collision; path planning is optimized by utilizing machine learning and risk assessment, so that the unmanned aerial vehicle dynamically adapts to a complex environment; and optimizing a low-altitude flight control strategy through positioning error compensation and control strategy optimization.

The invention belongs to the technical field of ocean detection, belongs to a multi-sensor fusion intelligent anti-collision method and system, comprises a sensing layer, a processing layer and an application layer, and provides an intelligent anti-collision and evidence recording ocean monitoring floating system integrating computer vision, target ranging, satellite positioning and ship automatic recognition system multi-sensor fusion. According to the invention, YOLOv8 target detection, Transform data fusion and a Kalman filtering algorithm are adopted, so that accurate detection and anti-collision early warning of ships and floating objects on the sea are realized. The system has an AIS failure processing mechanism and an evidence encryption storage function, and ensures reliable operation and data compliance under complex sea conditions.

The invention provides an urban building group carbon emission real-time monitoring and visualization system and method, and the system comprises a carbon emission data collection module which integrates multi-source data and carries out the denoising through a multi-mode perception sensor; the carbon emission calculation module is used for constructing a dynamic factor library, determining a fixed weight and accurately calculating carbon emission; the data transmission and storage module adopts a distributed account book and fog computing to ensure that the data is safe and efficient; and the carbon emission analysis module analyzes and predicts the carbon emission data by using a deep reinforcement learning algorithm based on transfer learning. The visual display module is used for forming a visual platform based on a digital twinning technology; and the early warning and decision support module is used for setting a dynamic carbon emission threshold value based on risk assessment and providing personalized emission reduction suggestions and decision support. According to the method, the accuracy of carbon emission calculation can be improved, multi-dimensional visual display is provided, and a user can conveniently analyze data and make decisions from different angles.

The invention provides an intelligent tool warehouse management system, which belongs to the field of tool management and comprises a multi-mode sensing module, an intelligent access control module, an edge calculation module, an anomaly analysis module and a visualization module. The multi-mode sensing module realizes identity recognition, real-time positioning and integrity verification of the tool through RFID and UWB dual-mode positioning, visual recognition and sensor fusion technologies; the intelligent access control module is based on dynamic authority control and gravity sensing goods shelves, and tool storing and taking compliance is ensured; the edge computing module adopts localized data processing and low-power-consumption communication, and supports network disconnection disaster recovery; the abnormity analysis module identifies violation operation through an LSTM model and triggers grading alarm; the visualization module displays the tool state and the warehouse thermodynamic diagram in real time based on the digital twinning technology. The problems that traditional warehouse management depends on manpower, efficiency is low and errors are prone to occurring are solved, and high-precision tracking, intelligent early warning and optimal scheduling of the tool in the whole life cycle are achieved.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the field of industrial automation, and particularly provides a semiconductor photoetching system based on an intelligent industrial robot. And a multi-sensor fusion and real-time compensation algorithm is adopted, so that photoetching positioning errors are accurately sensed and corrected, the positioning precision is greatly improved, and the stable performance of the chip is ensured. According to a multi-physical field monitoring and self-adaptive control algorithm, process parameters are dynamically regulated and controlled according to a photoetching environment and a photoresist state, pattern transfer is optimized, pattern defects are avoided, and photoetching pattern quality is improved. According to the multi-modal data fusion and deep learning algorithm, photoresist coating parameters are automatically adjusted according to the surface characteristics of the wafer, uniform coating is realized, and the stability of the photoetching process is enhanced. The reinforcement learning algorithm assists the multiple robots in efficient collaboration, optimizes task allocation, improves the overall photoetching efficiency and the resource utilization rate, and promotes the progress of the semiconductor photoetching technology in all directions.

The invention discloses a denial environment-oriented self-adaptive anti-interference unmanned aerial vehicle distribution system and a denial environment-oriented self-adaptive anti-interference unmanned aerial vehicle distribution method. The system integrates a navigation module, a communication module, a path planning module and a safety redundancy module, high-precision positioning is achieved through multi-source sensor fusion, and the stability of a communication link is ensured by combining frequency hopping spread spectrum communication, laser communication and an ad hoc network technology. The path planning module dynamically generates an obstacle avoidance path by using a three-dimensional threat map and a deep reinforcement learning algorithm, and switches to an offline mode when communication is interrupted. A safety redundancy mechanism guarantees the fault tolerance of the system through dual-system hot backup, an emergency parachute and a data self-destruction function. According to the invention, the anti-interference capability and the distribution efficiency of the unmanned aerial vehicle in a complex denial environment are effectively improved, and a solution is provided for the practical application of the unmanned aerial vehicle distribution system.

The invention relates to the field of continuum robot shape reconstruction, in particular to a continuum robot shape sensing method based on IMU and PVDF sensor fusion. The method comprises the following steps: acquiring node attitude information of the continuum robot based on measurement data of an inertial sensor; acquiring node pressure information of the continuum robot through a piezoelectric film sensor, and inputting the node pressure information into a pre-trained convolutional neural network model to obtain node curvature information; fitting a continuous curvature function of the continuum robot based on the node curvature information to obtain continuous curvature information; and the shape information of the continuum robot is obtained by using the node attitude information and the continuous curvature information in combination with the Cosseerat Rod theory. According to the shape sensing method, the PVDF sensor and the inertial sensor work cooperatively, and the machine learning model and the Cosseerat Rod theory are combined, so that high-precision and low-error shape sensing of the continuum robot in a dynamic complex environment is realized.