9003results about "Navigational calculation instruments" patented technology

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention discloses an unmanned aerial vehicle positioning system and method based on multi-source position signal fusion, and relates to the technical field of unmanned aerial vehicle positioning, and the system comprises a plurality of positioning data collection units which are carried on an unmanned aerial vehicle, and each type is specially used for collecting positioning data of a single source; the positioning data processing unit is suitable for calculating a confidence coefficient value based on the attribute parameter of each positioning data, and determining a weight of each positioning data according to the confidence coefficient value; and the positioning data fusion unit is suitable for fusing all the positioning data by applying a fusion algorithm and combining the weights of the positioning data to generate fused positioning data. According to the system, the confidence of each source positioning data is evaluated in real time, and the fusion weight is dynamically optimized according to the confidence, so that the dynamic evaluation and optimal fusion of the multi-source positioning data are realized, and the positioning precision and reliability of the unmanned aerial vehicle in a complex environment are improved.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention relates to the technical field of unmanned aerial vehicle cluster control, and particularly discloses an intelligent cooperative flight path planning method and system for an unmanned aerial vehicle cluster system, and the method comprises the steps: firstly constructing a dynamic environment perception model, collecting data through all unmanned aerial vehicle sensors, and carrying out the preprocessing, attention feature extraction and federal learning fusion, and generating a global environment situation map; planning and screening a candidate track set by adopting a particle swarm-genetic hybrid optimization algorithm for adaptive weight adjustment on the basis of the image; a global optimal track consensus is achieved through an improved consensus algorithm and conflict resolution through a distributed collaborative negotiation mechanism and no human-computer interaction evaluation indexes; and finally, monitoring the environment in real time during execution, triggering dynamic re-planning when the environment is abnormal, and ensuring track adaptation through multi-level threshold and incremental planning. According to the method, the unmanned aerial vehicle cluster can quickly respond to the environment change and adjust the flight path, so that the task execution efficiency and success rate of the unmanned aerial vehicle cluster in the complex dynamic environment are improved.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The present invention relates to a solar azimuth estimation method and system based on multi-channel feature enhancement and region-aware attention, belonging to the technical field of intelligent navigation for low-altitude economy unmanned systems. Aiming at the problem of decreased accuracy in solar azimuth estimation based on polarization images under complex cloud cover conditions, the present invention proposes a deep learning framework integrating multi-channel features and direction-aware attention. First, based on polarization light field information acquired by a division-of-focal-plane polarization camera, a three-channel composite input feature composed of a polarization intensity map, an adaptive threshold gradient map, and high-frequency residual edge information is constructed. Second, a ResNet backbone network embedded with a squeeze-and-excitation mechanism is adopted, and a direction-aware polarization attention module is introduced to achieve adaptive fusion of multi-scale features through luminance guidance, deep feature enhancement, and a gradient edge branch.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention discloses an unmanned aerial vehicle path planning method based on multi-objective optimization and improved particle swarm optimization. The method comprises the steps of 1, constructing a three-dimensional space map model; 2, introducing a multi-objective optimization strategy, and designing an objective function by adopting a weighted objective optimization method for evaluating the advantages and disadvantages of each path; 3, initializing particles by adopting an improved RRT algorithm in combination with a Sobol low-difference sequence, calculating a fitness value of each unmanned aerial vehicle path, and recording an optimal solution; 4, introducing a dynamic inertia weight adjustment strategy, and dynamically adjusting the inertia weight according to the number of iterations; a dive search mechanism in an eagle search algorithm is fused, and a particle restart mechanism is introduced to avoid falling into local optimum; 5, judging whether the set number of iterations is reached or not; if yes, iteration is stopped, and the optimal route of the unmanned aerial vehicle is returned to the environment model; if not, iteration is continued, and the optimal air route is searched. The invention aims to improve the path planning efficiency and robustness of the unmanned aerial vehicle in a complex environment.

The invention relates to a steering cost-fused four-way shuttle vehicle dynamic path planning method, which comprises the following steps of: obtaining storage system static information, four-way shuttle vehicle running state information, storage dynamic environment information and task information, and establishing a storage system grid map; constructing a steering multi-dimensional cost quantification model, introducing the steering multi-dimensional cost quantification model on the basis of a traditional heuristic A * algorithm, and constructing a multi-target improved heuristic A * algorithm cost function fusing distance, steering cost and energy consumption cost; based on an improved heuristic A * algorithm, a hierarchical planning logic combining global rough planning and local fine planning is adopted, and a four-way shuttle vehicle initial path set is generated; conflict detection is carried out on the initial path set, a multi-vehicle steering and collision avoidance cooperation strategy with the minimum steering cost increment is adopted, the multi-vehicle cross conflict is solved, and the path set is updated. The method has the effect of improving the operation efficiency of the warehousing system.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention discloses an ocean multi-mode environment perception and intelligent ship navigation decision-making method based on a double-branch vision-semantic encoder. The method comprises the following steps: S1, acquiring a multi-source data image containing a ship and a surrounding environment thereof from an existing public maritime data set or platform; s2, training a double-branch vision-semantic encoder by using the multi-source data image, and inputting a to-be-processed image extracted in real time into a multi-modal feature matrix in the trained double-branch vision-semantic encoder; s3, based on the multi-modal feature matrix, obtaining positioning information of the ship and surrounding environment elements, and constructing a dynamic security domain model; and S4, in combination with the dynamic security domain model and the multi-ship relative position relationship, carrying out quantitative evaluation on the navigation risk, and generating a self-adaptive navigation strategy based on an evaluation result. According to the invention, high-precision ship positioning and environment element identification under complex weather and illumination conditions are realized by using all-weather characteristics and multi-scale visual feature coding of SAR imaging.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, and discloses an unmanned aerial vehicle indoor and outdoor seamless navigation method fusing Beidou and visual positioning. Comprising the following steps: S1, synchronously acquiring original observation data of a Beidou satellite navigation system of an unmanned aerial vehicle, an image sequence acquired by a visual sensor and inertial data of an inertial measurement unit; s2, processing the original observation data of the Beidou satellite navigation system to obtain the position of the unmanned aerial vehicle, according to the unmanned aerial vehicle indoor and outdoor seamless navigation method and system fusing Beidou and visual positioning, through a self-adaptive fusion filter module, the fusion weight of the unmanned aerial vehicle and visual positioning is dynamically adjusted according to the Beidou signal quality; smooth transition of indoor and outdoor navigation main sources is achieved, pose jump is effectively avoided, a tight coupling depth fusion algorithm is adopted, the precision and robustness of the system are improved, the continuous and stable flight capacity of the unmanned aerial vehicle in complex indoor and outdoor environments is ensured, and the application scene is expanded.

The invention relates to the technical field of robot navigation, and discloses a safety inspection robot multi-mode navigation system fused with subconscious learning. The system obtains a visual image, a depth distance, an inertial measurement unit and other multi-dimensional perception data of an inspection environment, performs environment feature extraction to generate an environment semantic topological graph, and constructs a dynamic navigation cost map and a preliminary navigation path according to the environment semantic topological graph. During path execution, the system detects abnormal navigation behaviors in real time, extracts the features of the abnormal navigation behaviors and obtains a first correction coefficient. And the system judges whether the implicit environment semantic identifier is identified, and if the implicit environment semantic identifier is identified, the corresponding path key node is taken as a semantic navigation reference point, a final navigation path is generated through fusion, and a time stamp is recorded. And if the time stamp exceeds the preset tolerance, extracting a corresponding second correction coefficient. And the system combines the basic navigation performance evaluation value, the first correction coefficient and the second correction coefficient to output a multi-modal navigation decision result.

The invention discloses an unmanned aerial vehicle camera attitude estimation optimization method and device, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: acquiring data by using an unmanned aerial vehicle carrying various sensors, preprocessing, carrying out feature extraction and matching on preprocessed image data, removing mismatching points, and based on an initial point cloud, carrying out sparse reconstruction in an incremental expansion and bundle adjustment optimization stage; integrating the re-projection error term, the flight path constraint, the epipolar geometric constraint and the triangulation constraint into a target function; constructing a model for predicting the pose correction based on the deep residual convolutional neural network, and taking the initial pose of the camera and the corresponding local image as input; and adjusting the weight of each constraint in the target function based on the predicted pose correction and pose confidence, and minimizing the target function to improve the precision of unmanned aerial vehicle camera pose estimation. The problem of reconstruction scene deviation caused by inaccurate camera attitude estimation in the prior art is solved.

The invention discloses a cable trench inspection robot path planning method and device and a medium, a cable trench three-dimensional semantic map is constructed through multi-sensor fusion, and a laser radar and a depth camera are combined to accurately identify the spatial distribution of a cable support, a suspension cable and an obstacle. An improved directional path search algorithm is adopted, firewall passing sequential logic and lifting platform kinematics constraints are integrated, and a multi-mode inspection path is generated. The environment change is sensed in real time in the inspection process, the path is adjusted online through a dynamic path optimization engine, a planning strategy is iteratively optimized based on historical data, a digital twin system is introduced to realize path pre-verification, and the firewall interaction efficiency and the exception handling capacity are optimized by adopting reinforcement learning. According to the invention, the technical problems of poor real-time performance of path planning and low reliability of facility interaction in a complex cable trench environment are solved, and the inspection efficiency and safety are significantly improved.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention relates to the technical field of intelligent navigation, in particular to a navigation method, system and equipment based on a multi-modal model, a medium and a product. According to the method, feature extraction and alignment fusion processing are performed on multi-modal data acquired by a robot through a preset multi-modal model to generate unified features, and the task complexity of a target navigation task and the environment type of a target navigation environment are determined according to the unified features, so that the weights of a semantic map and a geometric map are adjusted; and constructing a comprehensive map according to the current observation value, determining a target action sequence according to the current observation value and the target observation value corresponding to the comprehensive map, and controlling the robot to execute the corresponding navigation action so as to improve the navigation efficiency and the navigation accuracy.

The invention belongs to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle exploration navigation method based on a sequence enhancement improved SAC algorithm, and the method comprises the following steps: obtaining multi-modal perception data, and enabling the multi-modal perception data to form a standardized state vector at each time step; forming a state sequence by the state vector of each time step, inputting the state sequence into an improved Transform network, and carrying out feature extraction to obtain a high-dimensional feature vector; inputting the high-dimensional feature vector into a teacher-student improved SAC network, and outputting an original action by a student network; and judging whether the distance of the nearest obstacle is smaller than a threshold value or not, if so, performing VO safety shield correction on the original action to obtain a safety action, projecting / cutting the safety action to a dynamic feasible set, and issuing and executing the safety action. According to the invention, safe and efficient autonomous exploration and flight of the unmanned aerial vehicle in an unknown environment can be realized.

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention relates to the technical field of data processing, and discloses a VICTS antenna attitude estimation and correction method and system based on satellite pointing feedback, and the method comprises the steps: constructing a tight coupling depth fusion system with extended Kalman filtering as a frame, introducing satellite communication antenna pointing information as a new observation source on the basis of traditional IMU / GNSS fusion, and carrying out the satellite communication antenna attitude estimation and correction. By deducing the Jacobian matrix of the carrier attitude, effective fusion of the observed quantity is realized, so that the attitude estimation precision and robustness of the carrier are reversely corrected and remarkably improved, the three-axis attitude error is estimated and corrected more comprehensively and steadily, and the problem of lack of roll angle observation is fundamentally solved. Observation information of different dimensions is unified into the same state estimation framework through accurate mathematical modeling, and the precision, autonomy and robustness in a complex environment of the system are greatly improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

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.