282 results about "Visual navigation" patented technology

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention provides a target perception and avoidance decision-making method in visual navigation of intelligent equipment, and belongs to the technical field of intelligent decision-making. The method comprises the following steps: constructing a space-time diagram neural network, introducing semantic tag codes to define nodes and edges of the space-time diagram neural network, and predicting the trajectory of each obstacle through a regression network; an environment semantic label is defined, the environment semantic label and the predicted obstacle trajectory are coupled, and a semantic enhanced risk field is generated; and constructing a semantic road network map based on the risk field of semantic enhancement, generating a global path point sequence, and calling a dynamic step RRT * algorithm to avoid obstacles by taking the current global path point as a temporary target. According to the method, the risks of different areas can be evaluated more accurately, the risk value is adjusted according to the type and the position of the obstacle, the safety and the reliability in a complex environment are improved, and decision errors caused by environment changes are reduced.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention discloses a target aircraft flight trajectory correction method based on multi-source fusion navigation, and relates to the technical field of trajectory correction. According to the method, multi-source navigation information such as AI visual navigation, inertial measurement and barometric altitude can be fully fused, short-time disturbance and long-time deviation are effectively distinguished through conjoint analysis of short-time envelope and trend envelope, and the sensitivity and reliability of trajectory anomaly detection are remarkably improved; by setting a direction consistency screening and grading weight suppression mechanism, misjudgment caused by air pressure fluctuation or instantaneous noise in low-altitude flight can be suppressed, so that the false alarm rate is reduced; the method has the advantages that the method is simple and easy to implement, an envelope difference accumulated evidence and a threshold linkage triggering mechanism are utilized, early warning of trajectory deviation is achieved, the timeliness of abnormal response in a flight task is improved, stable and accurate trajectory correction can be dynamically generated in a changeable flight environment, and robustness and convergence of the correction process are guaranteed.

The invention discloses a robot arm control method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a pre-training dynamic model and a real visual depth map of a real robot arm visual angle, and the pre-training dynamic model is used for completing a specified task; determining a joint control value according to the real vision depth map and a pre-training kinetic model; hybrid action control is carried out based on the joint control value, and remote target navigation is carried out on the response action of the real robot arm through a visual navigation model; and the controlled simulation target image and the actual image are obtained, feature matching and closed-loop estimation are carried out based on the simulation target image and the actual image, and pose error compensation is carried out on the action of the real robot arm. The motion is observed and deduced through a real visual depth map, then real and simulated mixed motion control is carried out to reduce a visual and dynamic gap, and finally pose error compensation is carried out. And the pose error of the arm is reduced, and a start pose guarantee is provided for downstream control.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The present discloses a lightweight attention mechanism distance estimation method for assisting visual navigation of a vehicle at a container terminal. Firstly, using a depth monocular camera calibrated with the imaging parameters of the planar checkerboard tool to collect RGB-Depth image pairs in the working scenario of the automatic guided vehicle. Secondly, performing depth completion and manual annotation processing on the collected depth image. Thirdly, inputting image pairs into a lightweight monocular metric depth estimation framework which uses an improved lightweight attention mechanism Squeeze Former as the token mixer for training. Finally, fusing the results of relative depth estimation and absolute depth estimation to obtain a prediction of an actual distance between an object in the RGB image and the camera in the real world. The method and model provided by the present invention feature simple equipment, low cost, high timeliness of prediction and accurate results.

The invention relates to the technical field of visual navigation, in particular to a blind person navigation path planning method combining visual SLAM and semantic segmentation. The method comprises the following steps: acquiring an environment image and pose data; performing front-end tracking by using the pose data, and determining a key frame sequence; generating a depth map based on the environment image, and identifying a passable area according to the depth map; constructing a three-dimensional point cloud map according to the key frame sequence; semantic segmentation is carried out according to the three-dimensional point cloud map, and obstacle type labels are recorded; according to the obstacle type label, carrying out safety level layering on the passable area, and determining a path cost weight; performing global path planning based on the path cost weight, and generating a semantic enhancement navigation trajectory; and driving real-time voice guidance by using the semantic enhancement navigation trajectory, and calculating a path execution deviation corresponding to the real-time voice guidance. According to the method, obstacle types are distinguished through semantic segmentation based on a visual navigation technology, a semantic enhancement path is generated, semantic hierarchical navigation is realized, and the intelligence of path decision is improved.

The invention discloses a multi-module collaborative navigation system for an intelligent robot, which relates to the field of robot navigation and comprises a multi-mode perception decision module, a semantic visual navigation module, a path memory fusion module and a rolling predictive control module which are electrically connected in sequence and work cooperatively. The multi-modal perception decision module performs weighted fusion on heterogeneous information, outputs scene representation and decision, and provides data support; the semantic visual navigation module analyzes the natural language instruction and combines visual data to complete path planning; the path memory fusion module carries out backtracking correction in an abnormal environment of semantic ambiguity and perception mismatch; and the rolling predictive control module realizes trajectory tracking and dynamic obstacle avoidance. According to the system, dependence on a prior map is reduced, instruction performability and dynamic obstacle avoidance capability are improved, collision risks are reduced, navigation real-time performance, stability and robustness are enhanced, and the system is compatible with multiple chassis and is suitable for complex dynamic scenes.

The invention discloses a multi-mode feedback control method and system for a femtosecond laser stone crusher, and relates to the technical field of medical instruments. The method comprises the following steps: constructing spectral component analysis, collecting spectral signals, and executing target chemical component identification; ultrasonic position detection: acquiring an ultrasonic echo signal through a transducer, and calculating a spatial distance; performing vision-assisted positioning, acquiring image data through an endoscope, and generating space image information; according to the spectrum identification result, the distance parameter and the space image information, multi-source data synchronization processing is completed; generating a laser parameter control instruction according to the fusion result; and executing a real-time feedback regulation and control process, obtaining feedback information, and completing control parameter updating and closed-loop response. Through fusion of multi-modal information such as spectrum identification, ultrasonic distance measurement and visual navigation, real-time identification of stone types, intelligent matching of laser parameters and accurate control of spatial positioning are realized, and the accuracy, safety and intuition of lithotripsy operation are remarkably improved.

The invention discloses a transcranial stimulation magnetic therapy coil navigation method, a transcranial stimulation magnetic therapy coil navigation system, a storage medium and equipment. The method comprises the following steps: acquiring three-dimensional structure information of the head of a target object, magnetic resonance imaging (MRI) data of an individual target object, or three-dimensional model data obtained by matching from a general MRI template library; real-time surface geometric information of the head of the target object is collected through an infrared optical tracking system; performing registration fusion on the three-dimensional structure information and the real-time surface geometric information, and generating an individualized three-dimensional brain model synchronized with a real space coordinate system on a navigation interface; the spatial position and posture of the optical marker fixed on the magnetic therapy coil are tracked in real time through an infrared optical tracking system and correspondingly mapped to the individualized three-dimensional brain model, and visual navigation of the magnetic therapy coil relative to the brain anatomical structure is achieved. Through the individualized three-dimensional navigation and real-time visualization technology, the problems that a traditional TMS is low in positioning precision, invisible in operation and poor in treatment repeatability are solved.

The invention discloses an unmanned aerial vehicle high-altitude cleaning path precise planning method and system based on visual navigation, and the method comprises the following steps: constructing an initial three-dimensional grid model of a to-be-cleaned building, dividing cleaning regions according to the initial three-dimensional grid model, and setting priorities; the unmanned aerial vehicle collects visual data of a cleaning area through a multi-mode visual sensor, wherein the visual data comprise surface texture, depth information and a stain distribution thermodynamic diagram; performing local correction on the initial three-dimensional grid model based on the real-time visual data to generate a high-precision dynamic three-dimensional grid model; generating a cleaning path by adopting an adaptive grid traversal algorithm according to the priority of the cleaning area and the coverage range of the unmanned aerial vehicle cleaning head; obstacles in the path are detected in real time through a visual feature matching algorithm, and when the obstacles are detected, path re-planning is carried out to avoid the obstacles until all the areas are cleaned. According to the method, the modeling precision and the dynamic adaptability are improved, and the intelligence and the pertinence of path planning are realized.

The invention discloses a rotor unmanned aerial vehicle visual navigation method and system for an unknown forest region, and the method comprises the steps: firstly, combining a depth camera with a Sobel gradient filtering mechanism, and effectively eliminating interference information; secondly, pixel-level segmentation and three-dimensional positioning of the trunk are realized by segmenting a cascade model of a convolutional network and a residual network, visual information is accurately mapped to a world coordinate system, and the problem of positioning drift caused by similar textures in a forest scene is solved; based on starting point-target point-terminal point minimum jerk trajectory planning, a smooth trajectory is generated through polynomial optimization and quadratic programming, flight jitter is inhibited fundamentally, and the mechanical loss is reduced while the image acquisition quality is guaranteed; and finally, through real-time target point updating and trajectory dynamic correction of the depth camera, a sensing-planning-execution closed loop is formed, so that the unmanned aerial vehicle has dynamic obstacle avoidance and path online optimization capabilities, autonomous crossing is finally realized, and navigation safety and stability in a complex environment are significantly improved.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention is suitable for the technical field of artificial intelligence, and provides a robot visual navigation optimization system based on edge computing, the system comprises a visual sensor module, an edge computing unit, a navigation algorithm module, a communication module and a robot executor, the visual sensor module is used for collecting high-definition images or video streams, and the edge computing unit is used for processing the high-definition images or video streams; the edge calculation unit is responsible for locally executing a visual navigation algorithm, the navigation algorithm module is deployed on the edge calculation unit, and the navigation algorithm module is used for executing a visual odometer, synchronous positioning and map construction, environment perception, obstacle detection, local path planning and motion control; and the communication module is used for carrying out model updating, task scheduling and non-real-time data uploading with a cloud. Complex visual data processing and navigation decision are sunk to the robot body or near-end edge equipment, so that the navigation precision and the response speed of the robot in complex and diversified environments are remarkably improved, and the energy consumption efficiency is optimized.

The invention provides a multi-source remote sensing collaborative recognition method and system for road passing height-limiting obstacles, and relates to the technical field of road height-limiting recognition. Pixel-level elevation statistics is carried out by moving a sliding window in an elevation information block to position M height-limiting obstacles, and then clearance height calculation is carried out to correct and output M reference height-limiting heights to be projected to a passing road network; and according to the vehicle type of the freight vehicle and the real-time load information, height-limited negative compensation is carried out to output a dynamic passing height so as to traverse a passing road network to locate R feasible routes and display the R feasible routes to a vehicle-mounted display screen of the freight vehicle. The technical problems that in the prior art, due to the fact that manual inspection is relied on for height limiting obstacle state monitoring, the height limiting state is not updated in time, and due to the fact that a static height threshold value is adopted for vehicle height limiting passing judgment, large vehicle passing path planning is affected are solved. The technical effects of dynamically tracking the state change of the height-limiting obstacle and the passing height of the vehicle and carrying out precise path planning and visual navigation of the large vehicle are achieved.

The invention relates to a modular bionic climbing robot and a multi-target continuous operation method thereof. The invention relates to a climbing robot simulating inchworm motion, in particular to a modular bionic climbing robot which is suitable for operations such as bamboo forest felling, forest pruning and tower inspection. The robot comprises a posture adjusting module, a self-adaptive clamping module, a replaceable tail end operation module and a visual navigation module. A battery is arranged in the posture adjusting module and supplies power through wiring of the arm body. The robot climbs, descends and transversely rotates along the rod through the pitching joint and the yawing joint. Based on the inchworm motion principle, the two clamping modules work alternately, and continuous transfer operation is achieved. When the distance between every two adjacent bamboo stalks is not larger than the arm span, multi-target continuous felling can be automatically completed, efficiency is remarkably improved, and cost is reduced.

The invention discloses an inter-ridge autonomous navigation method and system for an asparagus picking robot based on machine vision, and belongs to the technical field of agricultural automation. The method comprises the following steps: processing a field image acquired by a robot in real time by using a pre-trained lightweight inter-ridge path segmentation network to generate a path mask; extracting and smoothing left and right boundary points of the path based on the mask; fitting a middle navigation line; and finally, a control error is calculated according to the navigation line and the robot pose, and a steering instruction is generated to drive the robot to autonomously travel. Through lightweight model design and a robust path fitting algorithm, the problems that an existing visual navigation method is large in calculation amount, poor in real-time performance and insufficient in precision are solved, an efficient, accurate and easy-to-deploy autonomous navigation solution is provided for an agricultural robot, and the method is particularly suitable for complex field environments of ridge crops such as asparagus.

The invention discloses a multi-machine collaborative three-dimensional map updating method based on mobile augmented reality, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the following steps: carrying out the collection of initial collection data of a plurality of target regions through the wearing of AR glasses by a plurality of people; performing frame extraction on the original video image, and calculating to obtain a data grouping result; a sparse pairing list is generated by adopting a hybrid pairing strategy, and sparse point cloud data is obtained by combining incremental SfM calculation; performing distortion removal processing on the frame extraction image, and obtaining pure dense point cloud data through a multi-view solid geometry algorithm, a clustering algorithm and statistical filtering; a triangular mesh model is generated through Poisson surface reconstruction, and a high-precision three-dimensional model is obtained through Markov random field optimization view selection; and constructing a unified space-time reference conversion matrix to obtain a real-time AR visual navigation and map updating result. By adopting the method, the problems that the three-dimensional map is poor in current situation, virtual and real registration is misplaced, the modeling efficiency is low and the precision is insufficient are solved.

Navigation instructions are determined using visual data or other sensory information. Individual frames can be extracted from video data, captured from passes through an environment, to generate a sequence of image frames. The frames are processed using a feature extractor to generate frame-specific feature vectors. Image triplets are generated, including a representative image frame (or corresponding feature vector), a similar image frame adjacent in the sequence, and a disparate image frame that is separated by a number of frames in the sequence. The embedding network is trained using the triplets. Image data for a current position and a target destination can then be provided as input to the trained embedding model, which outputs a navigation vector indicating a direction and distance over which the vehicle is to be navigated in the physical environment.

The invention provides a high-precision IMU (inertial measurement unit) and vision combined unmanned aerial vehicle navigation method, an unmanned aerial vehicle is provided with a high-precision IMU resolving module, a vision navigation module, a GNSS (global navigation satellite system) module and a main control processor, and the main control processor executes the following steps: judging whether the precision and signal of the current GNSS module meet navigation requirements or not; if the precision and the signal of the current GNSS module do not meet the navigation requirements, judging whether the visual navigation module works normally or not; and if the visual navigation module cannot work normally, acquiring a latitude estimation value output by the high-precision IMU resolving module, and performing navigation according to the latitude estimation value output by the high-precision IMU resolving module. The latitude can be estimated in real time at least by sensing the earth rotation effect through a high-precision IMU, and then the earth rotation angular rate is compensated during attitude calculation; and the vision / laser radar and the high-precision IMU are combined to realize long-time high-precision positioning and navigation.

The invention discloses a dual-path non-cooperative spacecraft pose estimation method based on uncertainty perception, and belongs to the technical field of spacecraft pose estimation. According to the method, a dual-path prediction framework based on a shared backbone network is constructed and comprises a geometric reasoning path and a global context sensing path. In the training stage, prediction results of the two paths are aligned through a geometric consistency loss function, and joint optimization is carried out by combining multi-task losses such as key point regression, classification, rotation and translation regression and the like. In the inference stage, the pose estimation result is adaptively fused according to the predicted variance output by each path, when the variance is low, the output of the global context sensing path is directly adopted, otherwise, the key point projections of the two paths are combined, and the final pose is recovered. The method has higher estimation precision, robustness and interpretability in complex space environments such as shielding, illumination variation and large-scale variation, and provides reliable visual navigation support for on-orbit autonomous tasks.

The invention relates to a ship-borne unmanned aerial vehicle recycling method and system based on visual navigation and six-degree-of-freedom compensation. The method comprises the following steps: extracting infrared, polarization and fluorescence frequency domain features and fusing to obtain an anti-shielding composite descriptor; constructing a ship dynamics differential model, discretizing the ship dynamics differential model by adopting an explicit multi-step method, and predicting six-degree-of-freedom disturbance data of the ship; a vision and inertia combined cost function is constructed, a vision residual item is calculated, the continuity of the motion track of the unmanned aerial vehicle is restrained, and the optimized real-time pose of the unmanned aerial vehicle is obtained; decomposing the six-degree-of-freedom disturbance data into a body coordinate system compensation amount, setting a backstepping sliding mode control law containing feed-forward compensation, and generating an anti-disturbance trajectory instruction; and air data are collected in real time, the rotor rotating speed is corrected according to the air data, the electromagnetic adsorption force is calculated, and the ship-borne unmanned aerial vehicle is recycled. And the navigation stability under complex sea conditions is enhanced.

The invention relates to a neural puncture dynamic obstacle avoidance navigation method and system based on multi-modal image fusion, and belongs to the technical field of medical image navigation and surgical operations. A unified space-time reference model is constructed by fusing preoperative high-resolution static images and intraoperative real-time dynamic ultrasonic data; a three-dimensional model of a key nerve and blood vessel structure is automatically segmented and reconstructed by using an artificial intelligence technology, an optimal puncture path can be dynamically calculated based on real-time image data, a collision risk in a needle inserting process can be monitored in real time, and visual navigation guidance of multi-sensory fusion is provided for an operator through an augmented reality technology; by recording and analyzing data of the whole operation process, a big data platform is used for carrying out continuous iterative optimization on a segmentation and obstacle avoidance algorithm, and a self-perfect intelligent closed loop is formed. The problems that in a traditional nerve puncture operation, experience of an operator is relied on, real-time dynamic obstacle avoidance cannot be achieved, and the system lacks learning ability are effectively solved, and the accuracy, safety and intelligent level of the operation are remarkably improved.

The invention discloses a self-adaptive intelligent irrigation robot for hilly terrains. The self-adaptive intelligent irrigation robot comprises a crawler-type mobile chassis, a multi-mode irrigation device, a visual navigation module and an energy management system. The robot does not depend on a high-cost GPS, but utilizes an OpenCV visual module to carry out field path identification and hydrant positioning. Through a deep learning model, the system can automatically identify the types of crops such as tea trees and fruit trees, and automatically switch between two irrigation modes of drip irrigation and micro-spraying according to an identification result, so that precise operation is realized. The crawler-type chassis is integrated with a dip angle self-adaptive system, the differential speed of the crawler belts on the two sides can be dynamically adjusted, and it is ensured that the crawler-type chassis stably advances on a 35-degree abrupt slope. After the irrigation task is completed, the pneumatic recovery device can efficiently wind the water pipe, and the automation efficiency is improved. The problems that a traditional irrigation robot is high in positioning cost, poor in terrain adaptability and serious in water resource waste are effectively solved.

The invention provides a power transmission line adaptive inspection method and system, and belongs to the technical field of power transmission inspection. According to the real-time image, a light-weight visual navigation model is adopted to determine and recognize a tower, and the unmanned aerial vehicle flies to the tower top of the tower in an imitated mode according to the tower shape features of the recognized tower; wherein in the simulated flight, a GPS and a visual SLAM are adopted to independently solve a pose, and an optimally estimated fusion height is output through extended Kalman filtering fusion; adopting an identification algorithm for the tower to obtain tower parameters; and determining a routing inspection waypoint plan according to the tower parameter matching database, and shooting a routing inspection image of the tower key equipment according to the routing inspection waypoint plan. According to the invention, algorithms of target identification, visual tracking, automatic obstacle avoidance, path correction, adaptive dimming, automatic focusing, multi-angle shooting and the like are integrated, and power transmission line inspection with low difficulty, high efficiency and accurate identification is realized.

This invention provides a system and method for reliably interconnecting a native gladhand on a trailer with an AV truck, in which the gladhand can vary in placement and type on the trailer front. A visually guided robot manipulator arm accesses the trailer front. The arm includes an end effector assembly that is arranged to visually navigate to the gladhand and engage the gladhand by latching onto a gladhand using a connection tool that includes a hinged gladhand / wedge capture assembly and a pressurized clamping connection plate that selectively engages the trailer gladhand seal. The capture assembly is arranged to accommodate different gladhand geometries in latching thereonto. The connection tool includes a hinge that is spring-loaded in both a locked and unlocked bistable state, and sensors monitor the state. The system can include passive or active compliance elements that accommodate moderate misalignment between the engaged gladhand and the robotic arm / connection tool.

The invention provides a four-rotor unmanned aerial vehicle visual navigation control method and system suitable for a complex low-altitude environment, and is applied to the field of data processing application. According to the method, a quadrotor unmanned aerial vehicle is taken as an object, and flight-related basic information and environment data of the quadrotor unmanned aerial vehicle are acquired; processing the basic information to establish a flight control basic model, processing environment data and basic model information, establishing a state estimation system, using a depth camera to acquire and process visual information, and combining visual SLAM to realize pose estimation and positioning mapping to obtain environment perception and positioning mapping information; processing positioning mapping and basic model information, transmitting a signal to an embedded controller to control a steering engine, and fusing data to generate a control instruction and positioning tracking information; then, an upper computer monitoring system is constructed, and track planning and monitoring information is generated; and finally, integrating various types of information, and generating target implementation information of the visual navigation unmanned aerial vehicle control system.

The invention relates to the technical field of agricultural equipment control, in particular to an intelligent fusion control navigation system of a vegetable collection and transportation all-in-one machine, which comprises an image acquisition and preprocessing module, a cabbage ridge line identification and positioning module, a multi-source information fusion decision module and a path tracking and cooperative control module. The method comprises the following steps: collecting ridge row images through a visual sensor, executing multi-scale fusion denoising, histogram equalization and perspective correction, and extracting a clear visual navigation datum line; gNSS and IMU data are fused with visual information, and extended Kalman filtering is adopted to realize high-precision pose estimation; switching different operation modes based on the operation state machine, and dynamically generating a corresponding expected path; path tracking control is completed in combination with a pure tracking algorithm and a speed mapping table, and intelligent cooperative control over the whole receiving and operating process is achieved. According to the method, the ridge row identification robustness, the path tracking precision and the system collaborative operation capability are improved, and the method is suitable for automatic harvesting operation of cabbages in a complex farmland environment.

The invention belongs to the technical field of mouse neuroscience research, and relates to a multi-modal mouse virtual reality and nerve recording integrated system based on skull anchoring, which comprises a skull anchoring basic support, a binocular miniature VR display module, a multi-modal fixed expansion frame and a behavior and power supply platform. According to the invention, the skull screw is used as a unique space reference, accurate limiting of the head is realized through rapid butt joint of the MMA and the external fixing rod, and rapid plugging and coordinate multiplexing of equipment for two-photon imaging, optical genetic stimulation and the like are realized in combination with an M2 anchoring hole preset in the MMA; the total mass of the whole machine is strictly controlled within 3.2 g; the optical system provides panoramic visual stimulation with a horizontal field angle of 140 degrees, completely covers a key binocular vision area on the top of the mouse head, and supports high-order cognitive task paradigms such as complex ecological visual navigation, spatial memory and social interaction.