25487 results about "Flight vehicle" patented technology

A system for landing and charging an autonomous unmanned aerial vehicle (UAV) is disclosed. The UAV comprises a set of battery powered rotors to fly through the air. The UAV further comprises a landing peg that projects beneath the body configured to be received by a socket of a corresponding landing base. The landing base can have a funnel top to make landing easier. The landing peg has a set of power transfer means that connect with power transfer means in the landing base to recharge the batteries of the UAV. The UAV can be guided to land in the landing base by a ring of LEDs on the landing base.

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

An aerial vehicle configured for operation within indoor spaces has a meshed construction with a housing defined by upper and lower sections having meshes provided above and below propellers and motors of the aerial vehicle. The aerial vehicle also includes a suite of sensors such as LIDAR sensors, time-of-flight sensors, cameras, ultrasonic sensors, or others. Meshes of the upper and lower sections include central openings along with spokes and concentric rings provided about the central openings. Meshes of the lower section have substantially larger central openings than meshes of the upper section, but feature more dense spokes or concentric rings beneath tips of the rotating propellers, which may be hinged or foldable in nature. Data captured by sensors of the aerial vehicle may be utilized for any purpose, such as to generate environment maps of an indoor space, or to monitor the indoor space for adverse conditions or events.

A landing system suitable for receiving an unmanned aerial vehicle (UAV) comprises an autonomous ground vehicle (AGV). A landing surface is disposed on the AGV, and the landing system comprises a loading channel suitable for passing an object delivered by the UAV through a first loading channel opening in the landing surface. The object passes within the loading channel through to a second loading channel opening at a bottom aspect of the AGV. In this way, a UAV can land on the landing surface, and the AGV positions the object in line with a target delivery location, where the object is delivered. Aspects of the landing system comprise an electromagnet or vacuum chamber for securing the UAV to the landing surface, thereby enhancing stability of the UAV during movement of the landing system.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention belongs to the technical field of three-dimensional airspace management, and provides a three-dimensional airspace rasterizing management method and system based on multistage dynamic division, and the method comprises the steps: dividing height layers for a three-dimensional airspace, carrying out the rasterizing division of the three-dimensional airspace in each height layer, and carrying out the rasterizing division of the three-dimensional airspace; a live-action three-dimensional model of a three-dimensional airspace of a target area is constructed through multi-source data fusion, passable grids are marked, selection priority values of the passable grids are calculated through multi-source data fusion, when an aircraft is about to enter the three-dimensional airspace, route dynamic priority values of the grids are initialized, and a basic planning route of the aircraft is generated. Detecting the collision risk of the aircraft on the grids in the basic planning route, dynamically adjusting the route dynamic priority value, updating to obtain the actual planning route of the aircraft, monitoring the space between the aircrafts in the three-dimensional airspace in real time, and judging whether the collision risk exists between the aircrafts; and if there is a collision risk signal, triggering an aircraft obstacle avoidance and path re-planning strategy, and dynamically adjusting and updating the grid side length.

The invention discloses a low-altitude airspace management method and system based on data analysis, and relates to the technical field of air traffic management and low-altitude airspace planning, and the method comprises the steps: 1, multi-scale dynamic grid division: adjusting the grid granularity in real time according to the aircraft density and environment complexity, generating a multi-scale grid by adopting an optimized quadtree data structure, and carrying out the multi-scale dynamic grid division; boundary smoothing processing is carried out on boundary areas of the grids with different scales; 2, intelligent airspace layering: dynamically adjusting the thickness of a height layer according to a risk score, and setting a buffer layer to enhance the flight safety; and 3, intelligent semantic annotation: giving dynamic semantic information such as risk levels and traffic priorities to the grid units, embedding airspace control regulations and flight rules, and realizing multi-source data fusion and real-time updating of semantic information. According to the method, the problems of static division limitation, boundary problem, insufficient real-time performance and the like in the prior art are solved, and the airspace resource utilization rate and the flight safety are improved.

The present invention provides a flight attitude control method based on inertial navigation technology, which relates to the field of aircraft control technology and includes an attitude control system. The attitude control system includes a quantum inertial navigation fusion module, a distributed multi-source collaborative inertial navigation module, a real-time error compensation module based on deep learning, an energy collection and self-powered inertial system module, a distributed attitude data management module based on blockchain, a multimodal fusion and virtual twin collaborative control module, and a terahertz communication and inertial navigation fusion anti-interference module. The present invention uses a quantum annealing algorithm combined with a compressed sensing algorithm to optimize quantum sensor data, and then combines the D-S evidence theory and the particle swarm optimization algorithm in the distributed multi-source collaborative inertial navigation module to fuse multi-source data, which can deeply mine the deep connection and complementary information between different sensor data, break the simple data level superposition limitation, and greatly improve the accuracy of the attitude information after fusion.

A vehicle control and interface system configured to provide dynamic flight envelope protection of an aerial vehicle is described. In particular, the system dynamically calculates allowable trajectory limits for each axis of control through all phases of flight of an aerial vehicle and provides a visualization of the calculated limits on a graphical user interface. During normal operation, the system does not allow the operator to exceed the calculated limits. Responsive to receiving specified user input to modify vehicle navigation, however, the system causes the aircraft to enter an extended envelope by degrading vehicle parameters according to a hierarchy. Moreover, the system is configured to allow the vehicle to operate in a degraded flight state during a prolonged loss of a sensor signal by setting an adjustable hover trim position of each inceptor device and allowing an operator to control horizontal acceleration of the vehicle.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

A heat dissipation structure includes a housing configured to accommodate a heating element of an aerial vehicle. The housing includes a first air vent configured to guide an airflow into the housing and a second air vent configured to guide the airflow out of the housing. The airflow includes a propeller-generated airflow generated by a propeller of the aerial vehicle during rotation. The housing is located at an end of an arm of the aerial vehicle, away from the propeller in a longitudinal direction of the arm. A projection of the housing on a plane on which an area of rotation of the propeller lies at least partially overlaps the area of rotation of the propeller.

The invention discloses an aircraft flow field prediction method and system of a multi-region physical drive neural network, and the method comprises the steps: constructing a continuous region mask and high-dimensional physical parameter sampling system, carrying out the global sampling of high-dimensional physical parameters through employing a Latin hypercube sampling method, and carrying out the space division through combining with a KMeans clustering algorithm; inputting the space coordinates, the continuous area mask, the wall surface distance and the physical condition parameters into an AMPD model, and generating a boundary layer mask, an eddy current mask and a physical residual error; inputting the boundary layer mask and the eddy current mask into a physical constraint driven loss function system, and establishing a multi-target residual minimization loss function for training an AMPD model; based on the multi-target residual error minimization loss function and the physical residual error, training an AMPD model by adopting a course learning training strategy; wing surface flow field reconstruction is carried out through the trained AMPD model, aircraft flow field prediction is completed, and high-precision and high-efficiency intelligent prediction of wing streaming is achieved.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

An electric tilt rotor aircraft and a control system thereof. The aircraft comprises a fuselage (1), wings (2), horizontal stabilizers (3) and a landing gear system (4), wherein the fuselage (1) comprises a fuselage body and a rudder (10) for controlling and adjusting a flight direction of the aircraft; the wings (2) each comprise an integral wing box section (21), an aileron (22), a wing left / right nacelle (23) and a supporting rod left / right nacelle (24); left and right supporting rods are integrated on front sides of the integral wing box sections (21); the wing left / right nacelle (23) is connected to the integral wing box section (21) by means of a tilting shaft (25), and the wing left / right nacelle (23) rotates around the tilting shaft (25); the horizontal stabilizers (3) each comprise a horizontal stabilizer box section (31) and a horizontal stabilizer left / right nacelle (32); the horizontal stabilizer left / right nacelle (32) is connected to the horizontal stabilizer box section (31) by means of the tilting shaft (25), and the horizontal stabilizer left / right nacelle (32) rotates around the tilting shaft (25); the landing gear system (4) uses a tricycle landing gear system, and comprises a nose landing gear (41) and two main landing gears (42); brake systems are integrated in the main landing gears (42); and the nose landing gear (41) plays a role in balancing and controlling direction. By using the electric tilt rotor aircraft, urban air traffic can be realized.

The invention discloses a low-airspace aircraft flight control system and method based on an ai algorithm, and relates to the technical field of ai algorithms, and the system comprises a data processing module which collects the environment data of an aircraft flight airspace, and carries out the denoising of the collected data; and the airspace unit capacity dynamic adjustment module dynamically adjusts the airspace unit capacity based on the flight state, the wind speed and the communication precision of the aircraft, and predicts the unit capacity demand in the future time period through an LSTM model. According to the method, a path with the optimal total cost is generated in a complex environment through an ai algorithm based on a heuristic search strategy, population diversity of a genetic algorithm is further matched, accumulated cost and prediction cost are combined through heuristic search, the generated path has global optimality, and the path has the optimal total cost. A path generation strategy can be flexibly adjusted in a scene with sparse or dense obstacles, and the method adapts to the complexity of low-airspace flight.

The invention belongs to the technical field of aircraft navigation, guidance and control, and provides a satellite exploration control system and method based on deep reinforcement learning, and the system comprises a sensing layer, a decision layer, and an execution layer. The sensing layer is used for integrating equipment data and performing primary processing; the decision-making layer is used for deploying a deep reinforcement learning model and regularly carrying out data training and model updating; the execution layer is used for controlling satellite attitude and orbit adjustment and performing autonomous fault diagnosis and recovery operation; according to the method, effective control under different environments and emergencies is realized through continuous learning and optimization, the adaptive capacity and the autonomous control capacity are relatively high, and an efficient and accurate control strategy can be realized in a complex and changeable space environment through gradual optimization and iteration, so that the method is suitable for large-scale popularization and application. And the autonomous operation capability and the task execution efficiency of the satellite are greatly improved.

The invention discloses an unmanned aerial vehicle accurate position landing method and system based on image recognition analysis, particularly relates to the technical field of aircraft landing, and aims to solve the problem of influence of jitter interference caused by micro airflow on landing precision. Identifying the jitter signal intensity and judging whether the jitter signal intensity is abnormal or not by detecting the posture change of the unmanned aerial vehicle in the landing process in real time; when the jitter is abnormal, carrying out multi-frame image superposition processing to generate a stable image sequence; detecting fine texture disturbance between adjacent frames based on multi-resolution fractal dimension analysis, and compensating non-linear influence caused by micro airflow; recording a jitter trajectory and calculating the relative displacement of the landing target through reverse trajectory reconstruction; extracting calibrated landing area features in combination with a convolutional neural network, generating an offset compensation value of a landing point, and dynamically correcting a landing path; according to the invention, the landing precision of the unmanned aerial vehicle in a complex environment is improved, and reliable guarantee is provided for automatic landing of the unmanned aerial vehicle.

An emergency module may determine occurrence of emergency events of a vehicle traversing through an environment. The emergency module may rank the emergency events according to importance level associated with each emergency event. The emergency module may select an emergency event based on the ranking. The emergency module may notify a user of the vehicle of the selected emergency event. The emergency module may identify corrective actions associated with the selected emergency event, the identified corrective actions including a user action and a non-user action. The emergency module may perform the non-user action of the identified corrective actions. The emergency module may notify the user of the vehicle of the user action.

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 provides a target identification tracking guiding method of a photoelectric pod, detection equipment, a program product and a storage medium. And super-pixel segmentation is performed on the scanned image information to remove the background, so that irrelevant information interference can be effectively reduced, and subsequent target identification is more accurate and efficient. And calculating a three-dimensional coordinate by integrating the position and attitude of the sensor, the position of the target image and distance information, and locking the spatial position of the target. Target features are obtained, multi-hypothesis tracking is performed in combination with three-dimensional coordinates, complex motion states can be dealt with, the stability and accuracy of target trajectory detection are ensured, and the target is not easy to lose even if the target moves variably. Characteristics and trajectory data are input into a model to identify target identities, tracking conditions are judged according to a preset list, it is ensured that only key targets are tracked, and resource waste is avoided. And the target tracking capability of the autonomous or semi-autonomous aircraft in a complex environment is integrally improved.

The invention provides an electric engine, a control method, a related device, a motor controller and an aircraft, and relates to the technical field of aircrafts. The electric engine comprises a power motor, a heat dissipation system and a control module, a cavity is formed in the power motor, the control module is integrated in the cavity, the space occupied by the electric engine is reduced, and therefore the size of the aircraft is reduced; the control module comprises at least three integrated driving modules, part of the driving modules are electrically connected with the power motor so as to control the power motor, part of the driving modules are electrically connected with a variable pitch motor of the propeller so as to control the variable pitch motor, and part of the driving modules are electrically connected with a heat dissipation motor in the heat dissipation system so as to control the heat dissipation motor. The performance and efficiency of the electric engine are improved through unified integrated control over the power motor, the heat dissipation motor and the variable pitch motor. And through a unified control architecture, the system design is simplified, the development and maintenance cost is reduced, and the maintainability of the electric engine is improved.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

A disclosed airborne vehicle includes split-ring resonators (split ring resonators), which may be embedded within a material. Each split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. Each split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, each may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

The present disclosure relates generally to flight control of electric aircraft and other powered aerial vehicles. In one embodiment, a method is disclosed, comprising: receiving a descent rate command from a pilot input device, determining a proximity of each propeller of at least two propellers to a vortex ring state; and controlling the aircraft's descent rate to be less than the commanded descent rate when at least one of the at least two propellers is within a first threshold proximity to the vortex ring state.

The invention discloses an aircraft surface flow field intelligent reconstruction method and system based on multi-source data fusion, and belongs to the technical field of flow field intelligent prediction. Acquiring multi-source sensor data on the surface of the aircraft, and constructing a multi-source data set; a flow field reconstruction model based on Transform is constructed; a multi-source data set is adopted to train a flow field reconstruction model; and outputting an aircraft surface flow field reconstruction result based on the trained flow field reconstruction model, and visually displaying the aircraft surface flow field reconstruction result. Based on the strong nonlinear mapping capability of the deep learning model, through training of a large amount of sensor data, high-order correlation characteristics among pressure, heat flow and friction resistance stress can be automatically extracted, through fusion of sparse discrete data, a continuous physical field of the whole surface of the aircraft can be effectively reconstructed, and the method is suitable for the aircraft. The limitation of a traditional analytical model in dealing with a strong nonlinear problem is broken through, and the precision and reliability of aircraft flow field prediction are improved.

The present disclosure relates to an aircraft including two or more propulsive devices, and a fuselage integrated with a pair of wings to create a Blended Wing Body (BWB) structure, wherein an orientation of the fuselage and the pair of wings change based on a transition of the aircraft from a vertical position to a horizontal position. Further, each wing of the pair of wings includes a lower winglet and an upper winglet that are identical and coupled to each other at a wing tip. The aircraft further includes a plurality of elevons controlled by two or more propellers associated with the two or more propulsive devices.

The invention discloses a group aircraft collaborative perception decision control method and system in a complex high-dynamic environment, and relates to the technical field of cluster aircrafts, and the method comprises the steps: obtaining the performance parameters of a target group aircraft, building a task scene model in combination with the geographic information of a target task area, and building a target optimization function. Iteratively optimizing an aircraft and task allocation scheme through a graph neural network algorithm, outputting a task allocation result of each aircraft, and then according to a flight path constraint condition, analyzing a matching degree between a flight cost generated in a flight path and a corresponding task demand; and generating a globally optimal task allocation and flight path cooperation scheme. Task distribution and path planning are dynamically adjusted by sensing task requirements and environment changes in real time and combining performance parameters of all aircrafts, and compared with a traditional static method, the method can quickly respond to dynamic changes of task scenes, and task distribution reasonability and path planning effectiveness are ensured.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.