2092 results about "Attitude control" patented technology

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention discloses a cable inspection robot attitude control method based on MPC and ADRC fusion, and the method comprises the steps: firstly, building a model prediction control (MPC) model, carrying out the prediction of the future state of a robot, solving an optimal control problem, and obtaining initial control input for achieving the internal dynamic compensation; secondly, an extended state observer (ESO) in an auto-disturbance rejection control (ADRC) model is adopted, and external disturbance is estimated in real time and used for compensating control input; thirdly, a fuzzy self-adaptive module based on fuzzy PID is introduced, the attitude error, the attitude error change rate and regeneration disturbance intensity generated based on estimation disturbance are collected in real time, the MPC weight and the ESO gain are dynamically adjusted, and the response speed and robustness of the system under variable working conditions are improved. According to the hierarchical control scheme, the defect that an existing single control method is difficult to keep the optimal control effect under variable working conditions is effectively overcome, and technical guarantee is provided for stable operation of the cable inspection robot in a high-risk and high-complexity environment.

The invention discloses an unmanned aerial vehicle flight control system and method with precise positioning and autonomous obstacle avoidance, and particularly relates to the field of control, and the system comprises a positioning module, an obstacle detection module, a data processing and analysis module, an autonomous obstacle avoidance algorithm module, and a flight control module. Static and dynamic obstacles are detected in real time by integrating the laser radar, the millimeter wave radar, the visual sensor and the ultrasonic sensor, and an obstacle information report is generated through a data fusion algorithm based on machine learning; based on the processed environment data, the system uses deep reinforcement learning and a model prediction control algorithm to generate a safe flight path in real time, and an obstacle avoidance decision is optimized; the flight control part adopts a hierarchical control framework, and precise flight of the unmanned aerial vehicle is realized through trajectory tracking, attitude control and actuator control.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention relates to the technical field of vehicle dynamics control, and discloses a vehicle control method and system for a double-closed-loop adjustable magneto-rheological semi-active suspension, and the method comprises the steps: S1, obtaining the state information of a vehicle based on a sensor, building a vibration control model, and calculating and outputting the expected damping force of vibration control; s2, establishing a vehicle body attitude control model, and calculating and outputting vehicle body attitude control expected damping force; s3, calculating the expected damping force obtained in the step S1 and the expected damping force obtained in the step S2 to obtain the total expected damping force; and S4, according to the total expected damping force, the relative speed of the wheels and the vehicle body and the dynamic deflection information of the suspension, a suspension inverse model is established, and expected current is output. According to the method, the control system is optimized by combining the design parameters of the shock absorber and the test parameters of the real vehicle, the current loop adjustment is designed on the basis of the damping loop control, and the tracking compensation control of the current is mainly realized, so that the response speed and the accuracy of the control are improved, and the performance of the magneto-rheological semi-active suspension is further improved.

The embodiment of the invention provides an aircraft attitude control model generation method and device, equipment and a medium, and is applied to the technical field of aircraft control. The method comprises the following steps: acquiring a flight task and a flight environment; according to a pre-constructed simulation model, carrying out analogue simulation on the flight task and the flight environment, and outputting simulated flight data; training a pre-constructed reinforcement learning model by taking a flight strategy as an output and the simulation flight data as an input; and when a preset stop condition is met, stopping training, and taking the trained reinforcement learning model as an aircraft attitude control model. In this way, the training period and training cost of the controller can be reduced, and the aircraft attitude control model with a better control effect can be trained.

The invention discloses an unmanned aerial vehicle attitude feed-forward compensation control method for building wind field disturbance, and belongs to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: building a mapping relation between a building space position and a wind field disturbance characteristic; processing the real-time wind field data in combination with the mapping relation to generate a wind field prediction result; calculating a feed-forward compensation amount, and synchronously determining a dynamic weight factor reflecting the disturbance intensity of the wind field; obtaining an attitude error between a current attitude and an expected attitude of the unmanned aerial vehicle, and calculating a feedback control quantity; and performing weighted fusion on the feedforward compensation quantity and the feedback control quantity by using the dynamic weight factor, and generating a final control instruction to control the attitude of the unmanned aerial vehicle. According to the method, the wind field mapping relation is established and the wind field is predicted to calculate the feed-forward compensation quantity, and then the dynamic weight factor is used to carry out weighted fusion on the feed-forward compensation quantity and the feedback control quantity, so that the disturbance of the wind field can be actively inhibited, and the attitude control stability and precision of the unmanned aerial vehicle in a complex environment are improved.

The invention relates to the technical field of aircrafts, and discloses an aircraft attitude control method, device and equipment and a storage medium, and the method comprises the steps: collecting flight state parameters of an aircraft; the pitch angle data in the flight state parameters are compared with a preset angle threshold value, corresponding flight mode switching is executed, and the attitude deviation value is calculated; power distribution calculation is conducted on the three ducts, and the target rotating speed of each duct paddle is obtained; performing position optimization calculation on the guide plates below the duct to obtain the position control quantity of each guide plate; the three flow guide plates are configured as triangular control points, the position compensation amount of an aileron, a yaw rudder and an elevator is calculated and determined through vector synthesis, and the angle control amount of each control surface is obtained; according to the method, the attitude of the aircraft is converted into an execution signal, execution feedback data is acquired to correct a control instruction in real time, closed-loop attitude control of the aircraft is realized, the attitude control precision of the aircraft is improved, and the stability of the flight process is ensured.

The invention discloses an unmanned aerial vehicle visual positioning flight optimization system based on multi-source data fusion, and belongs to the technical field of unmanned aerial vehicle navigation and control. The system comprises a data acquisition module used for acquiring RGB-D image data and multiple physical data; the environment modeling module is used for constructing a local three-dimensional environment map; the path planning module is used for generating an obstacle avoidance flight path; the instruction generation module is used for converting the obstacle avoidance flight path into an attitude control instruction and an accelerator instruction; the state prediction module is used for obtaining an unmanned aerial vehicle prediction state vector at the next moment; the positioning switching module is used for generating compensated fusion positioning data; and the flight control module is used for executing the attitude control instruction and the accelerator instruction. Through the multi-source data fusion and state prediction compensation mechanism, the problems of positioning interruption and trajectory divergence caused by loss of visual signals and unstable flight caused by sudden change of multi-source coordinate switching in the prior art are solved.

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

The invention discloses an unmanned aerial vehicle autonomous homeward voyage charging system based on wireless charging, and relates to the field of flight control and energy management, and the system comprises the steps: monitoring the flight path, remaining power and environmental parameters of an unmanned aerial vehicle, predicting the remaining power required for completing a task, and triggering a homeward voyage condition signal and the current position information of the unmanned aerial vehicle; generating an optimal homeward flight path of the nearest wireless charging platform based on the homeward flight triggering condition signal and the current position information of the unmanned aerial vehicle; obtaining an optimal charging point based on the generated return path, and sending a deployment instruction to the mobile charging platform; the mobile charging platform advances according to the deployment instruction path, the unmanned aerial vehicle returns according to the optimal return path, the course is corrected through inertial navigation and a visual algorithm, and the landing point of the unmanned aerial vehicle is obtained. Fine adjustment and positioning of the flight attitude are formed by using the visual identification and attitude control module, and a closed-loop flight control mechanism based on image processing and attitude adjustment amount calculation is realized.

The invention discloses a high-precision three-degree-of-freedom attitude adjustment platform for a spectrometer, which is based on a simplified design of a Stewart platform and comprises a movable platform, a static platform and a three-degree-of-freedom driving mechanism connected between the movable platform and the static platform. The driving mechanism is composed of three sets of servo electric cylinders, telescopic rods and servo motors and is driven by a ball screw, and posture adjustment of the platform is achieved. According to the platform, a laser interferometer is adopted as a high-precision sensor, a closed-loop feedback control system is combined, and submicron displacement measurement and attitude control are achieved. The platform can achieve translation in the Z-axis direction and rotation around the X axis and the Y axis, the rotation angle can reach + / -15 degrees, and the precision can reach 0.02 mm. According to the invention, the device ensures high precision and stability in a complex working environment, has the characteristics of stable movement, quick response, easy maintenance and the like, and is suitable for the fields of optical experiments, spectral analysis and attitude adjustment and error compensation of precise instruments.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The invention discloses a rotor fault positioning and power reconstruction system and method for a composite wing unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle fault-tolerant control. The system comprises a data acquisition module, a fault verification module and a power redistribution module, and is characterized in that firstly, a rotor fault-attitude change direction symbol mapping table is established according to a rotor physical layout and attitude dynamics relationship, and a fault type is identified; after symbol matching is met, a dynamic threshold energy verification function is constructed for judgment, and whether the rotor has a real fault or not is judged through a fault dual verification model of symbol matching and energy verification; a control distribution matrix is dynamically reconstructed according to the verified fault rotor position, power compensation is provided by coordinating normal rotors, and stable control over the flight attitude is achieved. The invention provides an effective method for reducing the false alarm rate and the fault response time of the sensor, so that a control system can quickly respond and recover the flight attitude when the rotor wing suddenly fails, the attitude control stability is ensured, and the flight safety is remarkably improved.

The invention discloses a control method for adjusting the tunneling attitude of a shield tunneling machine, particularly relates to the field of attitude control of the shield tunneling machine, and aims to solve the problem that attitude deviation caused by underground environment change cannot be timely and effectively corrected in the tunneling process of the shield tunneling machine. The method comprises the following steps of: calculating membership degrees of horizontal deviation, vertical deviation and change rates of the horizontal deviation and the vertical deviation, extracting high-frequency and low-frequency characteristics, generating independent phase signals, and calculating a judgment coefficient by using a multi-dimensional data fusion and high-dimensional exponential integration method so as to dynamically adjust the weight of a fuzzy control rule base and realize adaptive optimization of PID (Proportion Integration Differentiation) gain parameters; a master control output signal is generated based on the updated PID parameters, the thrust of the partition oil cylinders is accurately adjusted, it is ensured that the posture of the shield tunneling machine is rapidly, accurately and stably adjusted under the complex environment condition, the control precision of the tunneling direction and the construction safety are improved, and the working efficiency is improved. And meanwhile, the overall posture correction capability of the shield tunneling machine is further enhanced through dynamic adjustment of the rolling angle.

The invention discloses an aircraft attitude control method and device based on a semiconductor microcomputer system, and the method comprises the steps: collecting the multi-source sensor data of an aircraft, calculating an attitude error vector, and constructing a three-dimensional error space coordinate system; taking the three-dimensional error space coordinate system as a reference, adjusting the model in real time, and solving the optimal attitude adjustment amount in a prediction window period; the optimal attitude adjustment amount is input into an execution mechanism distribution module, and a thrust adjustment instruction sequence and a control surface deflection angle instruction queue of each vector propeller are generated through calculation in combination with a propeller response matrix and a control surface efficiency coefficient; dynamically correcting and predicting weight parameters of the controller through closed-loop verification; and updating the multi-objective optimization model based on the corrected parameters, adjusting the instruction distribution proportion through a dynamic reconfiguration interface, and writing the updated parameters into the attitude database to form a complete closed-loop period. According to the invention, high-precision real-time control of the attitude of the aircraft is realized, and the flight stability and the energy efficiency ratio in a complex environment are improved.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably improved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention relates to an anti-interference control method and system for a hypersonic flight vehicle. The method comprises the following steps: constructing a control-oriented attitude system model of the hypersonic aircraft, and constructing a terminal function according to an acceleration convergence parameter, aircraft attitude system state information and error information based on the control-oriented attitude system model of the hypersonic aircraft; designing a dynamic sliding mode surface according to a terminal function and an adaptive law; a finite time controller is constructed by taking the dynamic sliding mode surface as a strategy, so that a tracking error is converged to a design value within a fixed time, and flight control input is obtained; the method comprises the following steps: constructing a fixed time fuzzy interference observer according to a fuzzy logic method and a fixed time control method based on a control-oriented attitude system model of a hypersonic aircraft, and obtaining an interference estimated value; and inputting the value into a finite time controller, and carrying out interference compensation on flight control input. The hypersonic flight vehicle attitude control method is used for hypersonic flight vehicle attitude control, and has the advantages of fast tracking speed, high tracking precision and strong robustness.

The invention discloses a hypersonic flight vehicle adaptive attitude control method based on near-end strategy optimization, and belongs to the technical field of hypersonic flight vehicle control, and the method comprises the steps: building an air-breathing hypersonic flight vehicle longitudinal dynamic model considering power; performing reinforcement learning training on the obtained environment model by using a PPO algorithm to obtain an optimized control strategy; selecting a proper state representation and action space, and designing a strategy network and a value network for the processed model, so that an original complex attitude control problem is converted into a reinforcement learning problem which can be effectively learned; and training the reinforcement learning model by using an improved PPO algorithm to obtain a final control instruction. According to the method, the model is trained by using the PPO algorithm, so that the defects of complex controller design, strict requirement on model precision and the like of a traditional control method can be effectively overcome. A simulation experiment verifies that pitch angle tracking can be effectively realized, and the stability of an execution mechanism is guaranteed.

The invention belongs to the technical field of reinforcement learning and aircraft attitude control, and particularly relates to an aircraft attitude control method based on explainable reinforcement learning, which comprises the following steps: firstly, designing an aircraft attitude controller with strong robustness, introducing a multi-channel control network and combining a reinforcement learning algorithm to control the attitude of an aircraft; the controller can effectively cope with challenges brought by external interference and system nonlinearity, and the adaptability and robustness of an aircraft control system are remarkably improved. Secondly, the invention provides an interpretability method combined with visual analysis. The t-SNE algorithm is used for carrying out dimensionality reduction visualization on the input characteristics of the aircraft control system, the SHAP value is combined for carrying out attribution analysis on the input characteristics, key factors in the model decision making process can be revealed, and understanding how the aircraft control system makes decisions according to different input characteristics is helped. According to the invention, high adaptability and transparency of the aircraft control system in a complex environment are realized.

The invention belongs to the technical field of coupling alignment, and particularly relates to an intelligent auxiliary system for coupling alignment based on dual-laser displacement and an MEMS gyroscope, which adopts a mode of cooperative work of an active stable attitude measurement module and a space reference beacon module to establish a dynamic stable measurement basis which is not influenced by barring operation and environmental disturbance. The micro attitude change of the measuring device can be sensed and actively compensated in real time in the measuring process, it is ensured that laser displacement data at all angles are collected in the same absolute space coordinate system, and the high consistency and accuracy of multi-point measurement data are ensured. And then fusing the attitude feedback signal and the attitude reference signal by using an intelligent resolving module to generate an attitude control instruction, and processing displacement measurement data acquired by a double-laser displacement sensor after the attitude is stable to resolve a centering deviation parameter, thereby being beneficial to improving the efficiency, precision and automation level of coupling alignment work.

The invention discloses a curved surface mapping track optimization and multi-modal force control method for complex wall surface polishing. The method can efficiently meet the polishing requirement of a complex curved surface. A polishing track is determined by introducing a projection technology, a path optimization model is constructed, and multi-node force fusion attitude control is combined, so that interpolation point calculation and attitude change and position planning matching are realized, and a robot polishing path including a polishing position, an attitude and adjustment parameters is effectively generated. According to the multi-mode force control method, a force control mode is dynamically adjusted by combining radial height state switching of state classification of convex and concave path sections; multi-node fusion attitude control is further introduced, the surface contact force of a sand disc is accurately tracked, the tail end attitude of the robot is dynamically adjusted to follow the curved surface characteristics, the problems of irregular curved surfaces, unstable force control and the like in the traditional grinding technology are solved, and the method can be widely applied to automatic efficient grinding operation in the fields of buildings, ships and the like; and efficient and high-precision grinding of the complex curved surface is achieved.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention provides a pose integrated control method for a quad-rotor unmanned aerial vehicle with a time-varying mass load, and the method comprises the steps: inputting a desired position and desired attitude quaternion based on a pose integrated dynamical model and a kinematic model of the quad-rotor unmanned aerial vehicle carrying the time-varying mass load, and a pose integrated hyperbolic sliding mode controller based on a torque, and carrying out the control of the time-varying mass load of the quad-rotor unmanned aerial vehicle. And performing unified control on the position and the attitude of the unmanned aerial vehicle through the attitude integrated hyperbolic sliding mode controller of the torque.

The invention relates to the technical field of unmanned aerial vehicle flight control, and particularly provides an unmanned aerial vehicle flight control method and system, and the method comprises the steps: obtaining real-time rotating speed information and real-time current signals corresponding to each rotor motor; acquiring expected rotating speed information and expected current information of each rotor motor according to the unmanned aerial vehicle attitude control instruction and the current flight state; for each rotor motor, analyzing whether the working state of the rotor motor is abnormal or not according to the corresponding real-time rotating speed information, the real-time current signal, the expected rotating speed information and the expected current information; when it is analyzed that the working state of the rotor motor is abnormal, a compensation control quantity is obtained according to the deviation between the real-time rotating speed information and the expected rotating speed information and the deviation between the real-time current information and the expected current information, and then working parameters of the rotor motor with the abnormal working state are adjusted according to the compensation control quantity; according to the method, the influence of disturbance caused by local airflow environment difference on the attitude of the unmanned aerial vehicle can be sensed and inhibited.

The invention discloses a dual-ring active-disturbance-rejection trajectory tracking control method for a four-rotor unmanned aerial vehicle, and the method comprises the steps: representing a dynamic coupling effect of the unmanned aerial vehicle caused by the load mass change and dynamic center-of-gravity shift through a six-degree-of-freedom flight dynamic model containing an asymmetric inertia matrix; an outer ring position control loop and an inner ring attitude control loop are constructed on the basis of an active disturbance rejection controller, and the following control cycles are executed in real time on the basis of integrating an extended state observer: the outer ring position control loop generates a position control signal and an attitude expected angle according to an expected track; the inner loop attitude control loop generates a control quantity for driving the unmanned aerial vehicle according to the attitude expected angle, and the two loops cooperatively work through decoupling logic to realize expected trajectory tracking; estimating and compensating unmodeled dynamics and external interference of the unmanned aerial vehicle in real time through the extended state observer; and dynamically adjusting parameters of the active-disturbance-rejection controller by adopting a particle swarm optimization algorithm.

The invention discloses an automatic stacking method and device, a terminal and a storage medium, and the method comprises the steps that package information of a to-be-stacked package is obtained according to an acquisition device, and an optimal stack type is matched for the to-be-stacked package in combination with historical stack type data of an automatic stacking system; carrying out point cloud segmentation on the tray with the stacked parcels through a preset segmentation model to obtain a segmentation result, calculating a tray gap in combination with the optimal stack type and a preset path planning algorithm, and generating a stacking path; the flexible clamping jaw device is controlled to grab the parcels to be stacked, the grabbing posture is corrected in real time in combination with feedback of the torque sensor, and the mechanical arm is controlled to execute stacking according to the stacking path; and when the segmentation result of the tray is matched with the optimal stack type, a stacking three-dimensional model is generated, the stacking three-dimensional model is bound with the AGV below the tray, and the AGV is controlled to be automatically put in storage. According to the method, stacking and warehousing are completed through stack type matching and path planning, and the adaptability and the space utilization rate of a stacking system are improved.

The invention discloses an autonomous navigation and obstacle avoidance method for an intelligent aircraft. The autonomous navigation and obstacle avoidance method comprises the following steps: S1, multi-modal data acquisition; s2, generating a motion path; s3, predicting an obstacle path; s4, flight obstacle avoidance; the invention discloses an autonomous navigation and obstacle avoidance system for an intelligent aircraft. The system comprises a sensor group, an edge calculation unit, an execution mechanism, an environment sensing and positioning module, a decision control module and a communication module, the autonomous navigation and obstacle avoidance capabilities of the aircraft are remarkably improved through multi-modal data fusion, three-dimensional environment map generation, dynamic obstacle prediction, path planning and attitude control; semantic tags are introduced to identify obstacle types, geometric consistency scores ensure authenticity and reliability of obstacles, and radar reflection intensity and point cloud density provide spatial distribution and material information; and the dynamic obstacle prediction model dynamically adjusts an obstacle avoidance strategy in combination with semantic tags, geometric consistency scores, radar reflection intensity and point cloud density.