2696 results about "Trajectory planning" patented technology

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The disclosure provides a method and system for trajectory tracking control of a vehicle-manipulator coupling system with finite time prescribed performance. Specifically, a coupling weaken trajectory planning method is designed to reduce the system's coupling effects. A finite time performance function is designed to constrain the trajectory tracking error. In a case the constraint conditions corresponding to the finite time performance function are satisfied, the trajectory tracking error is converted to obtain a transformed error. The sliding mode surface is designed based on the transformed error to control the transformed error to converge in a finite time, and the external disturbance of the vehicle-manipulator coupling system is observed based on non-linear disturbance observer. The control input of the vehicle-manipulator coupling system is designed based on the sliding mode surface and the non-linear disturbance observer output. This ensures that the vehicle-manipulator coupling system can operate precisely along the desired trajectory.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

The invention belongs to the technical field of laser processing, and particularly relates to a special-shaped curved glass laser cutting track planning method. According to the method, the instantaneous thermal effect and the historical thermal accumulation effect are subjected to coupling calculation, high-precision prediction and compensation of thermal deformation in the laser cutting process are achieved, the accuracy of the cutting track is improved, contour deviation caused by thermal deformation is avoided, and the geometric precision is improved; according to the local geometric complexity of the special-shaped curved glass, regions are divided, and differential virtual scanning layers and scanning modes are matched, so that the dynamic balance of efficiency and precision is guaranteed, the processing quality of regions with high curvature, thin walls and the like is guaranteed, the cutting speed of flat regions is increased, and the processing period is shortened; by monitoring the temperature and feeding back actual thermal historical parameters during cutting, model parameters are reversely optimized by using contour precision data after cutting is completed, so that robustness and stability are enhanced, and the yield and process reliability of laser cutting of the special-shaped curved glass are improved.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention discloses a multi-aircraft cooperative formation flight path planning method based on a leader and follower model, and relates to the technical field of environment perception and unmanned aerial vehicle cluster cooperation. The method comprises the following steps of: firstly, designing a multi-agent double delay depth deterministic strategy gradient (LFMATD3) based on a leader-follower model, and converting an optimization problem model into a Markov decision process model by introducing an artificial potential field model and a reward function; and secondly, constructing an independent agent for each unmanned aerial vehicle, optimizing a behavior strategy of the unmanned aerial vehicle by combining a reward function based on an algorithm framework of deep reinforcement learning, and performing flight path planning and realizing dynamic formation control. The method provided by the invention can effectively improve the formation stability and collaboration of the unmanned aerial vehicles in a complex environment.

The invention discloses an end-to-end automatic driving method based on dynamic multi-modal fusion in a complex scene, and belongs to the technical field of automatic driving. In order to solve the problems of sensor perception deficiency, cross-modal feature mismatching, unstable trajectory planning and the like easily occurring in night, low-illumination and complex dynamic environments in the existing end-to-end automatic driving method, texture details of a camera mode and geometric structure features of a laser radar mode are respectively enhanced through a double-flow feature refining mechanism; the characteristic difference between different modes is relieved; an information-driven dynamic fusion strategy is designed, the fusion weight is adaptively adjusted according to scene factors such as environment illumination and obstacle density, and the scene sensitivity and discrimination ability of the model are improved; asymmetric convolution and a low-rank-sparse decoupling technology are introduced, multi-order reconstruction of key channels is carried out on the multi-modal features, and the path change modeling capability is enhanced; and in combination with time sequence dependence of waypoints, outputting a future trajectory through an autoregression decoder to realize high-precision trajectory prediction and stable decision control.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention discloses a deep learning-based arm trajectory planning method and control system for an intelligent humanoid robot with a body. The method comprises the following steps: acquiring a three-dimensional grid map of a multi-modal data construction environment; generating an initial feasible path through a path planning strategy network based on deep reinforcement learning; extracting the collision probability of each path node on the initial feasible path; according to the collision probability of each path node, judging an area in which local re-planning needs to be triggered; and carrying out smoothing processing to obtain a smooth obstacle avoidance path. The initial feasible path is generated through the path planning strategy network based on deep reinforcement learning. According to the collision probability of each node, an area needing local re-planning is screened out, and the path of the area is adjusted through an artificial potential field method, so that the capability of avoiding dynamic obstacles in the path planning process is improved, the collision risk in the path planning is reduced, and the path planning efficiency is improved. And the self-adaptive capability of the arm path planning complex environment of the intelligent humanoid robot with the body is improved.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention relates to a psychological counseling interaction method and device based on an autonomous psychological planning architecture. The method comprises the following steps: acquiring initial user data of a target user; constructing a dynamic target map of a target user through a three-layer nested target structure, and pre-loading a matched personalized decision tree; after receiving the real-time interaction data, executing real-time path re-evaluation based on the real-time interaction data to obtain a treatment value function of each intervention path; constructing an intervention response prediction model by adopting a Bayesian network and a Monte Carlo tree search algorithm, adjusting the priority and the execution sequence of the intervention paths in the personalized decision-making tree in combination with the treatment value function of each intervention path, and selecting a target intervention path matched with the real-time interaction data from the adjusted intervention paths; the real-time interaction data is mapped into the multi-dimensional psychological state space to serve as the session trajectory, session trajectory planning is conducted based on the target intervention path, real-time interaction with the target user is achieved, and the accuracy of intervention decision making and the session fluency are improved.

The invention relates to flight path planning of aircrafts, in particular to a collaborative flight path planning method and system for a low-altitude multi-aircraft mixed take-off and landing field, and the method comprises the steps: carrying out the three-dimensional subdivision of an airspace based on a Beidou grid code, and carrying out the real-time mapping of an airspace state of a three-dimensional grid unit; dividing the three-dimensional grid units according to the grid state; constructing a geometric track generation rule, and eliminating a track crossing risk; introducing a time dimension on the basis of a three-dimensional space, carrying out four-dimensional space-time conflict detection on all tracks, and carrying out conflict resolution according to a four-dimensional space-time conflict detection result; a double-queue dynamic scheduling mechanism is constructed, and the aircraft is guided to land safely through queue separation and polling distribution; according to the technical scheme provided by the invention, the defects that the avoidance path of the low-priority aircraft is difficult to reasonably plan and the flight safety of the aircraft in the take-off and landing stage cannot be effectively guaranteed in the prior art can be effectively overcome.

The invention belongs to the technical field of unmanned aerial vehicle autonomous navigation, provides an unmanned aerial vehicle trajectory planning method and an unmanned aerial vehicle design applying the method, and aims to realize real-time and efficient environmental perception and unmanned aerial vehicle autonomous obstacle avoidance trajectory generation. A group of primitive sets is predefined in a three-dimensional state space to explore the whole search space so as to realize complete coverage of a feasible region, multi-mode perception input of'depth image, current state and target direction 'is adopted, and the depth image is acquired by a depth camera; the current state is obtained by the airborne vision positioning module; multi-modal sensing input is processed by a deep learning network, future expected position, speed and acceleration information is calculated according to output of the deep learning network and serves as input of a bottom layer controller of the unmanned aerial vehicle for trajectory tracking, and finally obstacle avoidance flight in a complex environment is achieved. The method is mainly applied to unmanned aerial vehicle design and manufacturing occasions.

The invention provides a well drilling automatic control system which comprises an input establishing module used for inputting well track design parameters and establishing a well drilling system dynamic model; the target trajectory planning module is used for verifying stratum parameters according to the actual drilling data and planning the target trajectory of the current drilling section based on the verification result by using the drilling system dynamics model to obtain the target trajectory; the control quantity feed-forward calculation module is used for calculating the control quantity of the drilling machine by adopting a feed-forward algorithm based on the target track parameters, controlling the drilling real-time calibration module of the drilling machine, and correcting the control quantity in real time based on real-time evaluation of drilling errors in the drilling process; and the data output module is used for outputting logging data in the drilling process of the column after the preset drilling distance of the column is reached. Full-intelligent operation in the drilling process is achieved, and the precision, safety and oil and gas recovery efficiency of drilling operation are effectively improved.

The invention provides an industrial multi-robot intelligent collaborative planning method based on deep learning. The industrial multi-robot intelligent collaborative planning method comprises six parts including environment modeling, feature extraction, task allocation, trajectory planning, control instruction generation and rule distillation. The method comprises the following steps: acquiring multi-robot environment information by constructing a probability grid map and a topological structure, and extracting state and task features to form a comprehensive feature matrix; training an optimal task allocation strategy by adopting deep reinforcement learning, and combining CVAE and CEM joint modeling to optimize trajectory generation; an adaptive impedance controller based on MADDPG is further designed, and dynamic adjustment of interaction parameters is achieved; and finally, the control strategy is converted into a decision rule set through knowledge distillation, the control interpretability is improved, and the deployment complexity is reduced. According to the invention, the task cooperation efficiency and the control stability of the multi-robot system in a complex industrial environment can be effectively improved.

The invention discloses a spraying robot trajectory planning method based on depth camera scanning. The method comprises the steps that wall surface boundary polygon data, an initial spraying stroke set and a spraying dosage model parameter set are obtained; calculating a predicted coating thickness field, and performing difference calculation on the predicted coating thickness field and a preset target thickness to generate a thickness error field; performing connected domain clustering on the thickness error field to generate topological thickness error regions, and determining region type labels for the topological thickness error regions one by one; calling a matched editing operator from a discrete stroke editing operator library, performing geometric constraint verification, and generating a candidate editing scheme; and evaluating the candidate editing schemes based on a preset comprehensive scoring function, updating the initial spraying stroke set by using the scheme with the optimal score, obtaining an optimized spraying stroke set, and converting the optimized spraying stroke set into a robot control instruction. According to the method, the problem that global coverage and local thickness uniformity cannot be considered in traditional geometric planning is solved, and high-quality full-automatic spraying is achieved.

The invention relates to a memory enhanced vision-language-action submerged space dynamic fusion automatic driving method. Comprising the following steps: generating a bird's-eye view feature map; extracting scene, agent and map marks from the aerial view feature map, and fusing the mark at the current moment and the previous i historical marks to generate memory enhanced visual marks; the memory enhanced visual mark and the vehicle state information are converted into a submerged space, text input of a driver is marked and unified into the submerged space, the submerged space is represented and fused, and fusion representation is generated; an automatic driving instruction data set is introduced for adjustment, and a large language model adapting to an automatic driving task is obtained; according to the fusion representation, track planning is carried out in an autoregression mode by using a large language model, and path point coordinates are obtained; and designing a transverse controller and a longitudinal controller based on PID (Proportion Integration Differentiation) to track and control the coordinates of the path points. According to the invention, the visual representation capability of end-to-end driving is improved, and the visual-language-action fusion effect is improved.

The invention provides an intelligent spraying composite robot system based on a large language model and a method thereof, and relates to the field of intelligent robots and automatic spraying, the intelligent spraying composite robot system comprises a chassis, a mechanical arm, a spraying gun, a camera, an instruction receiver and a controller; a mechanical arm and an instruction receiver are carried on the chassis; spray guns and cameras are mounted on the mechanical arms; the instruction receiver receives a natural language voice instruction, analyzes semantics through a large language model, and generates an action instruction; the controller comprises a motion control module, a three-dimensional point cloud data acquisition module, a feature point extraction module, a track planning module and a spraying operation module; the method improves the man-machine interaction, reduces the use threshold of the robot, is easy to operate, gives play to the spraying advantages of the composite robot, does not need to program in mainstream offline simulation software, can generate a spraying path with adjustable and controllable parameters only by collecting the point cloud template of the workpiece and selecting the feature points, and can be widely applied to furniture, furniture and the like. And spraying operation in the fields of automobiles, aerospace and the like.

The invention discloses an aircraft intelligent trajectory planning and cooperative control method based on offline optimization-intelligent learning-online guidance, and the method comprises the steps: firstly building an aircraft motion model and an aircraft-target relative motion model, and constructing a full-airspace and full-feature-point optimal trajectory data set; secondly, parameterized representation is carried out on the three-dimensional optimal trajectory, and a proportional guidance coefficient data set is constructed; then neural network training fitting is carried out on the proportional guidance coefficient data set, and a multi-constraint guidance method based on a neural network is designed; and finally, designing a three-dimensional collaborative guidance law by combining a multi-constraint guidance method and a residual time accurate estimation technology. According to the invention, the problems of insufficient real-time performance, incapability of meeting index optimality, incapability of coping with multiple constraints, difficulty in realizing multiple flight modes and the like of the existing trajectory planning and collaborative guidance technology are solved; the multi-constraint and optimality of the trajectory are realized through offline optimization, the global adaptability and small calculation amount of the trajectory are realized through intelligent learning, and a multi-cooperative flight mode is realized through online cooperative guidance.

The invention discloses a cooperative positioning and trajectory planning method based on an air-ground robot system, and the method comprises the steps: enabling unmanned vehicles to construct a global environment map through a laser radar, achieving the positioning of the unmanned vehicles, obtaining the distances between the unmanned vehicles through the combination of an IMU (Inertial Measurement Unit) and a UWB (Ultra Wideband) distance measurement chip of the unmanned vehicles, and carrying out the real-time pose estimation. Based on the fused pose and global map information, a method of combining mixed A * search and nonlinear trajectory optimization is adopted, a collaborative trajectory planning problem is converted into a multi-constraint optimization model, the sight distance quality between air-ground robots, an unmanned vehicle team-shaped geometric structure and an unmanned aerial vehicle rapid navigation problem are comprehensively considered, and the space-ground robot cooperative trajectory planning method is established. And generating a collision-free optimized cooperative trajectory meeting multi-robot kinematics constraints in real time. According to the method, the positioning precision and the coordination degree of the air-ground coordination system in a complex dynamic environment are remarkably improved, and a safe, efficient and real-time heterogeneous multi-robot trajectory planning method is provided for various application scenes such as routing inspection, search and rescue, environment monitoring and the like.

The invention discloses an enamel product and an intelligent spraying system thereof, and belongs to the technical field of enamel product spraying, and the enamel product comprises a high-precision visual guidance module, an environment intelligent control module and an auxiliary function module, the intelligent environment control module is used for monitoring and dynamically regulating and controlling parameters such as temperature and humidity and dust concentration of a spraying environment in real time, ensuring stable coating quality and reducing energy consumption, and the auxiliary function module is used for realizing accurate spraying track planning and quality control through process connection, safety guarantee and data collaboration. And high efficiency and reliability of the spraying process are guaranteed. On the basis that spraying of the enamel product is achieved, the quality of the whole process is improved, and comprehensive benefits can be optimized.

The invention belongs to the technical field of parallel robot controllers, and discloses a high-performance parallel robot controller based on an arm + fpga architecture, through deep heterogeneous fusion of an ARM and an FPGA, the control period is shortened to be within 10 microseconds, the trajectory tracking error is controlled to be 0.1 mm or below, and the performance bottleneck of a traditional architecture in a high-speed scene is solved; the multi-core ARM undertakes complex tasks such as global trajectory planning and dynamics solution, and realizes parallel processing by means of an NEON instruction set; the FPGA fully releases the hardware parallel characteristic of the FPGA, real-time tasks such as multi-axis motion control and sensor data fusion are synchronously completed through a distributed logic unit, and a complex decision-real-time execution assembly line cooperation mode is formed. Inertial parameters and load changes of the mechanical arm are estimated in real time through an LSTM neural network, and feedforward compensation is carried out on interference such as mechanical vibration and load abrupt change in combination with an extended state observer achieved through FPGA hardware; the innovatively designed double closed-loop control architecture supports seamless switching between a force control mode and a position control mode.

The invention discloses a fixed-wing unmanned aerial vehicle trajectory tracking control system and method based on a disturbance observer. The system acquires various state data of the unmanned aerial vehicle through the data acquisition module, the interference observer module estimates external interference in real time based on a dynamic model, the trajectory planning module generates an optimized trajectory according to tasks and environments, and the controller module realizes accurate control by adopting a model prediction control and sliding mode control composite strategy. And the execution mechanism driving module executes the control instruction. The method comprises the steps of data acquisition, interference estimation, trajectory planning, controller design and calculation, actuating mechanism driving and the like. The method can effectively improve the trajectory tracking accuracy and stability of the fixed-wing unmanned aerial vehicle in a complex environment, and has a wide application prospect.

The invention discloses an automatic driving planning method, device and equipment for a two-wheeled mobile robot and a storage medium, and the method comprises the steps: collecting environment data and vehicle body posture information through a multi-source sensor, and constructing a space-time joint map under a dynamic aerial view coordinate system; a hierarchical trajectory planning architecture is designed based on the four-dimensional state space, an initial path is generated by adopting an improved algorithm, and space-time joint optimization is performed through quadratic programming; a smooth trajectory meeting dynamic constraints is generated in combination with a differential flatness parameterization method, and accurate execution of attitude and motion instructions is realized based on feedforward-feedback cooperative control. According to the method, the problem that the trajectory is not feasible due to neglect of the inclination angle in the traditional automatic driving planning of the two-wheeled vehicle is solved, and the motion stability and safety in a dynamic scene are improved.

The invention discloses a dynamic grabbing method and system, and relates to the technical field of automatic manufacturing. The dynamic grabbing method comprises the following steps that S1, positioning and pose recognition are conducted on to-be-grabbed parts based on a deep learning model, and the to-be-grabbed parts are divided into grabbable parts or non-grabbable parts based on pose recognition; s2, calculating an affine transformation matrix based on camera calibration to convert a camera coordinate system into a robot coordinate system; s3, constructing a part track motion model based on the multi-frame image, and predicting a grabbable part track based on the part track motion model; s4, the robot implements a grabbing action and obtains feedback information of the grabbing action, and track planning parameters or time compensation values are adjusted based on the feedback information; realizing time synchronization of the camera and the robot based on the time compensation value; and the optimal adsorption position and speed are calculated according to the predicted grabbable part track and the preset triggering time, so that the movement of the robot is more efficient and smoother.