590 results about "Trajectory optimization" patented technology

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 relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention discloses a robot dog inspection path intelligent planning and dynamic adjustment method, system and device and a medium, and belongs to the technical field of robot path planning, and the method comprises the steps: calculating a task emergency degree index according to a weight coefficient of an inspection point and a time constraint, and determining a task priority; constructing a hierarchical electronic map containing the terrain difficulty coefficient and the moving cost; performing global path planning through a comprehensive cost function by adopting an A star algorithm; environment information is collected in real time through multiple sensors, and the dynamic obstacle layer is updated; performing real-time trajectory optimization by adopting a local path planning mode; selecting local path correction or global path re-planning according to the path execution state score; energy needed for completing the remaining tasks is predicted, and a charging path is planned when necessary. According to the invention, multi-target adaptive optimization of path planning is realized, a coordination mechanism of global planning and local adjustment is established, and the execution efficiency and reliability of inspection tasks are improved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses a vehicle trajectory planning method and device and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: obtaining vehicle state information of a target vehicle, a reference trajectory and environment perception data containing available traffic width, and performing trajectory optimization processing according to the vehicle state information, the reference trajectory and the environment perception data to obtain a trajectory optimization result of the reference trajectory; in the trajectory optimization processing process, taking a first passing cost required for minimizing a trajectory optimization result as a target; a passing width cost item in the first passing cost is a power function taking the residual passing width as an independent variable, and the passing width cost item is increased along with the reduction of the residual passing width; the remaining passing width is the difference between the available passing width and the vehicle width of the target vehicle. Therefore, in a narrow traffic scene, the traffic width cost item is increased in a super-linear manner, the constraint strength on the transverse position of the track is enhanced, the generation of a high-risk track excessively close to an obstacle is effectively avoided, the planning interruption frequency is reduced, and the trafficability of the vehicle in the narrow scene is remarkably improved.

The invention relates to the technical field of industrial robot control, and discloses a robot end trajectory optimization method and system based on industrial vision, and the method comprises the steps: obtaining a workpiece surface image in real time through a vision sensor, and obtaining deformation data through image enhancement and feature extraction; when the deformation quantity exceeds a threshold value, calculating a multi-axis coordination parameter by adopting an optimization algorithm to generate a trajectory correction instruction; integrating speed constraints by updating a control model, and determining a final trajectory by using Kalman filtering to fuse sensor feedback data; and the control parameters are iteratively adjusted in the deformation feedback loop, and a stable machining track is formed. The method can solve the problem that in the prior art, the robot tail end track precision is low.

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The invention discloses an automatic guided vehicle path optimization method and system based on fusion planning, and relates to the field of robot technology, computer science and automation control, and the method comprises the steps: carrying out the global path optimization based on an improved A star algorithm according to a two-dimensional grid map, and obtaining a global optimal path and a key path point set; performing local path planning based on an improved dynamic window algorithm according to the global optimal path and the key path point set, and obtaining a local obstacle avoidance track and an optimal speed control instruction; and based on the optimal speed control instruction and in combination with the local obstacle avoidance trajectory, obtaining an automatic guided vehicle path optimization result. According to the method, on the global planning level, the path length and the number of redundant nodes are remarkably reduced, the steering frequency is effectively reduced, and the path smoothness and the performability are improved; in a local planning level, efficient obstacle avoidance and real-time trajectory optimization in a dynamic obstacle environment are realized.

The present invention relates to a collaborative robot system used in an industrial environment where workers collaborate. The collaborative robot system comprises: a collaborative robot body having a multi-joint structure; a control unit for controlling each joint or auxiliary shaft; one or more sensors for detecting the operational state of the robot in real time; and a status diagnosis and response module for diagnosing the state of the robot on the basis of detected data and controlling the operation of the robot accordingly. Specifically, the system is configured to enable the robot to autonomously perform actions such as deceleration, stopping, and recovery in response to various state changes occurring during work, and to perform self-learning and trajectory optimization on the basis of data accumulated through repetitive tasks. Furthermore, the system includes a graphical user interface (GUI) for intuitive user interaction, which is linked to a digital twin-based virtual simulation environment, enabling presetting and modification of work paths. When multiple collaborative robots are operated together, the system enables task synchronization, path collision avoidance, and sharing of status information among the robots, and may be connected to an external control server or a cloud-based control system to allow integrated management of the entire workflow.

The invention provides an unmanned aerial vehicle cluster dynamic hunting method for a complex three-dimensional scene. The method comprises the steps that the state of an escaper unmanned aerial vehicle, the state of each hunting unmanned aerial vehicle and the environment state are obtained respectively; sub-targets are generated around the escaper unmanned aerial vehicle through a Fibonacci ball algorithm, and the sub-targets are dynamically adjusted; based on the dynamically adjusted sub-targets and the state of each trapper unmanned aerial vehicle, optimal sub-target distribution is carried out on each trapper unmanned aerial vehicle through an improved market auction algorithm, wherein the improved market auction algorithm considers path cost and angle cost in a cost function; constructing a dynamic target control obstacle constraint based on the environment state, the state of the escaper unmanned aerial vehicle and the state of each trapper unmanned aerial vehicle; an escaper position change factor and a self-adaptive attenuation rate are introduced into the dynamic target control obstacle constraint; and constructing a distributed MPC optimization problem based on optimal sub-target distribution and a dynamic target control barrier function to perform trajectory optimization and motion control. According to the invention, the efficiency and the safety are ensured at the same time.

Techniques are discussed herein for determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and / or the optimal trajectory for controlling the vehicle in the driving environment.

The invention relates to the technical field of intelligent driving assistance systems, in particular to a vision-assisted high-precision lane-level navigation system which comprises a visual perception module, a geometric modeling module, a trajectory optimization module, a dynamic obstacle avoidance module and an execution control module. Establishing a lane local coordinate system based on arc length parameterization, and accurately capturing road geometric characteristics; state representation is constructed in a Riemannian metric space, an optimal track is selected by calculating geodesic line deviation measurement, and the generated track is made to better conform to road geometric characteristics; compared with the prior art, the method has the advantages that the lane-level positioning with centimeter-level precision can be realized, the driving track with smoother curvature change can be generated, and the driving comfort can be obviously improved.

The invention discloses an unmanned aerial vehicle cluster relay system optimization method, and aims to significantly reduce the overall outage probability of the system by performing adaptive optimization on cooperative position deployment and flight trajectories of multiple unmanned aerial vehicles under multiple actual constraint conditions of flight speed, safety distance, flight boundary and the like. Therefore, the reliability and the service quality of the multi-unmanned aerial vehicle relay communication network are improved. According to the method, a Markov decision process (MDP) model is constructed, a multi-unmanned aerial vehicle trajectory optimization problem is converted into a multi-agent reinforcement learning problem, and efficient optimization of unmanned aerial vehicle trajectories is realized by using a centralized training-distributed execution framework, so that the outage probability of a communication system is minimized.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The invention relates to the technical field of intelligent monitoring of engineering hoisting, in particular to an intelligent monitoring system for an overweight segmented hoisting process based on BIM (Building Information Modeling) and force feedback, which comprises a data synchronization module, a state fusion module, a BIM matching module, a track optimization module and a simulation monitoring module. The system obtains a hoisting state through multi-source sensing data, matches a BIM fine model, and generates a theoretical motion track. And the trajectory optimization module generates a force feedback deviation field by comparing expected force feedback data with actual force feedback data, reversely corrects a theoretical trajectory, and realizes dynamic path optimization based on a mechanical state. And the simulation monitoring module drives virtual simulation according to the optimized trajectory, updates the posture of the BIM model in real time, dynamically calculates an interference area, performs risk assessment, and automatically generates and issues a control instruction. According to the scheme, the response precision and the active safety control capability of the hoisting process to the physical state change are improved.

The invention discloses an underground mine positioning and mapping trajectory optimization method based on underground control point constraint, and belongs to the field of positioning and mapping, and the method comprises the steps: respectively obtaining GNSS data, laser data, IMU data and image data; according to the GNSS data, the laser data, the IMU data and the image data, completing initialization of a laser radar-vision-inertial odometer system and GNSS initialization; extracting point cloud features of the distributed underground control points in real time based on an RANSAC algorithm, and generating a global coordinate constraint factor of nonlinear least square optimization; and performing relative attitude calculation according to the global coordinate constraint factor to complete underground mine positioning and mapping trajectory optimization. According to the method, the problems of accumulated error overrun and point cloud dislocation layering easily caused by pose drift in a special environment of an underground mine are solved.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention discloses a dragging positioning spraying track planning method for a local area of the surface of special equipment and a storage medium, and belongs to the technical field of industrial robot automation. The method comprises the steps of obtaining point set data of a to-be-sprayed area; calculating a convex hull based on the point set, and solving a minimum enclosing rectangle by using a rotary caliper method to determine an accurate rectangle spraying boundary; constructing a rectangular amplitude limiting sedimentary model through spraying testing and parameter identification; generating a grid track with a buffer area based on the model and the boundary, and optimizing the spraying speed by taking the minimization time as a target; and for the curved surface, performing B-spline curved surface reconstruction and extracting a normal vector to control the attitude of the spray gun. According to the method, the complex multi-parameter trajectory optimization problem is simplified into single-speed optimization through the rectangular amplitude limiting deposition model, the problems that in local spraying, trajectory planning is complex, edge control is not precise, a coating is not uniform and the like are effectively solved, and the spraying efficiency and quality are remarkably improved.

The embodiment of the invention relates to the technical field of ultrahigh frequency passive RFID, in particular to an intelligent storage RFID robot reading perception and trajectory optimization method and system. According to the method, the position coordinates of the goods shelf are obtained by building the model and utilizing the label characteristic values, the interaction state of the UHF RFID robot and the goods shelf can be sensed, the distribution state of the goods is further sensed, and the number and the position of the missed goods are estimated. Furthermore, the advancing track of the UHF RFID robot system in the warehouse management scene is designed and optimized according to the sensing result, so that the reading accuracy and the checking efficiency of the mobile RFID system are improved.

The invention discloses an unmanned aerial vehicle autonomous flight control method and a related device. The method comprises the steps of obtaining environment map information and unmanned aerial vehicle information; based on the environmental map information and the unmanned aerial vehicle information, obtaining a global optimal path by using a fusion dynamic exploration path planning algorithm; taking each path section in the global optimal path as a center to generate a plurality of ellipsoid areas covering the path sections, converting the boundary of each ellipsoid area into a convex polyhedron formed by a plurality of hyperplanes, and connecting the convex polyhedrons in series according to the sequence of the path sections to obtain a safe flight corridor; in the trajectory optimization process, the safe flight corridor is used as a constraint basis of space feasibility, it is ensured that the trajectory is always located in the safe flight corridor, and space constraints are determined; and carrying out trajectory tracking by adopting a DLQR controller based on the spatial constraint. According to the invention, efficient cooperation of path planning and trajectory tracking is realized.

The invention belongs to the technical field of computer vision, and particularly relates to a multi-view image three-dimensional reconstruction method, which comprises the following steps of: firstly, inputting a small amount of two-dimensional images, generating a color point cloud through depth estimation, initializing a three-dimensional Gaussian model, and performing coarse-grained reconstruction by combining luminosity loss and geometric regularization; secondly, planning a camera track based on geometric distribution of the rough model, and collecting a multi-view image sequence; thirdly, performing fine adjustment and repair on the image by using a diffusion model fusing the camera pose and the visual features; and finally, iteratively optimizing the three-dimensional Gaussian model by using the repaired image, and outputting a high-precision three-dimensional reconstruction result. According to the method, high-quality reconstruction can be achieved only through sparse view angle images, and dependence of a traditional method on a large amount of data is broken through. Through composite condition coding and dynamic trajectory optimization, geometric consistency and texture authenticity are effectively improved, artifacts are avoided, and calculation efficiency and reconstruction precision are considered at the same time.

The invention discloses an unmanned aerial vehicle trajectory planning method based on collision probability in a dynamic environment, and the method comprises the steps: obtaining an obstacle avoidance front-end search path from a look-ahead point of a current position of an unmanned aerial vehicle to a rolling local target point based on the current information of the unmanned aerial vehicle; performing parameterized expression on the continuous time trajectory of the unmanned aerial vehicle, constructing a trajectory optimization problem model, and inputting the obstacle avoidance front-end search path as a reference initial value into the trajectory optimization problem model to obtain an optimized trajectory; the collision probability of collision between the optimized track and the dynamic obstacle is determined; and based on the collision probability, performing adaptive trajectory re-planning on the optimized trajectory, taking a point C meeting a first preset condition on the optimized trajectory as a re-planning starting point, and determining the motion trajectory of the unmanned aerial vehicle at the current time step. According to the invention, the safety and real-time performance of the track in a high dynamic environment are ensured.

The invention provides a trajectory planning method and device based on a space-time corridor and an automatic driving vehicle, relates to the technical field of computers, in particular to the fields of automatic driving, path and trajectory planning and the like, and can be used for trajectory planning, obstacle modeling and other application scenes. According to the specific implementation scheme, a space-time semantic corridor is constructed in a three-dimensional space-time coordinate system according to semantic information of a road reference line and a driving environment, and a motion track to be optimized is generated; matching a space-time corridor unit corresponding to the coordinates of the current track point from the space-time semantic corridor according to the to-be-optimized motion track, and constructing corridor boundary constraints based on the space-time corridor unit and the punishment cost; according to the road reference line and the motion trail to be optimized, constructing a trail following cost; constructing a trajectory optimization model according to the corridor boundary constraint, the trajectory following cost and a preset vehicle kinematics constraint; and solving the trajectory optimization model by using a hierarchical optimization algorithm to obtain a planned trajectory. According to the scheme, the trajectory planning speed and quality can be improved.

The invention relates to the technical field of motion control, in particular to a motion trail real-time optimization analysis method and system based on multi-modal perception, and the method comprises the following steps: a multi-sensor array obtains position and speed visual features, carries out the coordinate transformation, extracts a direction amplitude, and builds a multi-modal perception data set; the method comprises the following steps: inputting Kalman filtering prediction trajectory comparison deviation to generate a trajectory deviation vector field, calculating a deviation gradient, screening an over-threshold point marking time sequence, generating a dynamic weight correction parameter through weighted least square distribution normalization, and outputting a real-time optimization trajectory scheme through weighted correction fusion coordinates. The position information and the velocity vector of a moving target are obtained in real time by fusing multi-modal sensing data, a state estimation model is combined to predict a trajectory, a trajectory deviation vector field is generated, and a deviation point is subjected to weighted correction, so that the trajectory optimization precision is effectively improved, the weight is dynamically allocated to optimize the priority, and the trajectory optimization precision and the system stability are improved. And the self-adaption and robustness in a complex environment are enhanced.

The invention discloses an electric linear servo system control method based on cascade LESO-DISMC. The method comprises the following steps: firstly, establishing a complete mechanism model of a permanent magnet synchronous motor servo loading system; then designing DOB interference observer real-time compensation by adopting a LuGre friction model and a gap dead zone model; a feedforward control quantity is constructed by utilizing a measurable displacement signal, and redundant torque is inhibited; designing a dynamic integral sliding mode controller to ensure global stability; a high-low bandwidth LESO is constructed, and both fast tracking and smooth filtering are considered; and finally, trajectory optimization is carried out, a smooth trajectory is generated by adopting polynomial interpolation for the step and triangular wave signals, and mechanical vibration is inhibited. According to the method, a LuGre friction model is adopted to compensate nonlinear friction, cascade linear LESO and sliding mode control are combined, a track is optimized through an S-shaped curve, and the problem of'differential explosion 'is solved; according to the method, instruction smoothing, core control and friction compensation are fused, and the anti-interference capability and the loading precision of the system are improved.

The invention relates to the technical field of intelligent traffic and artificial intelligence, and discloses an end-to-end automatic driving method and system based on multi-modal attention fusion, and the method comprises the steps: collecting multi-modal data; generating an initial semantic text and calibrating the initial semantic text to form a calibrated semantic text; performing feature processing on the environment image and the point cloud data acquired by the multi-view camera, performing cross-modal alignment on the calibrated semantic text and the first BEV feature, and performing spatial-temporal feature modeling; generating candidate trajectories, quantifying the collision risk of each candidate trajectory and the dynamic obstacle, screening out low-risk trajectories, and guiding the low-risk trajectory optimization through a rule mask; mapping the optimized track into a control instruction; according to the method, a CLIP cross-modal alignment mechanism is utilized, text features and BEV geometric features are deeply fused, and the recognition accuracy of the key region is improved.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses a low-altitude unmanned aerial vehicle logistics intelligent path planning and scheduling method, which comprises the following steps: acquiring urban environment space data, then establishing an unmanned aerial vehicle dynamics model, satisfying a given physical constraint condition, constructing a path cost function, and establishing a path cost function based on an optimal control theory. And performing time-space discrete processing on the trajectory optimization problem, and solving by adopting a numerical method to obtain an optimal state trajectory and control input, thereby obtaining a discrete trajectory, generating a continuous control instruction sequence for the unmanned aerial vehicle flight control system, obtaining a control trajectory, and taking the control trajectory as a task scheduling basis. And distributing logistics tasks among the plurality of unmanned aerial vehicles according to a preset priority strategy. A dual-stage task scheduling structure is introduced, an adaptive unmanned aerial vehicle set is screened firstly, and then path cost minimum matching is executed, so that effective compression of a scheduling solution space and remarkable improvement of matching efficiency are realized, and a scheduling effect still having high real-time performance and high feasibility in a multi-task concurrent scene is obtained.

The invention provides a mobile robot vibration sensing trajectory optimization method based on a physical information neural network, and the method comprises the steps: obtaining an RGB image and a depth image through a foresight camera, extracting semantic information of a topographic type and geometric information representing the topographic relief degree, and constructing an obstacle navigation cost map based on the semantic information and the geometric information; predicting the vibration influence of the terrain on the robot suspension system through a physical information neural network based on the semantic information, the geometric information and the robot state data so as to generate a vibration prediction navigation cost map; obtaining a final navigation cost map through map fusion; generating a local minimum cost trajectory on the final navigation cost map by using a trajectory planning algorithm; controlling the robot to run along the local minimum cost track; and a two-stage scheme is adopted in the training process of the physical information neural network to accelerate network training. Therefore, the hardware cost can be reduced, the physical information neural network training can be accelerated, the physical feasibility and smoothness of the trajectory can be improved, the tracking error can be reduced, and the safe and stable operation of the robot in the unstructured environment can be ensured.