1100 results about "Trajectory optimization" patented technology

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of robot strategy training, financial science and technology, medical treatment and health and the like, and discloses a strategy optimization method and device based on an interaction track, equipment and a medium. The reinforcement learning agent interacts with the environment to generate an action and state sequence and record an interaction track, an optimal action strategy is generated based on a track optimization strategy, an original empirical data set is further generated, and a pre-training model is optimized through supervised learning to obtain a target strategy model. According to the method, a task-related high-quality trajectory is generated through reinforcement learning agent and environment interaction, original empirical data is extracted on this basis, and a pre-training strategy model is optimized in combination with a supervised learning mechanism, so that the sample utilization efficiency is effectively improved, and the generalization ability and execution robustness of the model under a multi-task condition are enhanced.

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

The invention relates to the technical field of intelligent laser processing path planning, and discloses a laser flight processing path planning method and device for a complex curved surface, and the method comprises the steps: obtaining the geometric continuity features of the curved surface, carrying out the region division and boundary feature extraction, obtaining the geometric features of the boundary points of each region, and obtaining the geometric features of the boundary points of each region; performing track generation based on boundary geometric features to obtain an initial processing track path, then performing heat accumulation analysis and track screening, identifying a track section needing to be adjusted, executing multi-target path scheduling optimization, and obtaining adjusted track execution feature data; processing sequence optimization is carried out based on the feature data, and an optimized processing sequence is obtained; performing thermal input analysis and parameter correction according to the processing sequence to obtain an updated processing parameter set; and finally, performing global coordination based on a simulated annealing algorithm, generating a final processing track set, completing track optimization and instruction set generation, and outputting a complete processing scheme. According to the method, the problem of insufficient track fitting degree can be solved.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

The invention discloses an unmanned aerial vehicle flight path optimization system and method based on artificial intelligence and Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting communication delay, environment data and an unmanned aerial vehicle state in real time; analyzing historical communication delay data by using a Markov chain, and predicting a basic delay value in a short time in the future; correcting the basic delay value through a machine learning model; outputting a final delay prediction result; calculating a delay risk index, and judging a delay risk level; based on the delay risk level, when the delay risk level is low risk, sending a control instruction in advance according to the final delay prediction result; and when the delay risk level is medium or high risk, calling a pre-trained reinforcement learning strategy library, and generating a self-adaptive control instruction. According to the method, the multi-dimensional risk index is constructed, multiple strategies are provided to adjust the flight path according to the real-time data and the risk index, and the flight stability of the unmanned aerial vehicle is improved.

The invention relates to the technical field of image processing, and provides an image target tracking method and device based on deep learning, and the method comprises the steps: obtaining an original frame sequence of a target video, carrying out the adaptive frame screening of the original frame sequence, and obtaining a plurality of feature frame sequences, key frames and common frames, performing feature extraction and feature mapping on all the feature frame sequences to obtain target trajectory nodes, performing trajectory optimization calculation on the key frames and the common frames by using all the target trajectory nodes to obtain key frame trajectories and common frame trajectories, and inputting the key frame trajectories and the common frame trajectories into a preset deep learning model to perform trajectory reconstruction optimization so as to obtain a target trajectory; and obtaining a tracking target trajectory. By performing adaptive frame screening on the original frame sequence and generating the feature frame sequence, the key frame and the common frame, the data processing efficiency and the information extraction accuracy are improved, the accurate generation of the target motion track is realized, and the problems of reduced tracking accuracy and delayed system response in a complex scene are solved.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

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 key safety scene generation system for an automatic driving automobile based on a diffusion model. The key safety scene generation system is characterized in that a traffic module performs de-noising processing on a noise track through the diffusion model and generates real diversified traffic scenes; the key safety scene generation module calls a mixed guidance function in each step of denoising process of the diffusion model to guide an opponent agent to collide with a self-agent so as to generate an antagonistic key safety scene; the expert trajectory optimization module performs multiple iterative optimization on the target function through an Adam optimizer to generate a safe and real expert trajectory; the planner enhancement module carries out fine adjustment on a pre-trained planner through the generated key safety scene and the expert track thereof, so that the ability of the planner to cope with extreme scenes is improved; the safety and robustness of the automatic driving planner in an extreme environment are improved by generating a real, diversified and antagonistic key safety scene.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention belongs to the technical field of air-space-ground integrated networks, and discloses an unmanned aerial vehicle track optimization and task offloading method for an air-space-ground integrated network. According to the method, the task unloading efficiency in the air-space-ground integrated network is optimized, optimization of energy consumption and time delay is focused on, various conditions of the air-space-ground integrated network are comprehensively considered, a decoupling communication mode is considered, and the influence of service interruption is reduced. And constructing an optimization model to minimize the weighted sum of time delay and energy consumption. In order to improve the collaboration of heterogeneous agents in a space-air-ground integrated network, based on a hierarchical reinforcement learning framework, a hierarchical learning-based two-stage Qmix algorithm is proposed, and the collaboration of a low-orbit satellite and an unmanned aerial vehicle is reinforced. According to Pycharm platform simulation and multi-scene verification, the method has remarkable advantages in average response time delay, energy consumption and QoS indexes, the network performance is effectively improved, and user requirements are met.

The invention discloses a ship target identification tracking method and system based on multi-source data fusion, and relates to the field of electric digital data processing, and the method comprises the steps: collecting ship monitoring data of a multi-source sensor, including position coordinates, area and color features; generating an initial ship trajectory according to the monitoring data, calculating the speed, area and color similarity of a detection point, and associating to obtain a trajectory pair; carrying out abnormal point identification and trajectory optimization based on a similarity energy function containing options such as dynamics and continuity; calculating the state difference between the optimized trajectories to perform similarity test, and judging the trajectories exceeding a threshold value as the same target to form a multi-source trajectory group; and calculating a dynamic fusion weight according to the error standard deviation of the trajectory relative to the average trajectory, and performing weighted fusion to obtain a target trajectory. By implementing the method, the fusion precision of the ship target trajectory can be improved.

The invention belongs to the field of robot motion control, and discloses an online generation and control method and system for whole-body coordinated walking motion of a humanoid robot, a computer readable storage medium and a computer program product. According to the method, in walking motion generation, a whole-body walking motion track including upper body joint motion information is generated in real time by adopting a whole-body kinematics-centroid dynamics model; a model predictive control model is adopted to realize organic combination of motion trail generation and real-time tracking control; and solving the model prediction control model, and taking the optimal state x * and the corresponding control input u * as the input of whole body control. According to the method, in the planning stage, namely the whole-body walking motion trail including upper body joint motion information, the whole-body dynamic characteristics of the arms, the waist and the like are comprehensively considered, and a new method of long-time-domain trajectory optimization and online control is combined, so that real whole-body coordinated walking is fundamentally realized.

The invention relates to the technical field of aluminum plate processing, and discloses an aluminum plate processing cleaning intelligent monitoring system, which comprises a three-dimensional deformation sensing module, a surface residue detection module, a thermal stress distribution analysis module, a vibration spectrum identification module, a processing track optimization module, an environmental parameter fusion module, an intelligent cleaning regulation module and the like. The three-dimensional deformation sensing module realizes aluminum plate surface three-dimensional reconstruction and deformation characteristic spectrum generation through an industrial camera and an algorithm; the surface residue detection module identifies residue distribution and classification by using a laser speckle interferometer and the like; the thermal stress distribution analysis module inverts thermal stress by means of an infrared focal plane array; the vibration spectrum identification module analyzes the equipment vibration signal; the processing track optimization module generates an optimized track based on an ant colony algorithm and the like; the environmental parameter fusion module generates a fusion decision index through data dimension reduction and correlation analysis; and the intelligent cleaning regulation and control module combines feedback and learning to optimize cleaning parameters. The system achieves multi-dimensional monitoring and intelligent cleaning regulation and control, and the machining precision and efficiency are improved.

The invention provides a deep learning-based ship motion trajectory optimization control method, which relates to the technical field of ship control, and comprises the following steps: constructing a ship motion simulation environment, and establishing a dynamic simulation system containing marine environment parameters; acquiring ship operation data; a dual-network structure model is constructed, the dual-network structure model comprises a macroscopic strategy network and a microscopic control network, the macroscopic strategy network generates a course planning strategy at a long time interval, and the microscopic control network generates a specific control instruction at a short time interval; a hierarchical training strategy is adopted to train the dual-network structure model in a ship motion simulation environment, firstly, a macroscopic strategy network is trained to generate an overall course strategy, and then a microscopic control network is trained to realize accurate control; and deploying the trained dual-network structure model to a ship control system, generating an optimal control instruction, and realizing real-time optimization control of the ship motion trail. According to the invention, on the premise of ensuring the control precision, the real-time optimization control of the ship motion trail is realized.

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 provides an unmanned aerial vehicle (UAV)-based flux-inductance integrated beam forming and trajectory optimization method, on one hand, an FP-AROS method is proposed based on fractional programming and semi-definite relaxation technologies in combination with approximate rank-one solution construction to carry out optimization design on ISAC beams, the Cramer-Rao bound of AoD is adopted as a sensing performance evaluation index, and the FP-AROS method is adopted to carry out optimization design on the ISAC beams; target estimation performance can be reflected more directly, so that a collaborative optimization effect of communication and sensing performance is realized; on the other hand, the invention provides an improved continuous convex approximation method based on a trust domain, and the method is used for optimization design of a UAV track. The ITRSCA method obtains a local trend approximate solution of an original objective function by calculating a second derivative, and introduces a dynamic trust domain mechanism to control approximate precision, so as to ensure global convergence of an output solution; in addition, the method is combined with a penalty function method to process non-convex constraints, and the limitation of a trust domain method in a constrained optimization problem is effectively overcome.

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 provides a micro-flow field measurement method of a mechanical biological net port flowmeter, and belongs to the technical field of micro-flow field measurement, and the micro-flow field measurement method comprises the following steps: firstly, constructing a net port cavity digital model, and realizing micro-flow field high-resolution characterization by using fluorescent particle tracing liquid and a laser confocal system; and recording a fluid movement track and processing original data by applying a track optimization function under the condition that the microfluidic driving system generates a constant pressure gradient. And solving fluid pressure distribution by adopting an orthogonal grid system and a Navier-Stokes equation, and processing speed field and pressure distribution data by utilizing a pre-trained micro-flow field analysis neural network model. Finally, the flow and pressure relation curve is obtained by calculating the flow flux of the cross section of the network port, a micro-flow field measurement correction coefficient table is generated, and the technical problems that in the prior art, the accurate measurement difficulty of the biological network port micro-flow field is large, and multi-scale flow characteristic representation is insufficient are solved.

The invention relates to the technical field of image analysis, in particular to a dispensing track optimization control system based on visual template conversion, which comprises a visual template analysis module, a contour point position correction module, a track vector generation module, a multi-parameter linkage feedback module and an optimal path planning module. According to the method, a boundary is extracted through clustering pixel superposition brightness and channel weight, a line segment frequency screening track is counted, feature stability and precision are enhanced through datum line construction, a deviation value is calculated through gradient direction segmentation boundary, a matching degree is improved through tangent point interpolation correction contour, and a weight factor is generated through included angle change and tool parameter normalization. A reference point is optimized through superposition increment, a path fusion tool attribute dynamic adaptation parameter is adopted, a multi-dimensional data fusion visual offset and floating weight real-time adjustment track is acquired, an execution point and an adjustment vector are superposed to calculate a distance matrix, an optimal path is screened through minimum distance and energy consumption, efficiency and consumption are both considered, and the system robustness and execution economical efficiency are remarkably improved.

The invention discloses a path planning and trajectory optimization method for a mobile robot in a non-flat terrain, and the method comprises the following steps: S1, the mobile robot employs a laser radar to detect the surrounding environment, and an SLAM module constructs a three-dimensional map according to point cloud data and odometer data collected by the laser radar in real time; and S2, a trafficability analysis module performs terrain trafficability evaluation based on the generated three-dimensional map, and generates a trafficability layer accordingly. And S3, the global planning module performs path search in the trafficability layer to obtain an initial path of the mobile robot from the starting point to the target point. And S4, a trajectory optimization module optimizes the initial path to ensure that the obtained trajectory meets the kinematics constraint of the mobile robot. And S5, vertically mapping the optimized trajectory to the non-flat terrain surface to obtain a final path for direct tracking of the mobile robot. According to the method, it can be ensured that the mobile robot generates a feasible track conforming to kinematics constraints in the non-flat terrain.