144 results about "Path length" patented technology

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention provides an unmanned aerial vehicle pose visual angle optimization method and system for fracture refined shooting. The method comprises the steps of performing fracture detection and boundary extraction on a coarse inspection image; recovering a camera track and sparse point cloud based on multi-view three-dimensional reconstruction, carrying out back projection and estimating a normal vector of a crack surface; constructing a shooting spherical shell with limited inner and outer radiuses by taking the crack point as a center, and generating a view cone and spherical shell intersection region allowed to be shot as a candidate set in combination with a normal vector; sampling in the candidate area to generate a plurality of candidate shooting points, and synchronously resolving the flight and holder integrated pose of the corresponding unmanned aerial vehicle; constructing a multi-target cost function including path length, attitude, pan-tilt angle and shooting error, and generating an optimal shooting point sequence and an inspection path through an optimization algorithm; the system realizes fine, efficient and automatic shooting of cracks in a complex structure environment through cooperation of multiple modules, and effectively improves the imaging quality and the detection precision.

The invention discloses a reinforcement learning-based cable arrangement method and system in a power supply and distribution system, and a storage medium. The method comprises the following steps of S1, obtaining cable information, laying environment information and constraints required to be met by cable laying; s2, abstracting a bridge system into a weighted undirected topological graph; s3, calling a path planning algorithm based on reinforcement learning, and calculating an optimal cable laying path by taking a minimum comprehensive cost function including path length cost, plot ratio cost and precious penalty terms as an optimization target under the constraint that cable laying needs to be met; s4, calculating the center coordinate of each cable to be arranged in each bridge by adopting a greedy algorithm according to the optimal cable laying path, and arranging the cables according to the center coordinate; according to the method, the optimal laying and arrangement path can be planned on the premise of meeting various physical constraints of cable laying, and the method can automatically and dynamically adapt to different laying scenes.

A method of monitoring a subject with an opto-physiological sensor system is described herein. The method comprises obtaining a model of the optical properties of at least one body tissue type to be monitored, wherein the model of the optical properties comprises a definition of static (DC) and dynamic (AC) components of transmitted optical power and a definition of a source-detector separation related to a normalised path length for an illumination source of the opto-physiological sensor: obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject: obtaining an indication of at least one physical variable from the wearable device worn by the subject; determining, using the opto-physiological model, how the at least one physical variable affects the at least one physiological property; and determining a corrected value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

The invention relates to the technical field of robots, and discloses an autonomous obstacle avoidance navigation method, a robot and a storage medium. According to the autonomous obstacle avoidance navigation method, in the process of moving along an initial planning path, when a first target obstacle and a second target obstacle (within a preset range of the first target obstacle) are sensed, the first target obstacle and the second target obstacle are detected; firstly, a target avoidance point is determined based on parameters such as a passing distance between two obstacles so as to avoid the obstacles and re-plan paths to obtain at least two candidate planning paths, and finally, a target planning path is determined according to cost values and path lengths of the at least two candidate planning paths. Thus, according to the technical scheme, multiple obstacles can be sensed in real time, the target obstacle avoidance point can be accurately determined according to the multiple obstacles, and the obstacle avoidance efficiency can be improved while the obstacle avoidance safety is guaranteed and the collision risk is reduced.

The invention provides an APF-RRT * and genetic algorithm fused unmanned aerial vehicle path planning system and method, and belongs to the technical field of unmanned aerial vehicle autonomous flight. An improved APF-RRT * algorithm is used for carrying out path search, a collision-free initial path from a starting point to a target point is generated, and the improved APF-RRT * algorithm comprises the steps that intelligent sampling fused with a regional probability sampling strategy and a dynamic target bias strategy is adopted to guide growth of a random tree; and adaptively adjusting the extension step length according to the density of obstacles around the current node, the path points of the initial path serve as an initial population of a genetic algorithm, a final path meeting the dynamic constraint of the unmanned aerial vehicle is generated through iterative optimization of the genetic algorithm, and a fitness function of the genetic algorithm considers whether the path length, the collision condition and the path turning angle exceed the constraint or not; according to the method, the planning efficiency and the path quality are improved, the executability of the final path is ensured, and the optimization speed and the path quality are both considered.

The invention relates to the technical field of path planning, in particular to a risk awareness path planning method and system based on time sequence prediction, and the method comprises the steps: predicting the passability probability of a dynamic node at a future moment, calculating the predictive risk cost of the node, and adding the predictive risk cost into a cost function of an A * algorithm, thereby enabling a robot to actively avoid a high-risk region, and improving the path planning efficiency. According to the method, the path planning is prospective, the path which is failed during arrival is prevented from being planned, the path planning can be quantitatively balanced between the path length and the blocking risk, the optimal path selection is realized, and the efficiency and the quality of the path planning are improved.

The invention relates to the technical field of cable detection, in particular to a method and system for detecting the thickness of an insulating layer of a cross-linked insulated cable. The method comprises the following steps: acquiring a cable cross section image, performing region segmentation to identify three topological connected domains, and extracting inner and outer boundaries; establishing a physical field model and applying a first type of boundary conditions; solving a Laplacian equation in the insulating layer region to generate a convergent scalar potential field; performing differential operation on the scalar potential field to obtain a gradient vector field, and searching gradient modulus length extreme points on the virtual equipotential line to mark thinnest and thickest feature points; and taking the thinnest feature point and the thickest feature point as starting points, respectively executing streamline tracking along positive and negative gradient directions, and calculating the sum of path lengths so as to respectively obtain the minimum thickness of the insulating layer and the maximum thickness of the insulating layer. Noise is eliminated by solving the Laplacian equation, rapid positioning is performed by using the gradient modulus length extreme value, and the detection precision is remarkably improved.

The embodiment of the invention provides a path planning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-sensor data; preprocessing the multi-sensor data, and performing data fusion on the preprocessed multi-sensor data to obtain a fusion pose; then, based on the fusion pose, the environment constraint information and the current battery state of the inspection robot, a comprehensive cost function used for path evaluation is constructed, and the comprehensive cost function at least comprises path length cost, electromagnetic interference cost, safety distance cost and electric quantity consumption cost; and finally, based on the comprehensive cost function, performing path search by adopting an improved path search algorithm to obtain an optimal path. In this way, the path planning efficiency is improved, and the method is suitable for various complex scenes.

The invention relates to a dynamic self-adaptive obstacle avoidance path planning method for a hoisting operation area, and belongs to the technical field of path planning in hoisting operation. The method comprises the following steps: scanning point cloud data by using three-dimensional laser, and establishing a static three-dimensional working environment model with a known environment; dividing a space by using a grid method, and dispersing a continuous space into a limited three-dimensional point set; setting a safe distance d; redefining a heuristic function eta; defining a volatilization coefficient; constructing a self-adaptive adjustment function considering the path length, the hoisting height and the safety factor; and updating pheromones, carrying out iteration, stopping iteration after an optimal effect is achieved, and obtaining a three-dimensional path. The method is improved on the basis of the ant colony algorithm, and efficient and safe path planning of the hoisting equipment in a complex environment is realized by combining strategies such as environment precise modeling, dynamic visual area definition, three-dimensional space rasterization processing and algorithm core optimization.

The invention discloses a laser cutting path planning method based on reinforcement learning. The method comprises the steps that S1, a task scene model is established; s2, performing rasterization processing based on the spatial state matrix, and constructing a cutting state vector; s3, inputting the cutting state vector into a PathFormer network, extracting spatial topological features and path global semantic information, and generating high-dimensional path feature embedding; s4, constructing a multi-target reward function according to feature embedding and candidate path actions, and calculating an instant reward value; s5, training a strategy network by using an improved APPO algorithm, and updating Actor and Critic parameters to jointly optimize path length, energy consumption and hot area distribution; and S6, mapping the optimal path sequence back to the actual coordinate system to generate a control instruction, and driving the laser head to execute cutting according to the planned path. Self-adaptive planning and energy consumption balance control of the laser cutting path are achieved, and the cutting efficiency, precision and machining stability are improved.

The invention relates to the field of data processing, in particular to an automatic optimization method for a laser drilling path of a circuit board, and introduces a dual-pheromone ant colony algorithm to overcome the defect that a traditional algorithm only pursues a shortest path but neglects a heat accumulation effect. Two independent pheromones of path length and heat distribution are constructed, a hole site neighborhood heat influence factor and a path global heat entropy are defined to quantify the heat dissipation performance, and an algorithm is guided to consider the distance and heat during path selection, so that the processing efficiency and heat control are effectively balanced, the algorithm is prevented from falling into a suboptimal solution due to local heat concentration, and the processing efficiency is improved. And finally, a comprehensive optimal path considering the path length and the heat dissipation effect is found. According to the method, the path length pheromone and the heat distribution pheromone are constructed and independently updated by adopting the dual-pheromone ant colony algorithm, so that the drilling path length and heat control are balanced, and the optimal solution is found.

An unmanned aerial vehicle three-dimensional path planning method based on a multi-strategy adaptive RRT * algorithm relates to the technical field of unmanned aerial vehicle autonomous flight and intelligent control, and comprises the following steps: 1, obtaining spatial information, determining a starting point and a target point, and modeling an obstacle; 2, a target bias sampling strategy is introduced, a direction vector is calculated, and a new node is generated based on the step length; and 3, if the target bias expansion fails, generating a new node by adopting a rotation detection expansion strategy. And 4, the expansion direction is adjusted in combination with an improved artificial potential field method. And 5, sampling points are generated by adopting a random point guiding strategy to improve the search diversity. And 6, dynamically selecting an optimal father node for the newly generated node and completing node reconnection. And 7, after the complete path is obtained, optimizing the path length, the corner smoothness and the safety distance from the obstacle. The method can solve the technical problems that an existing path planning method is low in efficiency, is liable to fall into local optimum, cannot comprehensively optimize the path length / corner / safe distance, and is not suitable for the urban environment.

The optical module provided by the utility model comprises a circuit board, a laser chip and a substrate. The substrate is used for bearing the laser chip. The surface of the substrate is provided with a first differential signal transmission line and a second differential signal transmission line, and the first differential signal transmission line and the second differential signal transmission line transmit a first differential modulation signal and a second differential modulation signal to the laser chip respectively. The second differential signal transmission line is far away from the laser chip relative to the first differential signal transmission line. The first differential signal transmission line comprises a first guiding part and a first bending part. And in the transmission direction of the first differential modulation signal, the first guide part is gradually far away from the laser chip to guide the first differential signal transmission line to extend towards the direction far away from the laser chip so as to provide a bending space for the first bending part. The second differential signal transmission line comprises a second guiding part and a second bending part, and the second guiding part is gradually close to the laser chip, so that the differential impedance discontinuity is reduced. The curvature radius of the first bending part is larger than that of the second bending part, thereby counteracting the path length difference of the transmission line, reducing the phase difference, and improving the high-frequency performance.

A deflectable tube (312) has a deflectable tip section (301) and a bendable body section (309). The deflectable tube has one or more turning wires (16 (i)) that rotate helically in the body section (309) in order to compensate for path length differences occurring in the body section (309) when the body section (309) is bent. In order to isolate axial loads in the body section (309), the deflectable tube (312) is provided with one or more longitudinal force isolation elements (322 (k)) in the body section (309), the one or more longitudinal force isolation elements extending parallel to the one or more steering wires (16 (i)). Both ends of the force isolation element (322 (k)) are attached to the outer tube (340) or the inner tube (330) or both.

A phase plug for a compression driver includes a body having an entrance side with a front surface and an exit side with a back surface, the body disposed about a central axis. A plurality of channels are formed through the body from the entrance side to the exit side, each of the plurality of channels having an annular configuration with an entrance at the front surface, an exit at the back surface, and a path length between the entrance and the exit. The plurality of channels have unequal path lengths such that a sound signal propagating through the plurality of channels forms a convex wavefront at the exit side of the phase plug.

The invention discloses a page anomaly detection method, device and equipment and a computer storage medium, and relates to the technical field of page detection. The method comprises the following steps: firstly, acquiring a DOM structure tree and performance parameters of a page to be detected, wherein the DOM structure tree comprises a plurality of text label nodes and text lengths among the text label nodes; under the condition that the to-be-detected page is not loaded for the first time, calculating the path length sum of the DOM structure tree according to the text label node and the text length; clustering the to-be-detected pages based on the path length sum, the performance parameters and a preset reference page, and determining a target reference page; calculating a vector distance between the to-be-detected page and the target reference page, wherein vectors are vectors formed by path lengths and performance parameters; and under the condition that the vector distance is greater than a set threshold value, judging that the to-be-detected page is an abnormal page. According to the embodiment of the invention, during detection, the reference page is adjusted according to the real-time change of the page, so that the accuracy of page anomaly detection is improved.

The application relates to a path optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an optimal path and an optimization operator; the optimal path comprises a path for reaching a starting point from a plurality of locations in sequence, and each location is passed through only once; a new path of the plurality of locations is calculated according to the optimization operator and the optimal path; it is judged whether the path length of the new path is smaller than the path length of the optimal path; when the path length of the new path is smaller than the path length of the optimal path, the new path is determined as a new optimal path. The scheme provided by the application can reduce the path length.

The invention provides an intelligent metal cutting processing method and system, and the method comprises the steps: collecting material processing path data through scanning equipment, obtaining path length and corner number as path feature data, and carrying out the grouping of the path feature data through a data grouping method, and obtaining a path complexity classification result; extracting a starting section proportion threshold according to the path complexity classification result, judging whether the path length exceeds a preset threshold, if so, increasing the starting section proportion, otherwise, maintaining the starting section proportion, and determining a starting section length range; and updating whole-course temperature track data according to the self-adaptive proportion adjustment result, and transmitting the updated track data to processing equipment by adopting a control module to realize temperature dynamic matching in the material processing process.

The application provides a phase precision debugging method, which comprises the following steps: firstly, processing a debugging board and a positive sample plate for each LTCC combined substrate, obtaining initial phase precision of a beam forming matrix through the debugging board, and then designing a surface phase line of the positive sample plate according to the initial phase precision to realize coarse adjustment of the phase precision; secondly, printing the corrected surface phase line on the surface of the positive sample plate; and finally, cutting the debugging phase line on the surface of the positive sample plate to change the path length of the signal, and then to improve the phase precision and realize fine adjustment of the phase precision. As can be seen, the application can concentrate the phase errors of all components in the system by printing the phase line on the surface of the positive sample plate, and then eliminate or reduce the phase errors by debugging the surface printed phase line, so that the operation is simple and convenient, the reliability of the system is not affected, and other devices and circuits are not needed, which can ensure the overall structure of the system unchanged and solve the phase precision problem through debugging.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a limited space unmanned aerial vehicle autonomous homeward voyage method based on real-time mapping and path evaluation. The method comprises the steps that when the unmanned aerial vehicle flies in a limited space without a GPS, real-time positioning is carried out through a laser radar SLAM technology, a three-dimensional grid map is constructed synchronously, and meanwhile a flight path is recorded in real time in a key point mode. When the electric quantity is insufficient or communication is lost to trigger return flight, the system generates two candidate paths in parallel, one path is a historical path which is backtracked in a reverse order, and the other path is a new path which is searched on a real-time map by using an A * algorithm. Then, the two paths are evaluated through a comprehensive scoring function, and scoring standards comprise path smoothness, path safety and path length; and finally, selecting a path with a relatively high score as an execution path, and performing safe return by combining a local obstacle avoidance algorithm. According to the invention, the limitation of pure path backtracking is overcome, and safer, more efficient and more intelligent autonomous homeward voyage is realized.

The invention provides an improved RRT path planning method and device based on deep reinforcement learning, which are suitable for efficient barrier-free path planning of an air-ground cross-domain robot in a complex ground and air environment so as to ensure that the robot safely and quickly arrives at a target position. The method comprises the following steps: constructing a deep reinforcement learning model, and designing a distance heuristic exploration mechanism in combination with an epsilon greedy strategy, so that an algorithm can adaptively adjust a sampling strategy; a target bias factor and a dynamic sampling space adjustment strategy are introduced, and randomness and redundant node generation in the expansion process are reduced; and path simplification and smooth optimization processing are adopted, so that the safety of the generated path and the performability of the robot are improved. Compared with the traditional RRT and RRTConnect algorithms on the key indexes such as planning time, path length and node number, the method shows higher planning efficiency, path quality and environment robustness, and can better meet the application requirements of the path planning task of the air-ground amphibious robot.

The invention provides a knowledge network-oriented adaptive learning path planning method and system, and relates to the technical field of data processing, and the method comprises the steps: building a knowledge network by taking knowledge points as nodes and taking a first-to-second relationship of the knowledge points as a directed edge; setting a target node, and extracting a plurality of sub-graphs which can reach the target node from the knowledge network; performing breadth traversal on the plurality of sub-graphs from the path length and the path blocking degree to obtain an initial learning path; and performing mastery degree evaluation on the initial learning path, performing adaptive optimization on the initial learning path according to a mastery degree evaluation result, and determining a target learning path. The technical problem that learning efficiency and effect are low due to the fact that learning path planning cannot meet the individuation level of learners in the prior art is solved. The technical effects that the learning path is dynamically adjusted according to the personalized requirements of the learner, and the learning efficiency and effect are improved are achieved.

This invention discloses a path planning method for unmanned surface vessels (USVs), belonging to the field of autonomous navigation technology for USVs. The method includes: determining the navigable boundary of the USV in a multi-threat surface environment; initializing a tribe based on the navigable boundary; constructing a multi-objective fitness function, calculating the fitness value of each member in the initial tribe, and updating the fitness values ​​of the best member of each tribe, the best member of the opposing tribe, and the globally optimal member based on these fitness values; performing iterative optimization of tribe competition and member cooperation; and outputting the globally optimal path as the final planned path for the USV after satisfying the iteration termination condition. This invention improves convergence speed while considering path length, smoothness, and safety, achieving a dual balance between path optimality and navigation safety, and is suitable for complex marine environments with multiple threats.

The application discloses a code verification method and device based on path traversal, a medium and equipment. The method finds all paths from the program entrance to the target statement with a path length not exceeding K in a control flow graph through a loop body step decomposition manner, and forms a path set. Then, the paths are traversed, and reachability verification is performed on the traversed paths. When the paths are subjected to reachability verification, array variables are simplified into array elements through expression simplification calculation, and operations related to the array variables are expanded into SMT constraint expressions related to the array elements in combination with variable constraint replacement, so that the SMT constraint solving can process variables related to arrays, the complexity of the SMT constraint solving is reduced, the solving and analysis of unreachable path segments are avoided, and the analysis efficiency is improved.

Methods, error correction circuits, and circuit modules using error correction circuits are provided for performing co-bit calculations for error correction by means of circuit complexity reduction. Methods and related devices for co-bit computations for error correction by means of circuit complexity reduction are provided. The method comprises: in an error correction circuit using a predetermined error correction code, configuring a first group of XOR operational circuits in a plurality of XOR operational circuits corresponding to a predetermined codeword length, rather than all XOR operational circuits in the plurality of XOR operational circuits, so as to reduce the circuit complexity of the error correction circuit and improve the error correction efficiency of the error correction circuit. Wherein the first set of XOR operational circuits may be a set of optimized XOR operational circuits implemented based on co-located bit coverage reduction; and performing the co-bit calculation using the first set of XOR operational circuits for error correction corresponding to a shorter codeword length, where a smaller XOR operand and a shorter critical path length can be achieved.

This invention belongs to the field of robot path planning technology, specifically disclosing a robot path planning method guided by a large language model. The method includes: discretizing the environmental space to establish a configuration space for the robot and obstacles; calculating the collision region in real time and segmenting the free space into connected components; generating prompt text; calling the large language model to parse the text and output a recommended obstacle list; constructing a path tree; generating new nodes; backtracking to generate candidate paths based on the new nodes, and allowing node poses to shift within a preset range through a tolerance verification mechanism; when the target region is connected to a connected component, adding the path to the solution set library, and selecting the optimal solution based on a weighted cost function that minimizes the path length and the total distance moved by the obstacles. This invention utilizes the semantic understanding capability of the large language model to optimize the search strategy, significantly improving the efficiency and adaptability of path planning in dynamic environments, and is applicable to scenarios such as warehousing logistics and disaster relief.

This application provides a software defect prediction method based on a dynamic path adaptive graph convolutional network. The method includes: constructing graph structure data of software modules; inputting the graph structure data into a graph convolutional network to learn the dependencies between modules; adaptively assigning weights to multi-hop paths through a dynamic path weighted convolutional layer and suppressing long path noise interference based on a path length decay coefficient; optimizing the adjacency matrix in real time based on a multi-head attention mechanism, enhancing key dependency edges and pruning redundant connections; embedding dynamic path scoring and an adaptive graph update algorithm into a classification objective function, updating training weights with a balancing strategy, and outputting defect prediction results through the optimized model. This application can effectively solve the performance degradation problems of traditional graph convolutional networks in scenarios with insufficient dependency modeling, noise accumulation, and class imbalance under the static graph assumption.

This application belongs to the field of UAV path planning technology and provides a hierarchical collaborative 3D path planning method for UAVs in converter valve hall inspection. The method includes: first, equating the valve tower to a square column and the casing to a cylinder, constructing a 3D occupying grid and expanding it to obtain a feasible flight domain; within the domain, constructing a total cost function including path length, safety, altitude window, altitude difference, angle smoothing, and electromagnetic penalty; then, running the A* algorithm on a low-resolution grid to generate a global coarse path and construct a guiding corridor; under corridor constraints, using the RRT* algorithm for bias sampling and reconnection optimization to obtain a continuous and smooth reference path; finally, using DWA in velocity space for dynamic feasibility screening and safety assessment to achieve local tracking and online obstacle avoidance. This application effectively reduces the total path cost and planning time fluctuations, improves the success rate and safety distance, reduces the maximum turning angle, and enhances planning stability and executability in the narrow environment of converter valve halls.

The application discloses a multi-target path planning method and device, equipment and a storage medium, and relates to the technical field of traffic path planning. The method comprises the following steps: acquiring solar radiation data and original map data, performing multi-source data fusion based on the solar radiation data and the original map data, and obtaining radiation road network data; constructing a total cost function of path length by using a cumulative cost function of path length and a heuristic function of estimated cost based on the radiation road network data, generating a candidate path according to the total cost function; calculating the average radiation intensity of the candidate path based on the radiation road network data, and then obtaining the energy recovery amount of the candidate path; constructing a double-target cost function based on the total cost function and the energy recovery amount, adjusting the node priority and screening and updating a non-dominated solution set according to the double-target cost function, generating a target candidate path set according to the non-dominated solution set; and determining an optimal balance path in the target candidate path set by using a multi-dimensional index quantitative evaluation and a balance coefficient sorting.