23 results about "Pathfinding" patented technology

The invention relates to the technical field of robot navigation, and discloses an automatic path-finding intelligent robot and a path-finding method thereof. According to the method, multi-dimensional spatial data including obstacle distribution, ground features and dynamic target motion information is acquired through an environment sensing device, spatial discretization processing is performed on the data, and a topological map which has a hierarchical structure and includes a node connection relation and a region passing weight is generated. Then, according to the node connection relation of the topological map, a path planning model based on manifold learning is established, and an optimal path candidate set is determined by calculating the manifold distance between adjacent nodes; then extracting a key node sequence in the candidate set, dynamically optimizing a path in combination with a region passing weight, and generating a preliminary navigation track; and finally, inputting the initial track into a motion control model, carrying out smooth processing according to kinematics constraint of the robot, and outputting a final execution path. According to the invention, the robot can efficiently find the way in a complex environment.

This invention discloses a hybrid path planning method and apparatus based on an improved adaptive window algorithm, comprising: (1) obtaining an initial grid map of the environment through SLAM; (2) determining the robot's starting point S and target point G in the grid map; (3) performing global path planning using an improved A* algorithm; (4) finding the target point G using the improved A* algorithm to obtain a global path Path; (5) sequentially selecting the starting point, inflection point, and ending point from the global path Path as key points, and using them as a set of sub-target points for local path planning; (6) performing local path planning using an improved adaptive window algorithm; (7) when the robot finds the final target point G, completing the local path planning and completing the hybrid path planning from the starting point S to the target point G. Using this invention, the reliability and safety of the robot's obstacle avoidance and pathfinding process can be improved.

Systems, devices, methods, and computer-readable media provide improved flight path selections. A system includes a graph representation operator configured to generate a graph of potential flight paths and actual obstacles in the potential flight paths, the potential flight paths extending from a takeoff location to a destination location, a predictive analysis model configured to receive the graph and environmental data and generate predictions of future environmental conditions based on the graph and the environmental data, a cost function and heuristic estimate operator configured to determine costs associated with flight paths of the potential flight paths based on the future environmental conditions, and a predictive pathfinding model configured to identify a flight path of the potential paths based on the costs, aircraft specific performance data, air traffic control constraints, current environmental conditions, and the future environmental conditions.

The invention provides a role way-finding method and device based on a virtual scene and electronic equipment, and the method comprises the steps: obtaining a way-finding request and historical behavior data of a target role; determining a matching degree between the historical behavior data of the target role and scene information of a first preset path; under the condition that target matching degrees greater than or equal to a preset threshold exist in the matching degrees, determining a first preset path corresponding to the maximum matching degree in the target matching degrees as a target path of the target role; and carrying out path finding on the target role according to the target path. By adopting the method, the target path can better conform to role expectation, so that the immersion of the role and the game experience are improved.

This application discloses a method and terminal for selecting fitness trail routes based on the TSP and Dijkstra algorithms, belonging to the field of urban planning. The method includes: performing distance clustering on existing fitness facilities to obtain initial waypoints; mapping and correcting these waypoints to target waypoints using a set of feasible and prohibited paths; generating a comprehensive resistance value based on the environmental resource suitability and spatial access cost of the paths; inputting the target waypoints into a loop-shaped TSP optimization model; using a greedy algorithm to obtain the initial optimal loop connection sequence of waypoints that forcibly traverses all nodes; and using the comprehensive resistance value and the feasible path set, employing the Dijkstra algorithm to obtain the complete physical loop path corresponding to this connection sequence as the trail route selection. This application constructs a two-layer collaboration between TSP macro-topological ordering and Dijkstra micro-physical pathfinding, effectively avoiding physical spatial obstacles and harsh environments, achieving physical connectivity towards high-quality slow-moving resources, and outputting a high-quality route selection scheme that balances global efficiency and engineering feasibility.

This invention discloses an improved pathfinding method and chip design system, comprising the following steps: generating multiple routing rule matrices with the same grid size; adding various routing constraints to the corresponding routing rule matrices; obtaining a total routing rule matrix; forming an outer polygon P1; calculating the movement cost of all possible moving positions within the outer polygon P1; generating corresponding mask matrices and transition matrices according to the constraint type; starting pathfinding from the starting point, and extracting a submatrix centered at the new position and with the same size as the mask matrix from the total routing rule matrix at each new position; obtaining the constraint cost; selecting the adjacent position with the lowest constraint cost as the next moving point, and repeating the above steps until the destination is reached. The pathfinding speed of this invention is 45.4% faster than traditional algorithms, and when the number of lines increases from 1 to 5, the pathfinding speed is 75.6% faster than traditional algorithms, making it particularly suitable for high-density, multi-constraint chip routing environments.

Systems, devices, methods, and computer-readable media provide improved flight path selections. A system includes a graph representation operator configured to generate a graph of potential flight paths and actual obstacles in the potential flight paths, the potential flight paths extending from a takeoff location to a destination location, a predictive analysis model configured to receive the graph and environmental data and generate predictions of future environmental conditions based on the graph and the environmental data, a cost function and heuristic estimate operator configured to determine costs associated with flight paths of the potential flight paths based on the future environmental conditions, and a predictive pathfinding model configured to identify a flight path of the potential paths based on the costs, aircraft specific performance data, air traffic control constraints, current environmental conditions, and the future environmental conditions.

This application belongs to the interdisciplinary field of graph databases, artificial intelligence, and reinforcement learning. It provides a health management method, device, and computer-readable storage medium based on graph reasoning. By jointly extracting entities and relations from unstructured medical text and eliminating conflicting edges based on confidence rules, it constructs a target evidence-based knowledge graph with a deterministic pathfinding structure. By quantifying features of heterogeneous multi-dimensional health data to construct a set of state features, it determines the starting query node for graph reasoning, realizing the generation and distribution of the initial plan. It uses the time-series interactive data asynchronously uploaded by the terminal device to calculate the task deviation as a reward signal for reinforcement learning, driving the model to adaptively adjust the output action instruction vector. While effectively updating the timestamp and execution frequency parameters of the management plan, the underlying algorithm architecture strictly constrains the adjustment range of strategy iteration, balancing the intelligent optimization efficiency of the health management plan with the security of execution.

A method, control system, and computer program are provided for routing and rerouting container transport vehicles carrying storage containers in an automated storage and retrieval system comprising a frame structure. The method includes the following steps, performed by the control system when the computer program is executed: running a multi-agent pathfinding algorithm (MAPF) in the control system to establish and assign routes for the container transport vehicles on a track system from their current location to a destination location; determining how far the container transport vehicles can travel on a first portion of the assigned route within a set time interval, the first portion being shorter than the assigned route to the destination; locking the first portion of the assigned route that the container transport vehicles can travel within the set time interval; instructing the container transport vehicles to move from their current location to the destination location on the assigned locked route; and repeating the above steps.

The application discloses a virtual rope path searching method and device, a storage medium and an electronic device, and can also be applied to the field of maps. The method comprises the following steps: acquiring position information of a target end path searching point of a target rope, and storing the position information of the target end path searching point into a path searching point set, wherein the target rope comprises N rope points, the N rope points comprise the target end path searching point, N is a natural number; determining the position relationship between each rope path searching point in the N rope points and a region in a virtual scene in which a virtual object is allowed to move, and determining at least one target rope path searching point from the N rope points based on the position relationship; acquiring position information of the at least one target rope path searching point, and storing the position information of the at least one target rope path searching point into the path searching point set; and searching a path for the virtual object by using the position information of each path searching point in the path searching point set. The application solves the technical problem of low path searching accuracy.

This application relates to the field of cable assembly design technology, specifically providing a human-machine collaborative enhanced intelligent forward design method and system for complex cable layouts. The method includes: constructing a multi-level weighted region driven by human design intent, generating weighted costs for different guided regions; constructing a three-dimensional pathfinding mesh based on the physical diameter constraints of the cable to be laid, uniformly mapping the multi-level weighted regions to the voxel mesh nodes of the pathfinding mesh, and introducing obstacle proximity penalty costs; employing a heuristic search-based three-dimensional pathfinding algorithm to perform path search on the voxel mesh to determine the optimal cable path; sampling the optimal cable path according to the cable diameter to generate a list of cable control points, and then generating a three-dimensional cable entity model based on the list of cable control points. This application achieves highly efficient automated design of large-scale complex cable harnesses through automatic cable layout design with an automatic pathfinding algorithm as the main body and human design intent as the guiding constraint.

This application provides a pathfinding method, apparatus, medium, and computer device for AI characters. The pathfinding method includes: obtaining a starting position and an ending position of a pathfinding task, and loading a game map corresponding to the pathfinding task, wherein the game map is divided into a predetermined number of regions, including covered regions and uncovered regions; obtaining a set of cover segments corresponding to each uncovered region, wherein each cover segment is a boundary among the boundaries of the predetermined number of regions that satisfies a predetermined occlusion condition; and determining the optimal path for the AI ​​character to move from the starting position to the ending position on the game map based on the player character's position and the set of cover segments corresponding to the uncovered region where the player character's position is located.

This invention discloses a cross-language code vulnerability remediation method based on dual constraints of knowledge graph and large model, comprising: receiving multi-language heterogeneous vulnerability code samples, performing feature standardization processing, and extracting the language context and core vulnerability intent tags of the code; inputting the vulnerability intent tags into a pre-built cross-language security knowledge graph, performing cross-level pathfinding matching based on risk patterns, and dynamically extracting the necessary security interfaces for vulnerability remediation and prohibited dangerous functions; using a context-aware mechanism, fusing and concatenating the original vulnerability code with whitelist rules to construct prompt words as soft constraint intent guidance at the input end of the large model; simultaneously, mapping blacklist rules to a set of underlying lexical indexes, introducing a log-probability interceptor in the decoding and inference stage of the large model, forcibly modifying the generation log-probability of blacklisted lexical elements to negative infinity, and implementing hard constraint interception at the underlying level; finally, combining the soft guidance at the input end and the hard interception at the decoding end, bidirectionally controlling the autoregressive generation process of the pre-trained language model, thereby eliminating model illusion and outputting the optimal security remediation patch that conforms to the target language specification.

An advanced multi-agent pathfinding and optimization system for logistic networks, revolutionizing fourth-party logistics (4PL) management through Multi-Agent Reinforcement Learning (MARL). It aims to optimize package transportation across multi-hub networks, shifting from a volume-driven approach to a rate-driven model. This system adapts dynamically to business fluctuations, redefining risk dynamics in logistics. It prioritizes shipments based on rate tiers, altering risk distribution and incentivizing efficiency over volume. The system addresses current logistical challenges by integrating multi-modal perspectives and anticipating real-time disruptions, overcoming scalability, adaptability, and dynamic change limitations. Important features include an advanced “GeoEngine” for accurate geocoding, a first / last mile engine with VRP solutions, real-time A* algorithm implementation for hub assignment, and a comprehensive RL model utilizing Q-learning with lightGBM. This system provides a more balanced, market-driven approach to logistics, offering a paradigm shift in efficiency, adaptability, and strategic rate management.

This invention discloses a path planning method for unmanned surface vessels (USVs) based on an improved A* algorithm, comprising the following steps: Step S1: Establishing a grid map using environmental information obtained from semantic segmentation results, with each grid cell marked as a feasible or obstacle area, and providing a starting point B and a target point for path planning; Step S2: Introducing azimuth cost to optimize the cost function, reducing the total number of search path points and improving search efficiency; Step S3: Pruning and B-spline curve optimization of the improved A* algorithm pathfinding results, reducing the total path length while making the travel path smoother; Step S4: Adding collision detection functionality to reserve sufficient safety distance between the USV and obstacles. This invention introduces azimuth cost to optimize the cost function of the traditional A* algorithm, and prunes and optimizes the improved A* algorithm pathfinding results using B-spline curve optimization, reducing the total path length while making the travel path smoother.

The application provides a kind of building pipeline intelligent pathfinding system, comprising: three-dimensional scene modeling module, for grid map modeling, three-dimensional pipeline modeling and obstacle modeling;Pipeline and structure area pre-division module, for each pipeline edge expansion and area division;Intelligent pathfinding algorithm decision module, for judging the input form of project, if input is the starting point, end point and intermediate point of pipeline, then call the first improved A* intelligent pathfinding algorithm to carry out pipeline pathfinding, if input is the starting point and end point of pipeline, then call the second improved A* intelligent pathfinding algorithm to carry out pipeline pathfinding;Pipeline micro-adjustment module, for judging whether each pipeline is over-mapping or conservative mapping, if over-mapping, then carry out space tightening, if conservative mapping, then carry out space expansion.The beneficial effects of the application are that different algorithms are configured for pathfinding with different inputs, to improve the efficiency of pathfinding.

The invention discloses a role way-finding method and device based on a static obstacle, a storage medium and electronic equipment. The method comprises the following steps: in response to a way-finding request triggered for a virtual character, obtaining a candidate grid region; obtaining an obstacle path with a first dynamic identifier in the candidate grid region; obtaining a static obstacle with a second dynamic identifier in the candidate grid region; under the condition that the virtual role allows the execution of the conditional operation, the obstacle path with the first dynamic identifier is determined as a feasible path, and the feasible path is a path through which the virtual role can pass; and obtaining a moving path based on the feasible path in the candidate grid region. According to the invention, the technical problem of low role way-finding accuracy based on static obstacles is solved.

The invention discloses a hexagonal lattice path finding method for discrete event simulation. On the basis of a traditional A star algorithm, firstly, the operation of judging and cutting (namely pruning processing) is put forward when six nodes adjacent to a current node are cyclically traversed, and then the slope value between a reserved node and a target node is calculated; and finally, selecting the node of which the slope value is closest to the slope value between the starting node and the target node to enter the next round of path finding process. According to the method, the problem that many useless nodes are searched when a traditional heuristic A star search algorithm faces obstacles is solved, the calculation time and the storage space used for searching are reduced in the aspect of space calculation such as way finding in the field of discrete event simulation based on aviation, navigation, ground and the like, and the calculation efficiency of the A star algorithm is greatly improved; the problem of low calculation efficiency of way-finding calculation and the like in the simulation deduction process in the discrete event simulation field based on aviation, navigation, ground and the like is solved.

The invention relates to the technical field of path planning, and provides a multi-agent path planning method based on Gaussian belief propagation and global routing, and the method comprises the steps: collecting environment information including a static obstacle position and a dynamic obstacle motion state, and a self state, and constructing an environment grid map and an agent state vector; generating a reference path by using a global routing algorithm; a factor graph model is constructed, the factor graph model comprises variable nodes and factor nodes, the variable nodes represent the state of the intelligent agent in a future time window, and the factor nodes comprise tracking factors and inter-robot factors; performing message passing based on Gaussian belief propagation and the factor graph model, iteratively updating the belief of the variable node, and outputting an optimized path of each agent in combination with global constraint of a tracking factor and local collision avoidance of factors between robots; and the optimized path is smoothed by adjusting the weights of the factors and the tracking factors between the robots.

The present invention discloses a grid map-based robot pathfinding method and apparatus, a robot, and a storage medium. The method includes: obtaining a first grid map and a second grid map, planning a travel path of a robot from a current position to a preset target point according to the second grid map, determining an obstacle position in a case where the travel path is impassable, determining a first path of the robot from the current position to the obstacle position based on the second grid map, determining a second path of the robot from the preset target point to the obstacle position based on the second grid map, and planning a transition path from an endpoint of the first path to an endpoint of the second path, thereby obtaining a target path of the robot, which can reduce path search time, reduce memory computing consumption, improve the travel efficiency of the robot, and avoid collision caused by the fact that the robot is too close to a boundary or obstacle.

The application provides an adaptive pathfinding method for unmanned aerial vehicle clusters, specifically including task initialization and environment perception, path planning based on bionic intelligence, multi-vehicle cooperation and dynamic adjustment. The method designs an information pheromone updating strategy with variation mechanism and dynamic obstacle avoidance capability, making the path search more adaptive and stable. At the communication level, a decentralized communication architecture is adopted between unmanned aerial vehicles, supplemented by a cloud synchronization mechanism, to realize real-time sharing of task information and efficient adjustment of path schemes. The pathfinding method is applicable to various practical scenarios, including post-disaster search and rescue, ecological environment monitoring, and large-area inspection, and has good scalability and robustness, effectively addressing the challenges brought by dynamic environments and task changes.