17 results about "Path cost" patented technology

The invention relates to the technical field of intelligent path planning, and discloses a path planning method and system based on polling management, and the method comprises the following steps: obtaining the information of a to-be-polled store; constructing a store topological graph with edge weights according to the geographical location information and the priority scores of the stores; constructing an objective function of routing inspection path optimization based on the store topological graph; inputting the objective function into a genetic algorithm for solving, and outputting an inspection path under a given path cost constraint condition; monitoring store state information in real time, interrupting current path execution when a store with an abnormal state is detected, and inserting the abnormal store into path planning input; and based on the current polled path state and the inserted abnormal store information, the genetic algorithm is executed again to locally reconstruct the remaining paths, and an updated polled path is generated. According to the invention, the inspection efficiency and the key store response capability are improved.

The application discloses a multi-target path optimization method and device and a medium, and relates to the technical field of path planning. The method comprises the following steps: constructing a weighted graph model of a region to be planned and verifying an adjacency matrix to obtain verified weighted graph data; calculating the shortest path distance between all node pairs to form a full-source shortest path distance matrix and a predecessor record set, performing connectivity detection and feasible region pruning processing on unreachable node pairs; generating an optimized access sequence covering all target nodes based on the processed full-source shortest path metric data and a nearest neighbor greedy strategy, accumulating a composite path cost, performing a feasibility test on a time window, a capacity and a risk constraint to generate an initial path sequence and accumulated cost data; expanding adjacent node pairs in the initial path sequence into specific executable paths on the original graph according to the predecessor record set, and performing neighborhood optimization on the executable paths by using a local search operator, and outputting an optimized final path scheme and an accumulated cost report.

The invention discloses a path cost calculation method and device based on discrete graph key point recognition and a medium, and relates to the technical field of path planning, and the method comprises the steps: obtaining a working environment image corresponding to an intelligent agent working environment, and a current node and a target node corresponding to an intelligent agent; dividing the nodes into key nodes and non-key nodes based on the communication relationship, and drawing a skeleton graph based on the key nodes and the non-key nodes; calculating the distance between any two key nodes in the skeleton diagram based on a multi-source Dijkstra algorithm, and creating a distance table based on the distance; and judging whether the current node is a key node or not and whether the target node is a key node or not, and determining the optimal path cost corresponding to the intelligent agent based on the judgment result and the distance table. The method and the device are used for solving the problems of low efficiency and poor accuracy when path cost calculation is carried out by directly utilizing a discrete graph structure in the prior art, and the efficiency and the accuracy of path cost calculation are improved.

The application is suitable for the technical field of path planning, and provides a path re-planning method and device, a self-moving device and a readable storage medium. The method comprises the following steps: fusing a global cost map of a target area and an expected path cost map to obtain a fusion map, wherein the expected path cost map is a cost map generated according to an expected path; and determining a target path between a starting point and an ending point of the expected path according to the cost values of each point in the fusion map. According to the application, the expected path cost map is also used for planning in the process of planning the path, that is, the expected path is considered when the path is planned, a new path that is feasible and close to the expected path is obtained, and the situation that there is no solution for a local path can be avoided.

The application provides a multi-target waypoint planning method and system fusing an improved A* algorithm and an SPFA algorithm, and the method mainly comprises the following steps: a mobile navigator rasterizes the surrounding environment, and calibrates a target point set and a starting point of the navigator to simulate a task scene; a distance value calculation of the starting point to the target point is performed through construction of the improved A* algorithm; a negative edge weight map with point priority, direction and without negative loop is established through fusion of the improved A* algorithm and the SPFA algorithm, and the best waypoint order can be obtained according to the point priority in the negative edge weight map; the navigator moves according to the waypoint order to traverse the task target points, and global path planning and local path planning algorithms are realized. Through the application, a large number of path points and inflection points are reduced, the total path length is shortened, the total path cost is reduced, the overall efficiency is improved, and a reasonable and fast feasible path is planned in a multi-target waypoint task in a complex environment.

This invention provides a delivery route scheduling optimization method, system, and storage medium based on spatial indexing. The method divides the delivery geographic space into spatial partitions using a spatial index structure and records order ownership relationships. It utilizes spatial filling curves to perform one-dimensional dimensionality reduction mapping on pick-up and delivery locations to generate one-dimensional sorting codes, which are then arranged in ascending order to obtain an order sequence with spatial proximity clustering characteristics. Constrained by rider load capacity, consecutive orders are extracted along the order sequence to form candidate order combination combinations. The travel cost of these candidate combination combinations is estimated segment by segment using the spatial index structure, and the cyclic path cost is accumulated according to the access order. The cyclic path costs of all candidate combination combinations are compared, and the one with the lowest cost is selected as the scheduling execution result. Rider assignment information and pick-up / delivery order suggestions are output. This method effectively reduces the computational complexity of delivery route planning and improves the spatial rationality and route economy of order combination.

The invention provides a virtual navigator path planning method based on SAC-RRT *, which innovatively introduces formation geometric envelope parameters to map multi-agent entity constraints into virtual navigator geometric constraints, replaces traditional RRT uniform sampling with non-deterministic Gaussian sampling of an SAC algorithm, and realizes adaptive step length extension and formation-level collision detection in combination with a Critic network. Meanwhile, the path cost optimization quantity of RRT rewiring is creatively converted into rewards to be fed back to the SAC network to complete backward fusion, network training is enhanced in cooperation with a real and virtual dual-experience playback strategy, formation trafficability factors are fused in a state space, a multi-dimensional reward function optimization decision is designed, and finally, the formation trafficability is improved through three times of B spline curve smoothing processing. The multi-agent formation path planning considering safety, optimality and convergence efficiency is realized, and the problems that a traditional method ignores formation geometric constraints, sampling blindness is large, and reinforcement learning and sampling algorithm fusion is not deep are effectively solved.

The invention discloses a railway signal scheduling optimization method and system based on an AlphaEvove algorithm, and relates to the technical field of railway signal scheduling, and the method comprises the following steps: S1, constructing a track state diagram; s2, constructing a track state characterization model, a strategy network and a value network; s3, constructing a Monte Carlo tree, performing search by using selection, expansion, simulation and return stages in Monte Carlo tree search, and generating a preliminary scheduling strategy after search; s4, introducing a plurality of candidate scheduling sequences from the preliminary scheduling strategy as a population, generating new scheduling samples by using an evolution strategy, and screening the new scheduling samples and updating a strategy network based on path cost; and S5, selecting a scheduling scheme with least conflicts and minimum delay in the candidate samples as an optimal scheduling scheme, and outputting the optimal scheduling scheme. Compared with a traditional railway dispatching method based on rules or a single heuristic algorithm, the railway dispatching method has higher learning ability and generalization ability and can continuously adapt to different operation scenes.

The invention provides a path retrieval system and method for vehicle recovery, and relates to the technical field of intelligent logistics, and the method comprises the steps: constructing a recovery network topology, building a path cost evaluation function, carrying out the heuristic path retrieval of the recovery network topology, obtaining a candidate path set, and carrying out the dynamic optimization of the non-cooperative game weight of the candidate path set. An equilibrium weight ratio is obtained; cost weight feedback updating is carried out on the path cost evaluation function based on an equilibrium weight ratio, path evolution is carried out on the candidate path set, and an evolution path set is obtained; performing node state exploration and cost updating decision on the evolution candidate path set to obtain the disposal cost of each path in the evolution candidate path set; and determining the recovery path of the target vehicle from the evolution path set according to the disposal cost, thereby realizing game equilibrium optimization based on multi-target weight and exploration decision of uncertain node state, and further performing global static optimization and local dynamic adaptation on the vehicle recovery path.

The application discloses a multi-modal freight transport path optimization method driven by endogenous congestion and multi-dimensional overlap, aiming to solve the technical problem that the existing model cannot simultaneously process multi-dimensional endogenous correlation such as link congestion feedback, hub transfer bottleneck, path physical overlap and mode perception similarity. The application constructs a multi-layer directed multi-modal transport network and generates a candidate path set; establishes a generalized path cost function containing road section and hub endogenous congestion; constructs a road space correlation penalty term based on shared length and a mode similarity penalty term based on hub betweenness centrality; incorporates the above elements into a multi-objective optimization framework based on maximum entropy, and converts it into an improved nested fixed point equation; uses instrumental variable substitution method to solve the endogeneity problem in parameter estimation, and obtains network equilibrium flow through iterative solution. The application breaks through the independent and irrelevant assumption of the traditional model, and significantly improves the accuracy and behavior explanation ability of freight flow prediction in a complex multi-modal transport network.

The application discloses a multi-dimensional dynamic perception and intelligent computing power scheduling method and device for a computing power network. The method comprises the following steps: deploying a plurality of target components constituting multi-dimensional dynamic perception at terminals, edge nodes and regional computing power centers, and collecting computing power indexes and network indexes of each node; analyzing indexes in a preset period through a time sequence prediction model to obtain computing power bottleneck risk and time delay attenuation information; dynamically obtaining computing power dimension weight and time delay dimension weight according to a type of a task to be executed, and combining the computing power bottleneck risk and the time delay attenuation information to construct a cost function; determining a node corresponding to a minimum cost function as a target node; obtaining path costs corresponding to all feasible migration paths according to the target node, determining a migration path corresponding to a minimum path cost as a target migration path, and triggering intelligent scheduling of computing power according to the target migration path. The method can realize collaborative perception, intelligent decision and closed-loop control of computing power and network resources.

The present application relates to the field of three-dimensional warehouse automatic seeding, and more particularly to a multi-layer three-dimensional warehouse automatic seeding path optimization scheduling method and system, comprising the following steps: obtaining seeding associated data and three-dimensional warehouse associated data through a data interaction module to construct a multi-dimensional mapping table; calling a preset material characteristic and path constraint mapping library, filtering paths that meet the constraints through an improved A algorithm and calculating path costs to output an initial path; inputting AGV real-time load states, outputting regional task allocation plans through a global scheduling center, combining multi-source sensing data, optimizing local obstacle avoidance, and outputting an optimized task allocation table; calculating task priorities, allocating resources according to priorities and processing path conflicts to output final task scheduling instructions. The present application calculates task priorities through multi-dimensional parameters, balances AGV loads through distributed global computing power scheduling, effectively solves the problem of AGV task overload and idle coexistence, and avoids job interruption caused by hardware failure.

This invention discloses a trajectory planning method for an H-shaped motion platform based on the A* algorithm and fifth-order polynomial interpolation, belonging to the field of intelligent motion control technology. A two-dimensional grid map is used to model the operating environment of the H-shaped motion platform. An initial discrete path node sequence is obtained through an 8-neighborhood search using the A* algorithm. The actual path cost is calculated using Euclidean distance, and the heuristic cost is calculated using Manhattan distance, satisfying an underestimation condition to ensure the globally optimal path. The initial path node sequence is optimized and segmented. Redundant nodes are removed and key path points are extracted based on the direction vectors of adjacent three points, reducing path inflection points and improving path smoothness. A fifth-order polynomial interpolation algorithm is used, with adjacent key path points as boundaries, combined with six boundary conditions (position, velocity, acceleration) to construct the trajectory, completing the transformation from a discrete path to a smooth, continuous trajectory. This invention is suitable for precision operation scenarios such as semiconductor processing, panel inspection, and laser cutting, and has good engineering application value.

This invention discloses a path planning method for unmanned aerial vehicles (UAVs) in confined environments based on the BAT-RRT* (Bi-directional Adaptive Tangential RRT*) algorithm. The method acquires environmental data and establishes a 3D environment model including narrow passages and non-convex obstacles. It constructs a path cost evaluation function for the UAV and solves the evaluation function based on the BAT-RRT* algorithm to obtain the optimal and smooth path planning result. Firstly, this invention employs an adaptive flat corridor sampling mechanism for topology awareness and dynamic sampling, which actively conforms to the direction of the narrow passage, fundamentally solving the problem of narrow passage crossing from a probabilistic perspective. Secondly, it introduces a cascaded decision-making and fluid dynamics-inspired tangential gliding field, breaking the force balance of the traditional potential field at the saddle point and guiding the random tree to achieve agile wall-attaching and bypassing, effectively avoiding local minima and suppressing potential field oscillations. Finally, it combines subtree cost propagation and smoothing algorithms to correct the lag in cost updates and ensure the asymptotic optimality and continuity of the trajectory.

This invention discloses a cost optimization method for implementing net-zero carbon emissions in canals, relating to the field of canal construction and operation technology. It addresses the problem that existing technologies offer numerous options but lack a unified cost decision-making framework, preventing the achievement of global optimization. First, this invention obtains four types of basic parameters, transforming different technologies into quantifiable decision variables, providing a unified cost measurement benchmark for heterogeneous technologies and solving the problem of incomparability between technical solutions. Second, it sets an objective function, focusing on minimizing net present value, integrating investment, operation and maintenance, fuel, carbon costs and subsidies, and carbon revenue, achieving dynamic accounting of the entire life-cycle cost. Finally, by combining constraints and using algorithms, it automatically finds the equilibrium point between the marginal emission reduction cost and the carbon shadow price of each technology, controlling the overall cost within a preset range and preventing nonlinear runaway marginal costs, thus realizing the transformation from experience-based judgment to data-driven decision-making.

The invention discloses an unmanned aerial vehicle robust three-dimensional path planning method based on a channel knowledge graph, and the method comprises the steps: obtaining the channel knowledge graph based on an offline sampling and interpolation method; selecting a corresponding penalty strategy based on a node total cost function defined in the A * algorithm and information conditions of statistical characteristics; when the noise statistical characteristics of the channel knowledge graph are unknown, a penalty term is defined in a local statistical mode; when the noise variance of the channel knowledge graph can be obtained, defining a penalty term by adopting a probability model; and carrying out robust path search to obtain an optimal or approximately optimal three-dimensional path under the constraint of communication quality. According to the method, the penalty term based on the signal-to-noise ratio statistics is added into the total cost function, so that the suppression of the pseudo high-value point in the noisy channel knowledge graph is realized. According to the mechanism, the path cost can be dynamically adjusted under the condition of existence of a measurement error, interpolation deviation or channel fluctuation, the unmanned aerial vehicle is prevented from mistakenly entering an area with poor communication quality, and the robustness of path planning and the communication reliability are remarkably improved.

This invention proposes an efficient K-short path calculation method and system for dynamic road networks, relating to the field of path planning technology. It involves obtaining multiple OD pairs from a directed graph of the road network, constructing a list, and initializing it. For each OD pair, a shortest path is calculated, and a set of deviation paths is constructed based on the shortest path. The shortest path and the set of deviation paths are stored as an initial K-short path set. When the edge weights of the road network structure are adjusted, potentially affected OD pairs are screened based on the changed edge attributes and the initial K-short path set. The initial K-short paths from the potentially affected OD pairs are obtained, and a substantial impact test is performed: if an initial K-short path does not contain any changed edges, and its path cost is less than the total cost of potential new paths constructed from the changed edges, then the path is retained; otherwise, the path is deleted. New paths are supplemented based on the set of deviation paths until K shortest paths are satisfied, thereby significantly reducing computational complexity and improving the efficiency and accuracy of path updates in dynamic scenarios.