203 results about "ANT" patented technology

The invention relates to a mobile robot path planning method and system based on an improved ant colony algorithm. The mobile robot path planning method comprises the following steps of 1 environment modeling, 2 initial pheromone distribution and 3 optimal path searching and optimal path outputting. According to the mobile robot path planning method and system based on the improved ant colony algorithm, improvement is conducted on a previous traditional ant colony algorithm in the initial pheromone distribution aspect, ants can guide optimization of the ants at the beginning, and the early convergence speed is obviously increased; meanwhile initial parameters are reasonably selected, for instance, selection of pheromone evaluation, a result does not run into a locally optimal solution or has difficulty in forming an optimal solution, reasonable improvement is conducted on a pheromone update mode, the situation of running into the locally optimal solution can be effectively avoided, and the working efficiency and working reliability of the robot can be improved.

The invention belongs to the field of unmanned ship global path planning and particularly relates to an unmanned ship global path multi-target planning method based on the improved ant colony algorithm. The method comprises steps of establishing a marine environment map model by utilizing a Maklink graph theory; improving a path heuristic information strategy to obtain a path average value; designing an ant pheromone volatilization adaptive adjustment strategy; designing a strategy combining local pheromone updating and global pheromone updating; improving the state transition probability of anext node searched by an ant colony through a heading angle deviation factor of an unmanned ship; and designing an evaluation function by integrating the requirements of the shortest global path length, the least optimization iteration times of the improved ant colony algorithm, the lowest path smoothing coefficient and the like. The method is advantaged in that multiple targets such as a globalpath distance of marine navigation of an unmanned surface vehicle, the iteration frequency of optimizing the global path by improving the ant colony algorithm and the smoothness coefficient of the planned global path are comprehensively considered, the optimal global path of marine navigation of the unmanned surface vehicle is finally planned, and the method has relatively high safety.

The invention belongs to the technical field of robot navigation, automatic control and pattern recognition, and discloses an application method of adaptive ant colony algorithm (AACO) in mobile robotpath planning. The method comprises steps of constructing a point-to-point adaptive path selection strategy to select the best path point by the transfer optimization mode in the segmentation combination state; using an obstacle avoidance planning strategy to identify the nature of the obstacle, and selecting different local obstacle avoidance points to avoid obstacles; and applying a mixed deadlock processing strategy in the deadlock environment, and counting deadlock point and its fallback path point distribution information to guide ants to jump out of the deadlock environment. The data inthe embodiment shows that the AACO described in the present invention has better optimization ability than the basic ant colony algorithm (ACO), has better overall performance than the ACO, and can be effectively applied to the global process of robot path planning.

The invention relates to the technical field of route planning methods, in particular to an improved ant colony path planning algorithm based on non-uniform modeling. The improved ant colony path planning algorithm considers the shortcomings of a uniform modeling scheme to modeling in a complex environment, and quadtree modeling is proposed to obtain a non-uniform grid model; the improved ant colony path planning algorithm based on a non-uniform grid model is provided, the non-uniform grid model is used as a basis for improving a basic ant colony algorithm, the distance between a node and an obstacle is considered during selection of a path point, and the distance between a node to be selected and the obstacle is considered during the selection of the path point; an 'ant-cycle' pheromone update way is adopted, and the pheromone size is limited when the global pheromone is updated; a simulation experiment shows that the non-uniform grid model is superior to a uniform grid model, and theimproved ant colony algorithm is faster in convergence rate and can get a path which better meets the needs of underwater vehicles in the complex environments compared with other basic ant colony algorithms.

The invention provides a robot path planning method integrating an artificial potential field and a logarithm ant colony algorithm. The method comprises the following steps: S1, initializing; S2, establishing a grid map containing obstacle information; S3, establishing a movable grid table of the ants according to the current positions of the ants; S4, calculating an attractive force and a repulsive force received by the position of the current ant in the artificial potential field, establishing an influence function q (t) of the artificial potential field, and calculating a minimum included angle between a resultant force borne by the ant in the artificial potential field and an adjacent grid direction; S5, improving an ant colony algorithm heuristic function eta ij and a pheromone updating strategy; S6, calculating the transition probability density of the improved ant colony algorithm, and updating the tabu table; S7, judging whether path planning exploration is completed or not, ifnot, entering S3, and if yes, entering S8; and S8, performing re-iteration or ending according to the judgment condition. According to the method, the convergence speed of the ant colony algorithm inpath planning is effectively improved, and the situation that the artificial potential field algorithm is prone to falling into local optimum is reduced to a great extent.

The invention discloses a robot path planning method and a device based on an ant colony algorithm. The method comprises the following steps: establishing a grid map; inputting initial pheromones, andsetting algorithm parameters; placing ants at a starting point, and calculating current state transition probability is calculated; obtaining a current adaptive threshold value; comparing the currentstate transition probability with the current adaptive threshold value, and selecting a next node where the ants walk according to a comparison result; updating the path and the path length; repeating the above steps until the ants reach the end point; repeating the above steps until the M ants of the generation are all traversed; dynamically updating an information heuristic factor alpha, an expected heuristic factor beta and the pheromones; repeating the above steps until iteration is completed; performing optimization processing on the generated path; and outputting an optimal path. The method has the advantages that the convergence speed of path planning is high, and the path planning conforms to the actual motion trail of the robot.

The invention provides a double ant-counterfeiting method and system of product, relating to an ant-counterfeiting technology, wherein the first ant-counterfeiting method is a tracking number of obtained product and the second ant-counterfeiting method is an ant-counterfeiting code of obtained product. The method provided by the invention comprises two steps of batch production of product security label and anti-counterfeiting validation of the product; the system comprises a main body representing layer, a business processing layer and a data processing layer. The double ant-counterfeiting method and system of product provided by the invention are applied to various industries, and compared with conventional ant-counterfeiting method, the double ant-counterfeiting method and system of product provided by the invention haven the advantages that the cost is low, the product can be queried repeatedly by means of the tracking number which has uniqueness and permanent timeliness, the ant-counterfeiting inquiring rate of the product is high, the diversified information is provided and the management of ant-counterfeiting website to the sale channel of the product can be extended to thesale terminal.

The invention provides an anti-falling monitoring device and an anti-falling monitoring system for an elevator as well as a monitoring method for the anti-falling monitoring system. The monitoring system comprises the elevator falling-preventing monitoring device, a data acquisition card and a computer, wherein the elevator falling-preventing monitoring device selects an incremental magnetic grid sensor; when equipment to be detected falls, a sensing band and a magnetic sensing head of the incremental magnetic grid sensor are driven to simultaneously work; the data acquisition card is used for acquiring data of the sensor in real time and transmitting the data to the computer through a shielded data cable; the computer is used for analyzing the received information and generating a speed-system distance curve which vividly reflects the operation state of a construction elevator. The monitoring system disclosed by the invention overcomes the own defects of detection equipment and test errors caused by human operation factors and has the advantages of high accuracy, wide range and distance testing range and no test limitation; the monitoring device is easy to assembly and disassembly, is also suitable for safety monitoring of equipment with similar structures, such as the elevator, and is wide in application range.

Method, system and computer readable program code for dynamic and evolutionary component placement in an event processing system having producers, consumers, a plurality of nodes between the producers and the consumers, and a flow graph representing operator components to be executed between the producers and the consumers. A description of a change to the system is received. At each node, next-hop neighbor nodes for each consumer are identified. A routing value is assigned to each next-hop neighbor node for each consumer and the routing values are updated according to an update rule that represents a chromosome in a routing probe. The update rule in a routing probe is selectively updated from a plurality of update rules at the consumer. The probability of selecting a particular update rule is reinforced or decayed based on the success of an update rule in allowing routing probes to create many different efficient routes. At each producer, nests of scouting probes are adaptively selected from an available set of nests and dispatched to execute hypothetical placement of a query by an independent agent called a “leader”. A placement of the operator components that minimizes performance cost of the system relative to the hypothetical placement is selected. Each scouting probe contains chromosomes that guide placement. Scouting probes in two different nests have different chromosomes. The performance cost of the hypothetical changed placement is evaluated and the performance evaluation is used to evolve at least one chromosome of a scouting ant in each nest.

The invention relates to the technical field of cold-chain logistics distribution path optimization, and further discloses a cold-chain logistics path optimization method based on the improved ant colony algorithm. The method comprises the specific implementation steps: firstly, constructing vehicle speed characteristic models in different time periods, and determining constraint conditions such as the distribution distance, the customer demand and a time window with the purpose of minimizing the total cost composed of the vehicle fixed cost, the transportation cost, the goods loss cost, the refrigeration cost and the carbon emission cost as the target; secondly, initializing ant colony algorithm parameters and cold-chain logistics distribution cost model vector parameters; thirdly, usingthe improved ant colony algorithm to optimize the distribution cost, and when all ants complete path search once, carrying out global pheromone updating of the next time; and finally, dynamically improving a pheromone attenuation coefficient and a pheromone increment updating rule when the improved ant colony algorithm is used for solving so as to continuously adjust the pheromone, expand an optimal solution search space and determine an optimal path and an optimal target function value.

The invention relates to a route designing method of a cognitive radio mesh network. The method mainly comprises the following three steps (see an attached map): first, establishing a novel cognitive radio mesh network distributed layered graph model; then, updating a route list by introducing an ant colony algorithm and providing a novel ant colony algorithm z Ant Net which is adapted to the structure and the data flow distribution of the cognitive radio mesh network; afterwards, conducting data transmission and carrying out analog simulation on a mesh network client end node to be in communication with the Internet through a route; and finally sorting the designing method and providing specific data and graphs of the method to be used as an overall result of the network route designing method at the time so that displaying evidence and reference can be supplied to users. Based on the tense present condition of network spectrum resources, the route designing method of the cognitive radio mesh network fully takes time, space factors and frequency spectrum parameters into account aiming at the dynamic spectral characteristic of the cognitive radio mesh network, and adaptive design is conducted on route interface distribution and routing strategy.

The invention discloses a construction method for an ant colony algorithm fractional order PID controller of an axial mixing magnetic bearing. The initial information amount of nodes in crawl regions of five control parameters of a proportionality coefficient Kp, an integration coefficient Ki, and a differentiation coefficient Kd, a differential order coefficient lambda and an integration order coefficient mu are set, all ants are placed on starting points and begin to crawl, optimizing is conducted on the five control parameters respectively, the information amount of each node is updated, and optimum control parameters are output. The five control parameters of the fractional order PID controller are optimized through global searching features of an ant colony algorithm to ensure that the parameter optimizing process has good local optimizing performance and high converging speed, and therefore calculation precision and calculation efficiency are improved, and system stability can be better guaranteed when model parameters of the axial mixing magnetic bearing change.

The invention belongs to the field of expressway rescue path planning, and particularly relates to an expressway multipoint cooperative rescue path planning method based on an improved ant colony algorithm. The method comprises the following steps that an expressway abstract road network is established according to expressway line data and traffic flow data; two optimization objectives of short path passing time and high path safety are comprehensively considered, and a path evaluation function is designed; based on the characteristics of expressway rescue path planning, an improved ant colonyalgorithm is designed; an optimal rescue path from each rescue point to each accident point is planned by adopting the improved ant colony algorithm; and a multi-rescue-point cooperative rescue modelis established, and a rescue vehicle dispatching scheme is determined by adopting an on-table operation method. According to the method, the targets of short path passing time and high path safety can be comprehensively considered, the path planning function of multi-rescue-point cooperative rescue is achieved, precious time is bought for expressway rescue work, and the method has great practicalvalue.

The invention belongs to the technical field of satellite communication, and relates to a load balancing routing method based on ant colony optimization in a low earth orbit satellite network. The routing method comprises the steps that any ant calculates the routing probability of a link adjacent to a current satellite based on the link distance, delay jitter and congestion degree between satellites, and selects a next hop satellite according to the maximum probability principle; forwarding the data packet to a next-hop satellite, updating pheromones on a current satellite passing path according to a local updating formula, and continuing to select the next-hop satellite according to a maximum probability principle; otherwise, forwarding the data packet to a target satellite, and updatingpheromones on all links on the routing topology according to local update and global update formulas; after all ants complete link routing of the satellite, selecting a path with the lowest time delay as a routing path of the current satellite. According to the invention, low time delay can be ensured, the flow can be guided to be distributed on an idle link, and the reliability of the whole network is improved.

The invention discloses a cloudsim platform based virtual machine batch deployment method. The method includes extending a hybrid ant colony algorithm and taking the extended hybrid ant colony algorithm as a virtual machine batch deployment strategy. The hybrid ant colony algorithm performs global pheromone updating by adopting the following formula of pheromone [j][i]=pheromone [j][i]*rho, wherein the rho is a pheromone residual factor, the formula II is a pheromone matrix from a virtual machine i to a physical machine j, wherein ants[k].pheromoneDeltaMatrix represents a pheromone variation matrix of a kth ant, and antNum represent of the quantity of ants. Compared with a greedy strategy, the deployment method has the advantage that load balance and resource utilization rate of a system can be heightened obviously.

The invention discloses a warehouse mobile robot path planning method based on an improved ant colony algorithm. The method comprises the following steps: firstly, dividing an ant colony into a plurality of sub-ant colonies; performing path search by using each sub ant colony, and sorting according to utility function values to obtain better ants; updating the pheromones to neighbor sub ant colonies, and outputting an initial path after iterating for a set number of times; if no obstacle exists between connecting lines of any two nodes in the initial path, the two nodes are directly connected,and path planning of the warehouse mobile robot is completed; according to the method, the path search results are sorted by utilizing the utility function, so that the pheromones have correct directivity, the probability that ants select shorter paths is improved, and blindness in the search process is avoided; through information exchange among the sub ant colonies, the searching capability ofthe whole ant colony and the speed of searching the shortest path are improved; and the intermediate nodes are smoothed, so that the turning times and the total length of the planned path are reduced,and the running efficiency of the warehouse is effectively improved.

The invention belongs to the technical field of edge computing, particularly relates to an edge computing task cooperative scheduling method, system and device, and aims to solve the problems that anant colony algorithm is prone to local optimization and cannot well solve the problems of low resource utilization rate and unbalanced virtual machine computing resource load in an edge environment. The method comprises the steps of obtaining a scheduling queue of an edge computing task, and initializing related parameters; starting from the first ant in the ant colony algorithm, obtaining a feasible solution of the current ant; if the solution is the optimal feasible solution, updating the optimal solution and the shortest task maximum completion time; updating local pheromones until all antscomplete solution searching; carrying out global pheromone updating, and carrying out local optimization in an iterative mode until the set number of iterations is reached, and obtaining a globally optimal feasible solution and the corresponding shortest task maximum completion time are obtained. According to the method, the ant colony algorithm is further prevented from falling into local optimum too early, and load balance, reasonable task scheduling and efficient resource utilization are guaranteed.

The invention discloses a robot path planning method, device and equipment and a storage medium, and the method comprises the steps: S1, carrying out the modeling through employing a grid method, initializing each parameter in an improved ant colony algorithm, and obtaining the improved ant colony algorithm through building a new heuristic function and a pheromone updating formula; s2, placing each ant at a starting point, and selecting a next node by utilizing a roulette mode and combining a state transition probability calculation formula in an improved ant colony algorithm until the whole path is searched; s3, after path searching is completed, pheromone updating is conducted according to a pheromone updating formula; and S4, repeating the steps S2 to S3 until the maximum number of iterations is reached, screening out an optimal solution, and determining the shortest path of the robot. According to the method, the problem of poor path planning effect caused by low convergence rate and easiness in falling into a local optimal solution when an ant colony algorithm is adopted for path planning in the prior art can be solved.

The invention discloses a method for optimizing energy consumption of an aluminum section workshop based on an ant colony algorithm and in view of time difference electricity price. According to the method, a production scheduling process of the aluminum section workshop is abstracted as a nonequivalent parallel machine scheduling problem in view of time difference electricity price and energy consumption; the ant colony optimization algorithm based on iterative computation is proposed on the basis of analysis on the scheduling problem, and ants realize scheduling of a machine and a workpiece through an iterative mode of machine-workpiece-machine, so that solution errors caused by independent scheduling of the machine and the workpiece in a conventional ant colony algorithm are reduced; in addition, a right-shift local search method is proposed according to the characteristics of time difference electricity price, so that the precision of a solution can be improved, the cycle frequency of ant colony computation can be greatly reduced, and the energy consumption of scheme solving is reduced.

The present invention discloses a method for optimizing discounted cash flow of project scheduling using ant colony algorithm. The method is used for solving resource constrained scheduling problem of discounted cash flow with multi-mode (MRCPSPDCF). Firstly, MRCPSPDCF model is built, the model considers overhead costs and a punishment mechanism besides ordinary cash inflow and outflow. Then a structural map is built for MRCPSPDCF model, accordingly, the scheduling problem is changed into a search problem based on diagram. Ant constructs solution of problems step by step with pheromone and heuristic information in structural map by using serial schedule generation system (SSGS). The algorithm comprehensive uses the pheromone and heuristic information based on time and cost to guide ant search. The algorithm can obtain better result when solving problem by comparison with existing other algorithm.

The invention provides a threat intelligence analysis method and system based on an ant colony algorithm, and relates to the technical field of network security, and the method comprises the steps: firstly obtaining flow information of a to-be-detected host, and then calculating the target pheromone of the flow information of the to-be-detected host through employing the pheromone of an ant colonyalgorithm; and if the target pheromone reaches a preset threshold, determining the flow information of the to-be-detected host as threat intelligence. The target pheromone corresponding to the flow information of the to-be-detected host is calculated based on the flow information of the to-be-detected host, whether the flow information of the to-be-detected host is threat information or non-threat information can be determined based on the target pheromone, complex flow information can be dealt with, the calculation operation is simple, manpower and material resources are saved, and the analysis time is shortened.

The invention provides a stereoscopic warehouse three-dimensional space path optimization method based on an ant colony algorithm. The stereoscopic warehouse comprises a plurality of goods shelves arranged in parallel. A single goods shelf is provided with a plurality of layers, goods channels are arranged between adjacent goods shelves, roadways are arranged outside the goods channels, and stacking machines used for horizontally moving and lifting AGVs are arranged on the roadways. The path optimization method comprises the following steps: establishing a coordinate system, the X axis being parallel to the goods channels, the Y axis being parallel to the goods channels, the Z axis being perpendicular to the ground, and determining coordinates of all target points; and adopting ant colonyalgorithm function iteration to obtain an optimal path. By reasonably arranging the shelf structure, the AGV can move in three dimensions, the distance between two points is easy to calculate, and thepath of a three-dimensional space is optimized by combining an ant colony algorithm.

The invention provides an intelligent fitness riding platform system based on a FreeRTOS embedded real-time operating system. The intelligent fitness riding platform system comprises a power supply control module, an ARM processor main control module, a Bluetooth communication module, an ANT+ communication module, a speed collection module, a dynamic LED lamp module, a reluctance control module and an alarm module. The system adopts the FreeRTOS embedded real-time operating system to perform task priority management, realizes the cooperation among multiple tasks, and improves the instantaneityand stability of the system. The ARM processor main control module of the system performs real-time capture, calculation and fitting on the riding speed of a riding platform in real time, and sends the data to a wireless communication module for wireless communication; and meanwhile, the ARM processor main control module receives the control information of the wireless communication module, performs decoding and data extraction on the control information, and sends the control information to the reluctance control module to perform real riding condition simulation. Periodical control of the dynamic LED lamp module is performed by a real-time speed calculated by the system, the system can be compatible with third party riding software through the ANT+ communication, and the stability of the wireless signal of the system is judged, alerted and alarmed by the alarm module.

The invention relates to the technical field of target optimization, and discloses an ant colony algorithm-based cargo optimal distribution method. The method comprises the following steps: obtaining cargo distribution information, and carrying out coding representation on the cargo distribution information to obtain a cargo distribution information coding result; establishing a multi-objective function of cargo distribution, and initializing ant colony algorithm parameters; placing a plurality of ants at the path node positions in the cargo distribution information coding result, and enabling the ants to select the node positions where the ants need to walk in the next step according to the volatilization factors and the heuristic factors until all nodes are traversed, and ending the algorithm of this round; after each round of algorithm iteration, updating the pheromones of the algorithm until the maximum number of iterations is reached, selecting the path with the minimum multi-objective function value from all the paths as a cargo distribution optimal solution of the multi-objective function, and outputting an optimal distribution path result of cargo distribution. The invention further provides a cargo optimal distribution system based on the ant colony algorithm. According to the invention, optimized cargo distribution based on the ant colony algorithm is realized.

The invention discloses a robot path planning method based on a final distance index. The robot path planning method comprises the following steps of: S1, creating a robot environment map by adoptinga grid method; S2, searching the shortest path of the environment by adopting an ant colony algorithm. The ant colony algorithm comprises the following steps that: S21, parameters of the ant colony algorithm are initialized; S22, m ants are placed at a starting point to start searching; S23, a probability selection formula is used to select a next moving grid; S24, whether all the ants reach a target point or not is judged, if so, the step S25 is executed, and if not, the algorithm returns to the step S23; S25, a final distance index is updated according to all feasible paths obtained in the present generation; S26, whether the maximum number of iterations is reached or not is judged , and if so, the algorithm is terminated; if not, 1 is added to the number of iterations, and the algorithmreturns to the step S22. The robot path planning method further comprises the step S3, namely, taking the shortest path obtained in the step S2 as a planned optimal path; The method not only improvesthe global optimal solution, but also improves the convergence rate.