780 results about "Pheromone" patented technology

The invention provides a desilting robot intelligent control method and system based on deep learning, and relates to the technical field of intelligent control, and the method comprises the steps: collecting environment information through a multi-source sensor, extracting environment features through a convolutional neural network and a recurrent neural network, and building a three-dimensional dynamic model of a desilting robot operation environment; based on the model and a hierarchical reward function, training a dual neural network by using a depth deterministic strategy gradient algorithm to obtain an intelligent control strategy model; a motion track output by the model is optimized by an improved ant colony algorithm of a double-target pheromone updating strategy based on path length and energy consumption and then is input into a layered model prediction controller, and cooperative control over a dredging robot propeller and a dredging device is achieved. The working efficiency and the energy utilization rate of the dredging robot are effectively improved, and the dredging cost is reduced.

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention provides an inspection robot path planning method based on a transformer substation environment, and belongs to the technical field of transformer substation inspection, and the method comprises the steps: constructing a transformer substation weighted map, obtaining a plurality of optimal planning paths through employing an iteration method, and selecting the optimal planning path with the highest pheromone concentration as the optimal inspection path of an inspection robot. Comprising the following steps: calculating heuristic estimation cost of each node to obtain an initial planning path, introducing an inspection importance weight factor to calculate heuristic information between the nodes, and evaluating path fitness to obtain an optimal initial planning path in a current iteration process; calculating a pheromone increment between the nodes to update a pheromone concentration between the nodes; and calculating a selection probability based on an ant colony algorithm, and selecting a next node according to the selection probability until a planned path is constructed as an optimal planned path of the iteration. The problem that an existing automatic inspection system is insufficient in path planning in a complex environment is solved.

The invention relates to the technical field of autonomous navigation, in particular to an AUV (Autonomous Underwater Vehicle) multi-sensor fusion autonomous navigation system based on ant colony-particle swarm collaboration.The AUV multi-sensor fusion autonomous navigation system is characterized in that a sensor weight decision module constructs a pheromone matrix based on an ant colony algorithm, retrieves a mapping relation between historical errors and pheromone concentration in real time and dynamically generates a sensor priority sequence; adaptive weight distribution in a data conflict scene is realized; the dynamic path correction module is combined with a particle swarm algorithm and pheromone concentration gradient constraint to balance global path optimality and real-time obstacle avoidance requirements in optical camera obstacle deviation correction; and the closed-loop coupling module is used for converting sonar and geomagnetic residual errors into pheromone concentration correction values and confidence coefficient regulation factors by fusing residual error compensation and path tracking error feedback, so that an error traceability-weight calibration-path optimization closed loop is formed, sensor drift is inhibited, and the long-endurance navigation precision is improved.

The invention discloses a multi-target path planning method based on a topological map, and relates to the technical field of path planning of automatic driving vehicles, and the method comprises the following steps: S1, constructing a topological map comprising road lengths and important nodes; s2, calculating the distance between the connected road nodes, and establishing an adjacent matrix of the road nodes; s3, selecting a starting point and a plurality of target points; s4, performing multi-target path optimization calculation through an ant colony algorithm, introducing a growth type function, performing adaptive adjustment on ant pheromone volatilization, and designing updating strategies of local pheromones and global pheromones; and S5, designing an evaluation function, and planning an optimal multi-target path by comprehensively considering the conditions of the shortest global path length and the minimum number of optimization iterations of the multi-target ant colony algorithm. By means of the mode, the optimal multi-target path can be found on the topological map through the ant colony algorithm, and the path planning efficiency is improved.

The invention provides a quality data online management method and system in the processing process of traditional Chinese medicine decoction pieces, and relates to the technical field of data processing, and the method comprises the steps: carrying out the real-time monitoring of key quality indexes in the processing process of the traditional Chinese medicine decoction pieces through the final fusion data; when the abnormal data mode is detected, preset early warning is automatically triggered according to key features in abnormal data; using an ant colony optimization algorithm to optimize parameters of the neural network model used for quality prediction; searching a final solution in the parameter space by simulating the foraging behavior of ants and a pheromone updating mechanism so as to obtain optimized parameters; retraining the neural network model by using the optimized parameters to obtain a trained neural network model; and predicting the quality trend of the traditional Chinese medicine decoction pieces according to the trained neural network model. According to the invention, the prediction precision and real-time performance of the model are improved, and the production cost is reduced.

The invention relates to the field of software engineering, in particular to a chaotic engineering application scene experiment implementation method based on an ant colony algorithm. Carrying out structured processing and time sequence modeling on the collected complex original data; and importing the processed information into an ant colony algorithm, determining parameters such as the number of ant colonies, pheromone concentration and evaporation rate during initialization, initializing a pheromone matrix, and assisting decision making by using resource use conditions and related service information. In algorithm execution, ants select paths according to a probability formula, pheromone and heuristic index weights are determined according to fault injection history and service information, a pheromone matrix is adjusted according to volatilization, addition and update rules after traversal, and the steps are repeated until conditions are met. The algorithm outputs an adjacent matrix containing information of pheromones, nodes and sides, and is used for analyzing a fault path, influence, propagation and generation of a visual chart. The method can improve the experiment efficiency, accuracy and dynamic adaptability, and improve the reliability and toughness of the system.

The invention relates to a biological model technology, and discloses a workshop inspection task allocation method based on an ant colony algorithm, a control system and a medium, and the method comprises the steps: carrying out the modeling of the ant colony algorithm based on a workshop map; initializing an ant colony algorithm, and setting related parameters of the algorithm; starting to construct paths based on an ant colony algorithm; evaluating the path quality according to the estimated inspection duration of each path; after all ants complete path construction, performing volatilization updating on pheromone concentrations of all edges according to a pheromone volatilization coefficient; and according to the quality of the path, carrying out pheromone enhancement on the edge on the path, adding the volatilized pheromone concentration and the enhanced pheromone concentration to obtain a new pheromone concentration; and after the iteration of the ant colony algorithm is finished, according to the newest pheromone concentration, respectively planning inspection paths for the inspection robots. The invention further discloses a control device. The invention aims to improve the efficiency of allocating workshop inspection tasks to a plurality of inspection robots.

The invention discloses an AGV path planning method based on construction of a time cost function and improvement of an ant colony algorithm, and relates to the technical field of AGV intelligent guide robots, and the method comprises the steps: S1, constructing a grid environment map, and setting initialization parameters; s2, placing a plurality of ants at a starting point of the grating environment map, wherein each ant starts to independently search a path; s3, when all ants walk to the target node, side pheromone updating and pheromone volatilization are carried out according to the time cost of the walking path of each ant, whether the current number of iterations reaches the preset maximum number of iterations is judged, if yes, the next step is carried out, and if not, the S2 is repeated until the preset maximum number of iterations is reached; and S4, selecting the path with the optimal time cost as the final path according to the time cost of the path on which the ant walks in each iteration. According to the invention, the accuracy of path planning is improved, and the total operation time of the AGV is reduced.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention discloses a path planning method for a mine rescue robot, relates to the field of path planning of an intelligent algorithm, plans an optimal path of the mine rescue robot on a rasterized map, and aims to solve the problems of low search efficiency, multiple turning times, path redundancy and easiness in falling into a local optimal solution in the initial stage of iteration. The method comprises the following steps: establishing a grid map environment model; changing a heuristic function of a pseudo-random probability ACS algorithm to obtain an initial path; performing non-uniform distribution on initial pheromones through the initial path, and performing iteration; obtaining a neighborhood search mechanism according to a map environment, and selecting path nodes to form a path; smoothing each path successfully reaching the end point; a pheromone reward and punishment mechanism is added, and arrangement of pheromones is changed; and next iteration is carried out to obtain a final optimal path. The method is suitable for path planning in the fields of Internet of Things, cloud computing and the like, and an innovative solution is provided for path planning of the mine rescue robot.

The invention discloses an unmanned aerial vehicle cluster dynamic path planning method and system based on a Gaussian plume model. The method comprises the steps that chemical plant leakage gas concentration, wind speed data and thermal imaging data are collected; dynamically correcting the diffusion coefficient of the Gaussian plume model according to the wind speed data, and generating a leakage source probability distribution diagram in combination with the leakage gas concentration and the thermal imaging data so as to determine the concentration gradient value and pheromone field intensity of each position of the chemical plant, thereby planning the search path of the unmanned aerial vehicle cluster; and performing grid division on the leakage source probability distribution diagram to determine a search task, performing search task distribution according to an auction algorithm and the real-time state of the unmanned aerial vehicle, performing leakage source search positioning in combination with a search path, and finally marking a leakage point and returning the leakage point to a command center of the unmanned aerial vehicle cluster in real time. Dynamic path planning of the unmanned aerial vehicle cluster is realized in combination with multi-source data fusion, the unmanned aerial vehicles are guided to preferentially search high-probability regions through task allocation, and the positioning precision of leakage sources and the resource utilization rate of the unmanned aerial vehicles are improved.

The invention relates to a transformer substation unmanned aerial vehicle three-dimensional path planning method based on an improved ant colony algorithm. The method comprises the following steps that an electric power unmanned aerial vehicle inspection three-dimensional environment model is generated, and self-adaptive grid division is carried out; performing path search on the initialized ant colony in the electric unmanned aerial vehicle inspection three-dimensional environment model after grid division; each ant individual in the ant colony selects a next node according to a dynamic transition probability calculation model, the ant individual determines the next node which actually moves through a variable step size search behavior, and path information of each ant individual from a starting point to an ending point is recorded; when all the ant individuals arrive at the end point, screening the optimal ant individual of the iteration, executing the artificial ant foraging behavior, and updating the pheromone concentration of each path in the space; iteration is repeated until the preset number of iterations is reached, iteration is ended, and the path with the highest pheromone concentration is selected as the final path of the unmanned aerial vehicle.

The invention discloses a multi-algorithm fusion transmission path planning method and device, terminal equipment and a storage medium, and belongs to the field of transmission path planning, and the method comprises the steps: obtaining state data of each node of a power grid, repeatedly executing a position updating operation until a convergence condition is satisfied, and obtaining a target particle position and a target ant position; the position updating operation comprises the step of updating the particle speed and the particle position according to the current particle speed, the particle position and the pheromone; according to the current ant position, the pheromone and the global optimal solution, the ant position and the pheromone are updated; when the convergence condition is not met, taking the updated parameter as the current parameter of the next iteration; otherwise, outputting the updated particle position and ant position; and determining a final transmission path according to the target position. By implementing the method, the advantages of the particle swarm algorithm and the ant colony algorithm can be combined, so that the problem that a single algorithm is easy to fall into local optimum or slow in convergence in transmission path planning in the prior art is solved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on an improved multi-target ant colony algorithm, and the method comprises the steps: constructing two ant groups and two pieces of corresponding heuristic information according to a first optimization target and a second optimization target; dividing each ant group into a plurality of ant subgroups, and constructing an ant cluster; each unmanned aerial vehicle competes to obtain the execution right of the next task based on member pheromones of the unmanned aerial vehicle; the unmanned aerial vehicle obtaining the execution right of the next task selects the next task according to the state transition rule; updating local pheromones only when one task allocation solution is constructed by the ant clusters; updating global pheromones of the two ant groups only when all the ant clusters complete construction of task allocation solutions; and when iteration is completed, all solutions in the file set form a final task allocation scheme, and the unmanned aerial vehicle cluster executes tasks according to the task allocation scheme.

The invention provides a data transmission path generation method and device for a distributed wireless sensor, and belongs to the technical field of data transmission, and the method comprises the steps: constructing a state space based on the state of a sensor network, and constructing an action space based on a selectable transmission strategy of the sensor network in each state in the state space; determining an optimal data transmission strategy set based on the state space and the action space, and adjusting transmission parameters of the plurality of sensors based on the optimal data transmission strategy set; updating the sensor network based on the transmission path set, the optimal data transmission strategy and the adjusted sensor parameters to obtain a distributed sensor network; and determining a target transmission path based on the pheromone concentration of each transmission path in the distributed sensor network. According to the data transmission path generation method of the distributed wireless sensor, the technical problem that the efficiency of the data transmission process is relatively low in the data transmission path generation method of the distributed wireless sensor in the related technology is solved.

The invention discloses a large yellow croaker fry enhancement and release wild training device and method, and relates to the technical field of marine biological resource conservation, and the large yellow croaker fry enhancement and release wild training device comprises a water tank for containing water, and a plurality of fish reefs are placed in the water tank; the light source is arranged above the water tank; the plurality of wave making pumps are uniformly arranged on one side of the inner wall of the water tank; the throwing structure is used for throwing natural enemy pheromones into the water tank; the camera is arranged on the inner wall of the water tank; the controller is electrically connected with the camera, the light source and the wave making pumps, the camera is used for collecting behavior characteristic information of the large yellow croaker in wild training and sending the behavior characteristic information to the controller, and the fish behavior analysis system comprehensively analyzes swimming and fish reef use conditions of the large yellow croaker fry in the water tank and analyzes and judges the wild training effect of the large yellow croaker fry. The method has the advantages that the capability of the larimichthys crocea fry adapting to the marine natural environment is enhanced, and the survival rate of the released fry is increased.

The invention relates to a multi-mobile-robot path planning method based on swarm intelligence, and belongs to the technical field of robot path planning. The method comprises the following steps: setting the size of a map, starting and target positions and colors of a robot, creating a preset grid map, and initializing an ant colony algorithm, a genetic algorithm and a pheromone system; iteratively searching paths for the mobile robots with different starting points at the same time through an ant colony algorithm; a genetic algorithm is used for optimizing the path, conflict detection and processing are carried out after the path is optimized, and it is ensured that the final path is free of conflicts; and when the maximum number of iterations is reached, outputting the shortest path that each robot arrives at the target node and no collision exists between the robots. According to the path planning method, unnecessary turning can be reduced, the convergence speed of path searching is obviously improved, robot conflicts can be avoided, and the actual requirements of multi-robot path planning are met.

The invention discloses a path planning method based on a pheromone enhanced ant colony algorithm, and particularly relates to the technical field of path planning. By constructing an initial pheromone matrix, the ratio of the linear Euclidean distance between a path starting point and a path end point to the sum of the Euclidean distance from the path starting point to a candidate grid position and the Euclidean distance from the path end point to the candidate grid position is calculated, and the initial pheromone concentration is distributed near a theoretical optimal path; an elite solution pheromone enhancement strategy is adopted, all generated paths are sorted according to path quality in each iteration, and a high-quality path is selected, copied and then combined with a global optimal path to be used for updating a pheromone matrix; a prospective steering optimization strategy is utilized, punishment is applied to redundant turning in the searching process, and the pheromone updating amount is determined through the path length and the turning frequency; through multi-round iteration, stable pheromone distribution is realized, so that a final optimized path is obtained, the search efficiency is improved, and the method is suitable for a path planning task in a complex environment.

The invention relates to the field of path planning, in particular to an ant colony algorithm improvement method and system for unmanned aerial vehicle path planning, and the method comprises the steps: initializing a path diagram and an ant colony, carrying out the iteration through employing the ant colony algorithm, recording a complete path and a sub-path of an ant, and calculating the optimal degree of the path according to the distance and wind direction factors, comprehensively evaluating the sub-path quality; the pheromone volatilization amount and release strategy are adjusted according to the optimization degree, the high-optimization-degree sub-path participates in pheromone release, and the rest of the sub-paths only participate in volatilization; and updating the pheromone concentration in each iteration, after the maximum iteration number is reached, selecting the path with the highest optimal degree as the optimal path, converting node coordinates of the path into GPS coordinates, uploading the GPS coordinates to the unmanned aerial vehicle, and completing path planning. According to the method, the pheromone updating strategy is improved, and only the sub-paths with high optimization degree participate in pheromone release, so that the algorithm can be prevented from falling into a local optimal solution, the global search capability is enhanced, and the unmanned aerial vehicle path planning efficiency and accuracy are improved.

The invention discloses a chaos annealing ant colony-based adversarial blocking timeliness emergency material transportation toughness intelligent decision-making method, and relates to the technical field of intelligent emergency scheduling and optimization. The method comprises the steps of 1, extracting scene data and converting the scene data into model parameters; step 2, constructing a mathematical model for survivability optimization; 3, initializing algorithm parameters and generating an initial path; 4, dynamically adjusting the temperature and the pheromone volatilization rate; 5, generating an adversarial blocking perception path and implementing detour repair; 6, updating pheromone distribution based on an annealing criterion; and step 7, locking the critical path and outputting an optimal transportation scheme. According to the method, timely delivery of materials can be guaranteed in a complex road network blocking and emergency demand scene, the timeliness and toughness of an emergency transportation system are improved through a chaos annealing mechanism and an adversarial blocking simulation technology, the method can be widely applied to the fields of natural disaster rescue, public health event response and the like, and a scientific basis is provided for emergency decision making.

The invention provides a logistics trajectory prediction optimization system and method based on big data. The method comprises the steps of obtaining weather data, time data and traffic flow data of logistics transportation; based on the weather data, the time data and the traffic flow data, carrying out comprehensive prediction on the logistics tracks by adopting an ant colony algorithm to obtain comprehensive pheromone concentration corresponding to each logistics track; according to the pheromone comprehensive concentration corresponding to each logistics track, optimal path track pushing is carried out, and driving preference feature scores are collected in the logistics transportation process; correcting the comprehensive pheromone concentration corresponding to each logistics track based on the driving preference feature score, and updating the optimal path track pushing in real time; by adopting the scheme, the optimal path pushing track can be updated and optimized according to the driving preference feature score in the logistics transportation process, so that factors influencing the transportation efficiency, such as frequent speed reduction and bypassing, are avoided, and the stability of logistics transportation is improved.

The invention provides a biosynthesis inhibitor of migratory locust aggregation pheromone 4-vinyl anisole and application of the biosynthesis inhibitor. The biosynthesis inhibitor of migratory locust aggregation pheromone 4-vinylanisole reduces the activity of 4-vinylphenol methyltransferase, and the 4-vinylphenol methyltransferase contains an amino acid sequence selected from the following sequences: (a) a sequence as shown in any one of SEQ ID No.1 and SEQ ID No.3; and (b) a sequence which is obtained by deleting, inserting and / or substituting one or more amino acid residues in the sequence shown in any one of SEQ ID No.1 and SEQ ID No.3 and has the activity of catalyzing generation of migratory locust aggregation pheromone 4-vinyl anisole. The inhibitor can be applied to biological prevention and control of migratory locusts, can keep locusts in a harmless scattered state instead of killing the locusts, provides a new method for biological prevention and control of migratory locusts, and has a wide application prospect.

The invention provides a path planning method and system based on HER-ACO, and belongs to the technical field of robot path planning. The method comprises the following steps: S1, acquiring an environment grid map, wherein the grid map comprises a starting point, a target point and obstacle distribution information; s2, introducing an HER algorithm to improve a traditional ant colony algorithm ACO, and searching a feasible path from a starting point to a target point in the grid map by using the improved ant colony algorithm as an initial planning path; the improvement comprises the following steps: introducing an HER thought and a dynamic pheromone volatilization mechanism; increasing a direction weight factor, a neighborhood obstacle factor and a future obstacle factor; and S3, smoothing the initial planning path based on the feasibility check of the linear path, removing redundant intermediate nodes, and outputting a final planning path. A simulation result shows that the scheme has remarkable advantages in a complex environment, particularly has excellent performance in the aspects of path quality and convergence speed, and is suitable for scenes such as robot navigation and concrete robot path planning.

The invention relates to the technical field of network organization, and provides a monitoring network organization method, device and equipment, a program product and a storage medium. The method comprises the following steps: obtaining optimal positions and optimal connection paths of a plurality of target monitoring nodes in a monitoring network based on a target path search algorithm; adjusting the current positions and the current connection paths of the plurality of target monitoring nodes based on the optimal positions and the optimal connection paths; the target path search algorithm is determined based on a target pheromone updating algorithm and a target path security updating algorithm, and the target pheromone updating algorithm updates the pheromone concentration based on real-time feature data of a monitoring target and a monitoring environment; the target path security update algorithm updates the security function value of the path based on the real-time feature data of the path obstacle. According to the invention, the position of the monitoring node and the connection path can be rapidly adjusted when facing real-time changes of the monitoring target, the monitoring environment and the path obstacle, and the monitoring effect is improved.

The invention discloses a multi-agent dynamic task integrated allocation method and system based on a labor division mechanism. The method comprises the following steps: constructing a multi-agent execution dynamic task allocation variable model by using a six-tuple; constructing a multi-agent dynamic task allocation index model; constructing a multi-agent labor division mechanism ant colony in combination with a labor division mechanism and a task allocation constraint set; determining an ant selection probability in combination with task capabilities of multiple agents, gradually selecting ants, and constructing a task allocation sequence of each agent according to a state transition rule; updating pheromones on each distribution sequence through state transition, calculating an evaluation value through an index model, if iteration is performed for the first time, taking the current evaluation value as an optimal evaluation value, otherwise, if the current evaluation value is greater than the optimal evaluation value, taking the current evaluation value as the optimal evaluation value; the ant colony is updated and iterated, the solution corresponding to the optimal evaluation value is the optimal solution, and the optimal task allocation sequence of the intelligent agent is obtained. According to the invention, the efficiency and performance of the multi-agent system are improved, and complex tasks and environments can be better coped with.

The invention relates to the technical field of machine path planning, and discloses an island ant colony path planning method for an unmanned trolley, which comprises the following steps: after generating a grid map, decomposing global path search into path exploration between adjacent islands; non-uniform pheromone initialization is adopted between adjacent islands, local paths between the adjacent islands are searched respectively, and the position of a next grid is selected in the searching process by combining an improved node transition probability with a heuristic function and pheromone concentration; and when global pheromone distribution is updated, an optimal path is rewarded, and a worst path is punished. And combining the local paths of the island pairs to obtain global paths, and selecting the global path with the shortest length as the optimal driving path. According to the method, a segmented island strategy is adopted, the complexity of single search is reduced, the early random probing process is shortened through non-uniform initialization pheromones, the convergence speed is increased, the number of deadlock ants is effectively reduced, and the precision, convergence and stability of path planning are improved.

The embodiment of the invention discloses a battery pack composite material upper cover parameter determination method and device, electronic equipment and a medium. The method comprises the following steps: enabling a preset ant to search in a preset layer thickness range and a preset layer angle range according to an initialized pheromone concentration and initialized heuristic information to obtain an initial path; inputting each target position point of the initial path into the composite material upper cover finite element model to obtain a first-order modal frequency; updating heuristic information of the target position point, and updating pheromone concentration of the target position point according to the first-order modal frequency; and enabling the preset ant to continue searching iteration according to the updated heuristic information and pheromone concentration of each target position point and the heuristic information and pheromone concentration of other position points to obtain final parameters of the battery pack composite material upper cover. According to the scheme, the optimal upper cover parameter is searched and determined based on the improved ant algorithm, so that the optimal upper cover parameter is quickly and efficiently determined for actual industrial application.

The invention belongs to the technical field of robot path planning, and provides a mobile robot three-dimensional path planning method based on an adaptive ant colony algorithm, and the method comprises the steps: determining a starting node and a target node in a rasterized three-dimensional search space; iteratively searching the optimal path from the starting node to the target node, and outputting the optimal path from the starting node to the target node obtained by the last iterative search; each iterative search comprises the following steps of: A, putting all ants to an initial node; b, generating an attenuation factor which is in negative correlation with the number of iteration search times, and adjusting heuristic information among the nodes by using the attenuation factor; c, each ant selects the next node according to the heuristic information between the nodes and the pheromone concentration between the nodes; d, updating the pheromone concentration of the road section passed by the ants; the invention further discloses a three-dimensional path planning device for the mobile robot, a computer program product and the mobile robot. According to the method, the convergence and efficiency of path search are improved, and the three-dimensional path planning time is shortened.

The improved ant colony algorithm integrating global information enhancement and pheromone suppression strategies is provided based on the problems existing in a traditional ant colony algorithm and used for solving the path planning problem of the welding robot, the actual contribution of nodes to the path quality is comprehensively considered by introducing the global information enhancement strategy, and the path planning precision of the welding robot is improved. The guiding effect of high-quality nodes on ant path finding is improved, and then the search quality of the algorithm is improved. Meanwhile, the pheromone suppression strategy enables the algorithm to find out inferior fragments from historical information, and interference of inferior nodes on ant path finding is reduced. Simulation results show that the GIEPI-IACO algorithm has a remarkable effect on solving the welding robot path planning problem, and the overall performance of the GIEPI-IACO algorithm is superior to that of a basic ant colony algorithm and an existing improved ant colony algorithm.