173 results about "Heuristic function" patented technology

The invention relates to a dispatching and route-planning method for multiple AGVs used for material transportation in a factory. The method comprises the following steps: (1) modeling material transportation scenes in a factory, including the travelling routes of AGVs, AGV charging points, loading and unloading points and standby zones for AGVs; (2) storing allocated tasks in a queue; (3) findingout one AGV closest to a current-task issuing site from a set of available AGVs; (4) calculating the shortest route from a current-task start point and a current-task stop point by using an A* algorithm; and (5) calling a time-window algorithm for maintenance of a time-window vector table of the shortest route obtained in the step (4). Compared with traditional A* algorithms, a heuristic functionin the A* algorithm used in the invention takes the traveling cost and turning cost of the AGVs on roads in a workshop into consideration, the time-window vector table of each route section is maintained, and whether conflicts exist in planned routes is determined by judging whether superposition exists; so the problems of collision conflicts, deadlock and the like of AGV route planning are effectively overcome.

The invention relates to an unmanned aerial vehicle route planning method based on a Dubins route and sparse A* searching (the method is called a Dubins-SAS route planning method for short), and belongs to the technical field of route autonomous planning. According to the method, the Dubins route and a sparse A* searching algorithm are combined, Dubins route length is used as a heuristic function of the sparse A* searching algorithm, the heuristic function is used for searching a space for nodes, and unmanned aerial vehicle route planning is achieved. The distance between the nodes can be estimated accurately, the accuracy of the heuristic function (also called heuristic function cost) is improved, the number of the nodes needed for expanding for completing route searching can be reduced, then route planning time is shortened, and unmanned aerial vehicle route planning efficiency is improved.

The embodiment of the invention provides an obstacle avoidance path planning method based on improved mechanical arms D* for achieving dynamic obstacle avoidance path planning of mechanical arms. The method comprises the steps that all components of the mechanical arms and obstacles in the environment are converted into a cuboid envelope through a hierarchical bounding volume method, and a relevant algorithm is designed for achieving fast collision detection between the mechanical arms and between the mechanical arms and the environment; according to spatial mechanical arm characteristics, a path search algorithm based on a heuristic function is obtained; obstacle avoidance path planning applied to seven-freedom-degree mechanical arms D* is improved, and dynamic obstacle avoidance path planning of the seven-freedom-degree mechanical arms is completed. According to the technical scheme, the method is used for achieving dynamic obstacle avoidance path planning of the seven-freedom-degree mechanical arms based on the improved mechanical arms D*.

The invention provides a live line work mechanical arm autonomous obstacle avoidance path searching method based on movement primitive libraries. A complete mechanical arm kinematic model and a mechanical arm simplified kinematic model are built, and a mechanical arm working environment is built; the movement primitive libraries are designed in a classified mode, and corresponding movement primitives are designed for the various libraries to serve as library members; distance calculation functions between a mechanical arm and a target point and between the mechanical arm and an obstacle are built in a classified mode, and the danger area threshold value and the target point arriving precision threshold value are set; a heuristic function hd(x) in the Cartesian space and a heuristic function hc(x) in the articulate space, and a heuristic rule are created, and heuristic searching is achieved; and the current pose and the expected pose of the mechanical arm and the Cartesian space distance of the obstacle are calculated, and a path is planned according to the set judgment condition. Mechanical arm operation scene information can be built rapidly and precisely, it is guaranteed that the obstacle avoidance path searching result has suboptimality and completeness, the path can be planned rapidly under the premise of meeting the kinematic constraint, and the stationarity of the mechanical arm movement process is guaranteed.

This invention discloses a carrying guidance system in the route optimum domain, which comprises the following steps: establishing a biparametric path unit standard dynamic file; considering the path rational constrained and joint failure constrained; using the method of heuristics weight to establish the optional path set off line; improving A* heuristics function to increase the efficiency of optional path set which satisfies the customer rational constrained and joint failure constrained; Off-line optional path set coded storing and backtrack on line; screening of on-line dynamic path, supplementary searching and multiple path issue.

The invention discloses an improved A* robot optimal path planning method suitable for a warehouse environment. The improved A* robot optimal path planning method comprises the steps of: firstly, designing an effective warehouse model capable of flexible expansion, including the design of shelf distribution and a road operating rule; simplifying a path planning problem into path planning problems among nodes according to the road operating rule in the warehouse mode; then adopting an improved A* algorithm to search for an optimal path between two nodes, wherein the calculation of a heuristic function includes estimation of a steering price, a Manhattan distance and bypass distance; and finally, adding a path between an initial position of a robot and an initial node as well as a path between a target position and a target node into a previous node list to form a complete path list. The nodes in the path list correspond to positions in an actual warehouse, the optimal operating path of the robot is obtained, and the operating paths of the robot are extended into an operating state list of the robot.

The invention relates to a service robot optimal path program method based on the heuristic function, belongs to the technical field of a path program method in a mobile service robot navigation system and solves problems of low efficiency and poor searching accuracy existing in a currently-used optimal path program method in the prior art. According to the method, an A-Star algorithm of an improved heuristic function is employed, as the direction angle and the distance have different physical dimensions, normalization processing on the corresponding distance and the corresponding direction angle of each associated node is carried out, repeated access to one same node is avoided, the searching space is greatly reduced, and the algorithm operation time is shortened. According to the method, the heuristic function contains two factors of the direction and the distance, as the direction angle and the distance have different physical dimensions, normalization processing on the distance and the direction is proposed, searching efficiency is improved, and the optimal path is programmed.

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 present invention discloses a new path planning method based on regular grid DEM data. According to the new path planning method, the distance and the slope are used as the evaluation indexes of the path search; in order to balance the interaction between the distance and the slope, the complete function g(n) of a DA* algorithm is designed based on the distance function of the space path and the slope function constructed with the exponential function; the parameter in the function is calculated with the DEM data, and the new complete function g(n) is optimized so as to be adapted to the change of different resolutions of the DEM data; based on the same strategy and the function construction method, the new heuristic function h(n) of the DA* algorithm is constructed; and in order to improve the search efficiency of the algorithm, by setting the initial weight and dynamically adjusting the weight in the search process, the influence of the complete function and the heuristic function on the evaluation result is changed, such that the new constructed DA* path finding algorithm can efficiently search the appropriate path. According to the preset invention, the new path planning method can self-adapt to the DEM data resolution, and can be used in different application environments.

The invention discloses a block-based subgraph construction and distributed graph processing method. The block-based subgraph construction method comprises partitioning a graph, relabeling summits in the graph, associating intervals with shards, partitioning blocks and constructing subgraphs, and employs a heuristic and lightweight SGP (Streaming Graph Partition) method to partition a graph, and a user-defined heuristic function to designate the summits to P subgraphs in order; the method possesses the characteristics of high performance and low edge cutting rate, and considers subgraph work load balance. According to a distributed graph processing system, after partitioning, subgraphs perform communication by employing a whole memory block as a unit; compared with the fine grit communication mode of the prior art, the distributed graph processing system exchanges data based on a memory block, and can fully utilize network bandwidth and reduce communication time.

The invention discloses a heuristic shortest path search method based on direction optimization, which comprises the steps that expandable nodes of the current nodes are subjected to directional selection; a node search scope is reduced; according to a heuristic search function, satisfactory path estimated values of the nodes are compared; the node with a lower weight value is selected preferentially; information of a father node of the node is recorded; and road nodes are searched step by step in a depth-first mode till all the expandable nods are ergodic. The method has the advantages that the ergodic number of the nodes is reduced greatly by using a direction optimization principle; the search speed is increased; in addition, the heuristic function is introduced, so that the blindness in a node expanding process by a depth-first method can be reduced; the priority is given to the optimal node for expansion, so that the shortest path can be found at an early search stage; and multiple alternative paths can be provided.

The invention discloses a mobile robot path planning method. The method comprises the following steps: S1, creating a robot environment map by adopting a grid method, and defining a start point and atarget point; S2, searching an environment shortest path by adopting an ant colony, wherein the ant colony algorithm contains the following steps: S21, initializing parameters of the ant colony algorithm; S22, placing m ants at the start point, beginning searching to obtain a feasible path node grating; S23, selecting the next step of moving grid by utilizing a distance heuristic function, and adding the current grid into a tabu table; S24, judging whether all ants reach the target point, if all ants reach the target point, performing the step S25, or returning to step S23; S25, performing pheromone updating by utilizing a path deviation amplifying strategy; and S26, judging whether reaching the maximum number of iterations, ending the ant colony algorithm if reaching the maximum number ofiterations, or adding one on the number of iterations and returning to step S22; and S3, taking the shortest path obtained in the step S2 as the optimal path of the planning. The planning method disclosed by the invention not only improves the global optimal solution, but also improves the convergence speed.

The invention discloses a mobile robot route planning method with an ant colony algorithm. The method comprises that a grid map of a mobile robot is formed according to a grid method, distribution ofinitial information elements is formed according to barrier information of origin and terminal points, an expected heuristic function is formed according to a critical barrier influential factor, theinformation elements are updated according to an information element update formula, parameters of an information element heuristic factor and an expected heuristic factor are adjusted dynamically according to a fuzzy algorithm, and an information element volatilization coefficient is adjusted dynamically. A batter optimal path solution can be obtained, and the convergence speed is higher. According to simulated experiments, the method is feasible and effective.

The invention discloses an improved ant colony algorithm-based path planning method. In comparison with the traditional ant colony algorithm, an ant path search strategy is modified to jump out of partial obstacles with special shapes; a heuristic function in the traditional ant colony algorithm probability selection formula is modified, and target node information is added to the heuristic function to accelerate the convergence of the algorithm; and a volatilization coefficient in the traditional ant colony algorithm probability selection formula is modified, and time and space information isadded into the volatilization coefficient to improve the adaptability of the algorithm. Through the above improvements, the convergence time of the ant colony algorithm can be effectively reduced, and the operation efficiency is improved.

The invention discloses an AGV (automated guide vehicle) path planning method based on an A* algorithm. The AGV path planning method comprises steps as follows: firstly, an environmental map is established with a topological method, and the abstract environment is described with a specific data structure; secondly, a heuristic function is established with the A* algorithm, a reasonable evaluationfunction is set, and a path between nodes on the map is solved; finally, according to a specific task requirement of an AGV, a reasonable price mechanism is made to guide the algorithm for obtaining of the optimal solution. With adoption of the method, the working environment of the AGV can be described accurately, storage space of the map is reduced, and efficiency and accuracy of the algorithm are improved.

The invention discloses a global path planning method based on a weighted A * algorithm for expanding a search neighborhood, and the method comprises the steps: S1, carrying out the environment modeling according to the point cloud information collected by a sensor, and building a two-dimensional environment model through a grid method; S2, optimizing a target cost estimation function h (n) in anestimation function f (n) of path planning of a weighted A * algorithm for expanding a search neighborhood, adding distance information and angle information, and performing weighted measurement; S3,expanding the searchable neighborhood of the current node of the A * algorithm; and S4, according to the searchable neighborhood obtained in the step S3 and the estimation function cost value of the calculation A * algorithm defined in the step S2, sequentially selecting the node with the minimum global estimation cost value as the next node until the target point, so that the path is the shortestpath. By designing the new heuristic function and expanding the operation of searching the neighborhood, the optimized A * algorithm is more excellent in path length, better in smoothness and shorterin path finding time compared with the original algorithm, and can be suitable for the situation with many obstacles.

The invention relates to a path planning method based on an improved ant colony algorithm. According to the algorithm, an improved artificial potential field (APF) algorithm and an ant colony algorithm are combined, and the improved APF algorithm is adopted to carry out initial map planning, so that the blindness of initial planning of the ant colony algorithm is reduced. According to the algorithm, a heuristic function is improved by utilizing an evaluation function of an A * algorithm and a path turning angle, and a heuristic information incremental function is introduced, so that the convergence rate is ensured while local optimization is avoided. The state transition rule is improved, and the threshold of the state transition function is adaptively adjusted, so that the operation efficiency of the algorithm can be improved, and the diversity of solutions can be improved. The pheromone updating mechanism and the path evaluation function of the algorithm are improved, the global optimality of the algorithm is improved, and the obtained path better meets the actual requirement.

The invention discloses a changing-environment-oriented ultra-low-altitude aircraft three-dimensional multi-batch multi-track planning method. The concept of environment change is introduced, surrounding information is continuously detected in the flying process of an aircraft, a track is changed and corrected on the basis of the surrounding information, and autonomy and authenticity of track planning simulation are improved. Besides, the track of the aircraft of a current batch is planned according to opposite side deployment conditions obtained by an aircraft of a previous batch, so that the aircraft is more intelligent, and the probability of task completion is increased. Moreover, a heuristic function is introduced when an initial feasible track is searched through the ant colony algorithm, blindness of initial searching is reduced, and the searching efficiency is effectively improved.

There is disclosed a method and system for configuring a data dependency graph (DDG) to handle instruction scheduling in computer architectures permitting dynamic by-pass execution, and for performing dynamic by-pass scheduling utilizing such a configured DDG. In accordance with an embodiment of the invention, a heuristic function is used to obtain a ranking of nodes in the DDG after setting delays at all identified by-pass pairs of nodes in the DDG to 0. From among a list of identified by-pass pairs of nodes, a node that is identified as being the least important to schedule early is marked as “bonded” to its successor, and the corresponding delay for that identified node is set to 0. Node rankings are re-computed and the bonded by-pass pair of nodes are scheduled in consecutive execution cycles with a delay of 0 to increase the likelihood that a by-pass can be successfully taken during run-time execution.

The present disclosure is a system and method for improved RAID rebuilds under host IO conditions, that greatly improves rebuild times and prevents host IO starvation. A queue in a drive that is part of the RAID is used to store rebuild and host IO requests, with rebuild IOs issued to the head of the drive queue. Rebuild requests in the drive are delayed by a delay time. This delay ensures there is no unintended side effect of this invention that may result in host IO starvation for the RAID system. Delay is computed as a heuristic function from a plurality of variables, such as disk head response times, time allotted for rebuild, number of rebuild threads, drive behavior, rebuild IO profile, host IO profile including workload and locality, and a timeline of sequenced events, which in one preferred embodiment cycles every 70 seconds.

The invention discloses an autonomous underwater vehicle path planning method based on an improved ant colony algorithm. For the defects that the ant colony algorithm is long in search time, the stagnation behavior easily occurs, and the description on a complex problem is not strong enough, the following improvement is mainly performed in the process of applying the ant colony algorithm to a three-dimensional path planning process of an autonomous underwater vehicle: pheromone is released on a node but not a node side, the storage space is saved, and the computation burden is reduced; according to heuristic function design of the algorithm, a novel heuristic factor is added, depth information is used as a new heuristic function, and the convergence of the algorithm is improved; an updatepolicy of the pheromone adopts an updating way of combining global pheromone and local pheromone, the defects that the basic ant colony algorithm is long in computation time and the stagnation behavior easily occurs can be well overcome. Through the above improvement, the autonomous underwater vehicle can well respond to the complexity of the marine environment, and can better accomplish the taskunder the wide and complex marine environment.

The invention provides an unmanned ship global path planning method based on an improved A star algorithm, which relates to the field of path planning. According to the method, a traditional A star algorithm neighborhood search strategy is changed, a search neighborhood is expanded, a traditional heuristic function is improved, a deweighting strategy is combined, and an angle factor is added, so that a result obtained during path search is distributed near a connecting line of a starting point and a target point in a biased manner, and the search efficiency is improved. According to the method, the marine geographic information is acquired by utilizing an electronic chart, the improved A star algorithm is combined with a dynamic grid method, the grid map model is constructed through grid dynamic refinement, the improved A star algorithm is applied to search for the path, the path precision gradually meets the precision requirement, and redundant path nodes are further reduced through smooth path processing.