1115 results about "Planning approach" patented technology

The present invention provides a deep and reinforcement learning-based real-time online path planning method. According to the method, the high-level semantic information of an image is obtained through using a deep learning method, the path planning of the end-to-end real-time scenes of an environment can be completed through using a reinforcement learning method. In a training process, image information collected in the environment is brought into a scene analysis network as a current state, so that an analytical result can be obtained; the analytical result is inputted into a designed deep cyclic neural network; and the decision-making action of each step of an intelligent body in a specific scene can be obtained through training, so that an optimal complete path can be obtained. In an actual application process, image information collected by a camera is inputted into a trained deep and reinforcement learning network, so that the direction information of the walking of the intelligent body can be obtained. With the method of the invention, obtained image information can be utilized to the greatest extent under a premise that the robustness of the method is ensured and the method slightly depends on the environment, and real-time scene walking information path planning can be realized.

The invention discloses a parking system path planning method based on dynamic time windows, and belongs to the technical field of path planning. The method is characterized by comprising the following steps: S1, building a work environment model of AGVs in an intelligent garage in a topological method; S2, setting priority for each AGV and each car parking/picking task according to different evaluation criteria; S3, using a Dijkstra algorithm to plan a shortest possible path for an AGV accepting a task; S4, arranging feasible path time windows; S5, designing conflict resolution strategies according to different types of conflicts; and S6, planning a conflict-free optimal path for the AGV using a parking system path planning algorithm based on dynamic time windows. A time-sharing use strategy is used, and the Dijkstra algorithm and a time window method are combined effectively, so that the problem that the existing multi-AGV path planning is of poor flexibility and is prone to deadlock or collision conflict is solved effectively, and a shortest conflict-free optimal path can be planned for an AGV accepting a task. In addition, the overall operation efficiency of an intelligent three-dimensional parking system can be improved effectively, and the car parking/picking waiting time can be reduced for social members.

The invention discloses an unmanned vehicle dynamic path programming method based on environment uncertainty, comprising steps of establishing a vehicle kinetics model, establishing a dynamic environment model and necessary conditions for anew programming a path, obtaining a motion state original value of the unmanned vehicle, a vehicle motion state original target value and a vehicle motion state candidate target value, generating a candidate path, obtaining an optimal path through selection based on a safety index and a rapidity index, and reprogramming the optimal path of the unmanned vehicle when the unmanned vehicle motion environment satisfies the condition where the new path can be programmed. Compared with the prior art, the invention can not only satisfy the safety requirement for safety driving, but guarantees the driving efficiency under the constraint of the vehicle model. The invention realizes the coordination optimization of the performance index through distribution of various weights, realizes the real-time planning under the condition where a plurality of dynamic obstacles exist, and effectively improves the safety of the unmanned vehicle driving.

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 a power distribution network double layer planning method considering the time sequence and the reliability. The method comprises: according to the meteorological files and the load power statistical data, obtaining the typical daily power time sequence curves of the wind electricity, the photovoltaic output and the load in different seasons; based on the opportunistic constraint planning method, creating a power distribution network framework and a distributed power capacity double layer planning mathematical model, including the objective function and the constraint condition; using the particle swarm optimization algorithm to solve the model and using the minimum spanning tree algorithm to ensure the radiation and connectivity structure of the distribution network during the iterative process; and obtaining the target network framework and the Pareto optimal solution set of the distributed power capacity so as to generate the best planning scheme. The invention solves the problems that unnecessary investment into the a power distribution network incurred by the fact that a traditional power distribution network planning method containing a distributed power supply cannot reflect the typical output characteristic of a distribution type new energy; and 2) that by incorporating the power distribution network power supply reliability into a model target function, the reliability target can be realized at the planning stage.

The invention provides a route planning method based on a multi-target glowworm swarm algorithm and belongs to the technical field of route planning. The method includes: modeling a route planning problem, initializing the multi-target glowworm swarm algorithm, updating the glowworm position, determining a non-inferior solution set, updating an external archived file, judging whether a preset maximum iteration number is achieved or not, and determining a Pareto optimal route. The basic glowworm swarm algorithm is improved based on the Pareto dominant conception, and global searching and parallel computing capacities of the glowworm swarm algorithm are well used. Multiple route performance indexes are considered simultaneously during planning, a group of Pareto optimal solution sets can be obtained by planning once, and high flexibility is achieved. Further, the route panning method is different from traditional route planning methods for a single target and route planning methods of using a weighing method to convert multiple targets into the single target, and can better meet practical requirements of route planning.

The invention discloses a collaborative flight path intelligent planning method for formation flying of unmanned planes under a dynamic environment. The method comprises the steps of offline intelligent planning of a formation pre-flying collaborative flight path of the unmanned planes, online replanning of the flight path for avoiding threats, collaborative rebuilding of a formation team and the like. The offline intelligent planning of the formation pre-flying collaborative flight path of the unmanned planes adopts an intelligent planning method based on a Voronoi graph and an ant colony algorithm, and an integrated optimal pre-flying flight path with collaborative time can be planned off line for the unmanned plane. The replanning of the online flight path adopts an intelligent flight path planning method based on an RRT (Rail Rapid Transit) algorithm, and quick flight path correction can be provided when the unmanned plane formation meets a sudden threat. The collaborative rebuilding of the formation team adopts a method combining unmanned plane formation member flying speed adjustment and coiling maneuvering. According to the method, pre-flying collaborative flight path offline planning and generation of online replanned flight path are provided for the unmanned plane formation, a collaborative rebuilding scheme is provided, and the online rebuilding problem of unmanned plane formation flying cooperativity is solved.

The invention provides a path planning method and a device for an automatic guided vehicle, and relates to an automatic guided vehicle. The path planning method comprises the steps of receiving a source point and a target point of a conveyance task assigned by a server, planning a first conveyance path, driving the automatic guided vehicle to march forward according to the first conveyance path, and sending the task completion information when the automatic guided vehicle reaches the target point of the conveyance task. According to the technical scheme of the invention, the path planning method is applied to the automatic guided vehicle and a path planning task is assigned to the automatic guided vehicle to be executed. Therefore, the operation amount of the server is reduced and the cargo conveying efficiency is improved.

The invention discloses an accompanying robot path planning method and system based on obstacle virtual expansion. The method includes a step of constructing an environment map, namely a step of constructing a two-dimensional occupancy grid map according to an actual scene, with each grid being labeled as an obstacle zone or a walkable zone; a step of setting initial coordinate positions of an accompanying robot and a movable target in the grid map; a step of constructing a sliding window for the robot; a step of subjecting the obstacle zones to expansion processing, namely a step of performing initial expansion on grids where an obstacle is according to a shortest distance between the center of the robot and a body edge, determining the number of grid layers expanded on the basis of the minimum impassable zone, adopting the grids in the expanded zones as obstacle virtual expansion grids, and labeling the grade of danger of obstacle influences on the obstacle virtual expansion grids; and a step of planning a path for the accompanying robot based on an A* algorithm and an incremental path planning process. Incremental path updating is performed by adopting a path of the last moment,thus saving path planning time and increasing the response speed of the accompanying robot.

The invention discloses a planning method and system for optimizing a micro-grid containing distributed power sources and stored energy, wherein the planning method and system are based on an intelligent optimization algorithm. The planning method is used for optimizing the micro-grid containing the distributed power sources and the stored energy, and comprises the steps that basic data are provided for a planning and design scheme, and modeling and load flow calculation are performed on an area to be planned; a micro-grid wiring mode suitable for the area to be planned is determined; site locations, to be chosen, of the multi-type distributed power sources are determined; a site distribution and capacity determining model is established, and a grid frame of the micro-grid containing the distributed power sources and the stored energy is planned and designed; a joint planning scheme and a joint planning module under a decided investment subject are formed; the joint planning scheme is output and evaluated. According to the planning method and system for optimizing the micro-grid containing the distributed power sources and the stored energy, on the basis of basic characteristic study of the distributed power sources and the micro-grid, planning for optimizing the micro-grid containing the photovoltaic power sources is systematically studied, the positions, capacities and the grid frame structures of all kinds of distributed power sources in the micro-grid in the specified planning area are reasonably allocated, and the micro-grid optimization planning scheme which is complete, effective and high in integrity, and is closely integrated with engineering practice is established.

The invention discloses a thick plate robot welding system and a multilayer multiple-pass weld real-time tracking and planning method. The thick plate robot welding system comprises a robot, a laser structure optical sensor, a welding system and a control system. The laser structure optical sensor comprises a main system, an optical system, a clamping system and a cooling system. In the welding process, images containing weld groove feature information can be obtained in real time and transmitted to the control system; the control system obtains the groove information from the images and controls the robot to constantly correct the position of a welding gun in the welding system; meanwhile, the control system adjusts a weld pass track and welding parameters so that multilayer multiple-pass real-time planning can be achieved, and labor efficiency and production quality can be improved.

The invention relates to a multi-robot path planning method based on a time window. According to the method provided by the invention, a feasible path of a new robot is planed through a point time window and an edge time window of a node occupied by an existing robot; and when the time window of the robot is disturbed, a path of each robot is dynamically re-planned, so that the conflict of a plurality of robots in a system can be avoided. According to the multi-robot path planning method provided by the invention, a plurality of times of iteration are not needed, and the possibility is only calculated on a limited path set; and the relative calculated quantity is relatively small so that the multi-robot path planning method is more suitable for being applied to a real dispatching scene.

The present invention provides a method for planning a path for an autonomous walking humanoid robot that takes an autonomous walking step using environment map information, the method comprising: an initialization step of initializing path input information of the autonomous walking humanoid robot using origin information, destination information, and the environment map information; an input information conversion step of forming a virtual robot including information on the virtual robot obtained by considering the radius and the radius of gyration of the autonomous walking humanoid robot based on the initialized path input information; a path generation step of generating a path of the virtual robot using the virtual robot information, the origin information S, the destination information G, and the environment map information; and an output information conversion step of converting the path of the autonomous walking humanoid robot based on the virtual robot path generated in the path generation step.

The invention discloses an indoor AGV (Automated Guided Vehicles) path planning method based on an improved A* algorithm. The method comprises the following steps: firstly, constructing an environmental map by adopting a grid method, and expanding the grid map according to the size of an AGV; then, optimizing a search strategy of a traditional A* algorithm, introducing a multi-value map model, cost function evaluation and path inflexion smoothing, and adding safety factors as path evaluation indexes; and finally, adding a starting point and a target point of the AGV to the grid map, adopting an improved distance evaluation function, taking the node with the smallest cost function as a node of the next step, gradually planning a running route of the AGV, and finally displaying the running route in an electronic map. Simulated analysis verifies that the AGV path planning based on the improved A* algorithm is higher in search route safety factor, smoother in path and higher in search speed.

The invention discloses a path planning method and device, and relates to the field of intelligent transportation, and the method and device provided by the invention are used for solving the problem that a great amount of time is wasted in the driving process for users when abnormal conditions occur in the driving path determined by a navigation system in the prior art. The method provided by the invention comprises the following steps: determining the static driving path according to the initial position and the final position selected by users, wherein the static driving path comprises at least one driving path from the initial position to the final position; acquiring the transporation information of each road section in a transportation network, wherein the transportation information of the road section comprises the road condition information of the road section and/or the event information occurs for the road section; and carrying out dynamic path planning according to the transportation information of each road section in the transportation network and the static driving path, and determining the driving path. The method and device provided by the invention are suitable for the field of intelligent transportation, and are used for path planning.

The invention belongs to the field of unmanned path planning, and discloses an unmanned local path planning method based on equal-step sampling A* algorithm in order to meet the kinematic constraintsand actual traffic restrictions of automobiles. The method concretely comprises the following steps: 1, defining a search step size and a search security domain; 2, determining the starting point andthe target point of path search in a local grid map; 3, creating an Open list and a Closed list; 4, solving the cost functions of grid points in the Open list; 5, selecting the grid point with the lowest cost function value from the Open list; 6, respectively investing all security neighbor nodes of current nodes; 7, performing no sub-node processing on the current nodes in the search process; and8, repeating step 5 to step 7 until conditions are met, and returning to a feasible path or to search fail. The method is mainly applied to unmanned roads.

The invention discloses a robot cartesian space trajectory planning method. The method includes establishing a connecting rod coordinate system and obtaining a forward kinematic equation through a kinetic modeling analysis method; solving the rotating angles of a master control joint and a middle joint according to the vector geometric property and trajectory planning requirement of the robot; seeking a relation equation including introduced variables and solving the rotating angle of the corresponding joint by means of the kinetic modeling analysis method and the solved joint rotating angle; determining whether the pose position can be reached through a vector geometric method when the trajectory planning is carried out in a task space with obstacles; and planning the continuous time variant attitudes of coupled position information to complete the planning tasks. According to the method, extraneous roots can be prevented, efficient solutions can be screened and matched, strange paths can be effectively avoided, and meanwhile, the defects of complex end trajectory planned by the joint space can be prevented and optimized.

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm. The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition. Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information. The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization. A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected. The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases.

The invention belongs to the field of path planning technology and specifically relates to a three-dimensional multi-UAV coordinated path planning method based on SAS. The method comprises the following steps: carrying out modeling on a path planning environment; initializing multi-target SAS calculation parameters consisting of the length of a minimum path section, a maximum turning angle, a maximum angle of climb/glide, a minimum safe distance of UAVs and a minimum flight altitude of UAVs; initializing the positions of the UAVs, wherein each UAV represents a path; updating the positions of the UAVs; expanding a current node; determining whether a path section collides with other path sections; updating a node table of the path section; executing a step (8) if minimum path cost set in the step (2) is met, and otherwise, executing the step (3); determining a cooperated planned optimal path so as to complete path planning. The method overcomes multi-objective optimization problems, has versatility, provides a reasonable optimal solution for a decision maker and better accords with practical needs.

The invention discloses a microgrid power source planning method based on game and Nash equilibrium. Cooperative game optimal configuration models are established with identities of different participants respectively, generated output characteristics of micro sources of different kinds in a microgrid and various constraint conditions are taken into full consideration, a game is carried out between revenue functions formed by microgrid electricity sell revenue, investment cost of the microgrid, system losses cost, environment cost and interactive power cost and revenue functions formed by loss reduction revenue of large power grid loss, electricity sell revenue to the microgrid, discharged exhaust gas cost due to electricity sell to the microgrid and hot standby cost saved by a large power grid, solving is carried out by adopting a game iterative algorithm, and the power source planning problem of the game models is solved. Simulated analytical investigation is carried out by adopting a typical microgrid structure, simulation results show that cooperative the game between the microgrid and the large power grid can achieve better revenue, and therefore sound development of the microgrid can be promoted by combining power of all interested parties.

The invention relates to a planning method for a simulated path of a robot under a complex dynamic scene and a robot motion planning simulation platform for achieving the method. According to the method, path security evaluation criteria are established according to a collision possibility and an arrival possibility, an algorithm framework for planning two layers of interactive paths is adopted, motion planning is divided into an environment exploration layer and a partial path planning layer, the two layers perform information interaction by means of a self-adaptive path buffering area, and a security update search tree is taken as a top planner and applied to the environment exploration layer in the two-layer interactive framework. The simulation platform comprises a problem module, a planning module and an execution module. A safe and real-time path planning scheme is provided according to a path planning strategy selected by simulating a human path, the defect that only the feasibility of the path is considered but the continuity of the path is not concerned in the prior art is overcome, and the motion planning of the robot in a complex dynamic environment is realized.

An embodiment of the invention provides a path planning method for robot fast collision avoidance. The method comprises using a BBRrt algorithm and a collision detection algorithm based on the convex set theory, extending alternatively from initial configuration and target configuration to opposite configuration form two balance RRT trees, quickly finding, in a configuration space, a collision-free motion path connecting the initial configuration and the target configuration, effectively passing through a narrow channel and avoiding collision with obstacles in the environment, making full use of a multi-step expansion method and a balance extension method with an inspiring purpose, and thus effectively improving the convergence and repeatability of the RRT algorithm. Based on the convex set theory, a convex hull fast collision detection algorithm constituted by a linear inequation group is provided, and the efficiency and performance of a collision detection module in the path planning method for collision avoidance can be improved. The method provided by the invention can be extended to high-dimensional robot multi-tree RRT path planning.

The invention belongs to the technical field of path or flight path planning of robots as well as low-altitude flight aircrafts, specifically relates to a path planning method of a passable area divided at unequal distance, and is used for solving the problem that existing planning algorithm has large time complexity in time and space complexity. The path planning method comprises the following steps of: calculating convex extreme points of each barrier curve; dividing the passable area by using each convex extreme point as a horizontal line; abstracting each small area obtained by dividing into a peak of a graph; forming an undirected graph by all peaks; finding out a peak serial number corresponding to the small area at which a starting point and a final point are located; finding out all paths for the undirected graph by breadth-first or depth-first scanning; finding out an actual to-be-travelled path of a moving object according to the situation on an actual map. The path planning method disclosed by the invention has the beneficial effect of overcoming the problems of algorithms of A* and the like on memory space and operation time, and overcoming a convergence problem of an ant colony algorithm at the same time. Besides, time complexity and space complexity are improved greatly in comparison with other algorithms.

The invention designs an AUV (Autonomous Underwater Vehicle) three-dimensional path planning method based on reinforcement learning. The AUV three-dimensional path planning method comprises the following steps: firstly, modeling a known underwater working environment, and performing global path planning for an AUV; secondly, designing a bonus value specific to a special working environment and a planning target of the AUV, performing obstacle avoidance training on the AUV by using a Q learning method improved on the basis of a self-organizing neural network, and writing an obstacle avoidance strategy obtained by training into an internal control system of a robot; and finally receiving global path planning nodes after the robot enters into water, calculating a target heading plan by the AUV with the global path planning nodes as target nodes for planning a route, and avoiding obstacles by using the obstacle avoidance strategy in case of emergent obstacles. Through adoption of the method, the economical efficiency of the AUV routing path is ensured, and the security in case of emergent obstacles is ensured. Meanwhile, the route planning accuracy can be improved; the planning time isshortened; and the environmental adaptability of the AUV is enhanced. The method can be applied to the AUV which carriers an obstacle avoidance sonar and can implement autonomous routing.

Provided is a multi-target route planning device and method considering searching robot target node timeliness. A multi-target genetic algorithm is used to achieve route planning considering two optimization objectives including route consumption and the node timeliness through two stages of decoupling which comprise the route planning and route generating, and therefore improvement of searching performance of a mobile robot which is supervised by an operator is benefited. Particularly, when the problem of the node timeliness needs to be considered in the process of route searching, a good searching manifestation can be obtained.

The invention provides a navigational constellation slow varying inter-satellite link planning method based on earth station layout constraints. First of all, a satellite-earth link visible model and an inter-satellite visible model are constructed, then inter-satellite/satellite-earth visible time slot preprocessing is carried out, after the topology structure of a constellation is established, a router optimized constraint model and an optimization object function are determined, and a router table of a navigational constellation slow varying inter-satellite link is obtained through solving the model. According to the invention, the problem of managing a global navigational constellation through the slow varying inter-satellite link under the condition of a scarcity of ground measurement and control resources is solved, the model better meets engineering needs, the application value is quite good, and a new router can be rapidly established at a minimum cost in case that nodes are changed.