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    • 专利摘要:
    Method and system for coordinating route planning A method for coordinating route planning for one or more automated vehicles is described, which includes consulting with an online route planner regarding possible solutions for at least one executable task for each or all of the automated vehicles. more automated vehicles, examine query results, decide a coordinated route plan for each vehicle, and communicate the coordinated route plan to a traffic manager, where the traffic manager ensures that one or more automated vehicles perform each executable task according to the coordinated travel plan.
    公开号:BR112013026178A2
    申请号:R112013026178
    申请日:2012-04-10
    公开日:2019-10-01
    发明作者:Jay Thomson Jacob
    申请人:Crown Equipment Ltd；
    IPC主号:
    专利说明:

    ‘B METHOD AND SYSTEM FOR COORDINATING ROUTE PLANNING’ ’
    FUNDAMENTALS OF IN VENTION
    FIELD OF THE INVENTION
    Modalities of the present description, in general, relate to a vehicle management system and, more departed ariously. to a method and apparatus for efficient scheduling for multiple automated non-hokmomic vehicles using a coordinated route planner. RELATED TECHNOLOGY DESCRIPTION
    Automated vehicles (AVs) operate in dynamic depot environments with mixed use of multiple vehicles »The nature of this environment can cause automated vehicles to be prevented by unknown obstacles or situations as they take care of the execution of tasks. This delay causes any planning to become obsolete, since the interaction of automated vehicles can cause deadlocks, and urgent delays are at risk of completion. Factors, including general driving time, vehicle restrictions, such as non-hokomomic movement and fuel usage, also play a role in planning. These problems motivated the development and 20 the implementation of the programming solution presented using coordinated routes for multiple vehicles.
    Although research on multi-vehicle route planning is not a new topic, for example, a coordinated approach is used to restrict robots to defined road maps, resulting in a complete and relatively fast solution, a multi-vehicle approach close to ideal for non-holonomic vehicles that focus on continuous curved paths that avoid moving obstacles and have no collision are not available. Even though these solutions are useful, consideration of the problem is not broad enough to be used directly in the target industrial environment. There may be demands for resource utilization and all level of productivity. Current approaches used to solve the problem of planning and programming, particularly with multiple vehicles, have often been very limited in scope to address and attempt to optimize 5 solutions.
    Therefore, there is a need, in technology, for a method and an apparatus for efficient programming of multiple automated non-holonomic vehicles using coordinated route planning.
    SUMMARY OF THE INVENTION
    A method for coordinating route planning for one or more automated vehicles is described which includes consulting an online route planner for possible solutions to at least one executable beating for each of the one or more automated vehicles, examining the query results , decide a coordinated route plan 15 for each vehicle and communicate the coordinated route plan to a traffic manager, where the traffic manager ensures that the one or more automated vehicles perform each executable task according to the coordinated route plan .
    BRIEF DESCRIPTION OF THE DRAWINGS
    So that the way in which the exposed features of the present invention can be understood in detail, a more particular description of the invention, set out in summary, can be given by reference to the modalities, some of which are illustrated in the accompanying drawings. However, it is noticed that the attached drawings illustrate only modalities 25 typical of this invention and, therefore, they should not be considered limiting its scope, since the invention can admit other modalities equally effective.
    Figure 1 is a functional block diagram that illustrates an apparatus for efficient programming of automated vehicles using a map and implementation of a coordinated route planner according to various modalities;
    Figure 2 illustrates a multilevel graph to carry out route planning «Kírdenado of an automated vehicle according to several modalities;
    figure 3 is an exemplary graphic road map that illustrates a depot comprising automated vehicles according to various modalities;
    figure 4 is an exemplary graphic road map that 1.0 represents a programming solution for automated vehicles in a warehouse according to various modalities;
    figure 5 is an exemplary graphic road map that represents another programming solution for automated vehicles in a warehouse according to various modalities;
    figures 6A - C illustrate various levels of a multilevel graph for efficient programming of multiple automated non-holonomic vehicles using coordinated route planning according to various modalities; and figure 7 is a block diagram illustrating a system for efficient automated vehicle path planning and planning using a map and implementation of a coordinated route planner according to various modalities,
    DETAILED DESCRIPTION
    Given a set of objectives, such as moving product around a warehouse, various modalities of a method and an apparatus for the efficient programming of multiple non-holonomic automated vehicles using coordinated route planning, they find a solution that optimizes resource utilization, while while meeting current and future spanning deadlines. d.e according to some modalities, an objective can be defined for optimization, including terms for maneuver speeds, fuel usage and imminent task locations. The speed at which planning solutions are found allows for many different possibilities for current and future objectives to be evaluated, enabling 5 the best solution to be selected. Route solutions are also augmented by the use of uniformly curved paths to allow an automated vehicle to drive through routes without having to stop.
    The present description describes a device or system for planning and scheduling multiple vehicles for automated non-holonomic vehicles. This device was developed for use in automated vehicles (for example, robots, automated forklifts and / or the like) to solve planning problems. In general, non-holonomic systems (also referred to as anolonomic) include systems whose states are defined by paths that are used to reach the 15 states.
    Planning time and scalability are critical factors for functional systems. To help reduce search space and calculation time for the solution, a restriction on the total number of automated vehicles in one. multimode node is introduced. This limits search complexity 20 with little negative impact, since automated vehicles, in general, do not need to occupy the same area in the warehouse. Short planning times have allowed prognostic plans to be generated. Prognosis allows the programming component to spend more time finding an ideal solution without impacting the shooting automated 25 vehicles in motion. Prognosis also provides a level of visibility for order completion and helps to ensure that the use of automated vehicles is efficient not only for the current task, but also for testing tasks.
    Motivated by the flexible use of automated vehicles and interaction with an environment (for example, a warehouse), this description also describes coordinated route planning, while allowing automated vehicles to drive on and / or off a road map. graphic. This enables an automated vehicle to be activated in any position and drive there until the end of a route with sufficient precision to be able to interact correctly with the environment while performing tasks. Furthermore, because blocked routes can cause other segments of the route to be blocked, preventing other automated vehicles from trying to drive through that area improves resource utilization and saves a significant amount of travel time that would be wasted on other circumstances expecting the area to be cleared or determining an alternate route that avoids obstruction and the blocked route.
    Figure 1 is a functional block diagram illustrating an apparatus 100 for efficient programming of automated vehicles using a map 102 and implementing a coordinated route planner 104 according to various modalities. In addition to the coordinated route planner 104, the device 100 implements several modules (for example, code of óü / hvuFc, simónóboard, components of / wedmore and / or similar h 20 such as programmer 106, navigation module 108 and traffic manager l 10.
    In some embodiments, programmer 106 consults coordinated route planner 104 with different possible solutions for one or more available automated vehicles (AVs) that perform several 25 bumps. Programmer 106 allocates these tasks in automated vehicles more effectively by examining the results of possible solutions that are provided from coordinated path planner 104, Once a decision is made as to which solution to execute, programmer 106 communicates a plan coordinated to the traffic manager (>
    110 to manage and / or monitor execution by automated vehicles, The i 10 traffic manager ensures that automated vehicles perform the tasks (those allocated according to the coordinated plan. Each automated vehicle includes navigation module 108 to control movement of the 5 vehicle (ie. Direction) and perform location. Traffic manager 110 controls travel distance based on a current running state. As new information becomes available such as changes in map 102 or new tasks to consider, programmer 106 continues to find better solutions and to re-route automated vehicles along multiple routes.
    Finding the best solution requires programmer 106 to consult coordinated route planner 104 in relation to any and all possible solutions for each of the tasks available by different automated vehicles. Programmer 106 processes the results for each solution and searches for the solution that most closely satisfies the heuristic. A satisfactory runtime performance can be achieved by applying limits on the results and / or selecting the best solution in a given period of time. Improving the performance of the runtime prevents several problems, such as delays caused by inactivity, waste of resources and / or absence of deadlines.
    Programmer 106 predicts future solutions based on information about impending tasks according to some modalities. When planning for an automated vehicle, another automated vehicle moves to a location and blocks an area for an estimated amount of time while performing some aspect of a current task. An estimated amount of time like this is considered during route planning and scheduling. Once the estimated time elapses, the other automated vehicle can drive to a different location. As a result, task execution by the automated vehicle does not conflict with the execution of the current task by the other automated vehicle.
    Identifying and avoiding problematic situations (for example, positions that are inescapable) improves time efficiencies and long-term utilization.
    In response to a query from programmer 106, coordinated route planner 104 resumes time estimates for each possible configuration of one or more automated vehicles. Several factors can influence each time estimate. For example, allocating an automated vehicle to a task can adversely impact other automated vehicles that are also completing tasks or are inactive. Because starting automated inactive vehicles costs time and resources 10 (for example, fuel), programmer 106 uses a heuristic that reflects such costs according to some modalities. For example, coordinated route planner 104 adds terms that represent costs for starting inactive automated vehicles.
    The apparatus 100 can carry out coordinated route planning 15 continuously or periodically. In some modalities, as tasks become available over time, coordinated route planning is subsequently carried out instead of all at once due to calculation time and limited information. Optionally, whenever an event occurs, such as a new beating or a change in the map 20 102, a current schedule becomes invalidated, as there may potentially be a better solution. Programming, however, is not instantaneous, and it will be inefficient to get automated vehicles to stop steering while a new plan is being calculated. In some embodiments, programmer 106 communicates a specific time to traffic manager 110 after which automated vehicles will stop; traffic manager 110 also returns the estimated position of the automated vehicles at this time.
    In the meantime, programmer 106 performs route planning and programming from this moment on with the updated event.
    When the time expires, programmer 1 06 selects the best solution discovered so far, considering that a solution like this is at a pre-defined limit, and updates the current schedule. If the limit is not met, then further planning is necessary . If the event does not change, 5 the immediate plan. automated vehicles continue to perform tasks continuously.
    In an industrial environment (for example, a warehouse), several areas will often be unavailable for traffic due to a number of reasons, such as an automated vehicle breakdown or an obstruction (for example, an obstacle that is not included in map 102).
    The tbrma explained below with additional details, due to the size of the search space (for example, a supergraph that includes any and all automated vehicle configurations, as further explained below) prevent changes to be made or / > c .15 whenever there are changes in map 102, a list of blocked nodes is saved instead. The coordinated route planner 104 examines a list like this when performing route planning in order to stop different automated vehicles from planning the route and / or navigating through these areas. If it is known that the same 20 nodes will be blocked for an instant, then the measurements in relation to the heuristic are recalculated according to some modalities.
    Rather than using standard Zãuó / nx routes for non-holonomic automated vehicles, coordinated route planner 104 modifies Z> uO> S routes to add transition periods of constant change in curvature. A continuous change in a curved path is desired to allow the automated vehicle to drive precisely at a higher speed. In some modalities, the apparatus 100 implements, the paths of modified by the construction of graphic segments and association of paths among uniform paths. The association paths can have sharp curves at the ends and smoother curves when the association paths associate the graph, since the automated vehicle will be going faster one. time the automated vehicle reaches the graph. Due to the extra space that these paths require, the association of the association paths needs to be repeated with more pronounced path segments if the association fails in the softer ones.
    Figure 2 illustrates a very graphic 200 to carry out coordinated route planning of an automated vehicle according to various modalities. The coordinated route planner 104 considers each and every automated vehicle together as a composite unit with one or more degrees of freedom. Starting positions of automated vehicles are one configuration of this unit and target positions are another configuration. Each configuration can constitute a study in a non-holonomic system.
    From the illustrated example, the multi-level graph 200 defines a starting position 202 and an objective position 204 for the unit composed of one or more automated vehicles. A total number of possible configurations is limited by the discretization of the 200-level graph on a graphical road map, as explained below with additional details.
    The movement of one or more automated vehicles can be represented as a series of configurations. Each configuration defines positions for one or more automated vehicles, which may include one or more road mana nodes, such as a 20o road mana node. one or more nodes of. connection at a high level node, such as an aho level 208 node. One configuration can correspond to another configuration when one or more automated vehicles move between connected roadmap nodes, provided that these movements do not result in a collision.
    In some embodiments, the. coordinated path 104 places various types of nodes across the map and then associates these nodes using segment segments that form a graphic road map »The various types of nodes include, but are not limited to, road map node 206, the node high-level 208, a connection node 210 and an end connection node 212. The path segments that connect several of the nodes include, but are not limited to, 5 a path 2 14 and a path 2.16, Automated vehicles move from node to the node along the path segments until the automated vehicles reach the target position 204.
    The coordinated route planner 104, in an o / phone process, forms high-level nodes using all possible combinations or 10 configurations of automated vehicles in different nodes on the road map. These level nodes are connected by the movement of an automated vehicle along a connected path segment to reach another high-level node. The coordinated path planner 104 uses various computation techniques (for example, 15 computation techniques. supergraphic) to remove all impractical solutions. In some embodiments, the high-level nodes and associated connections form a supergralko. Therefore, the supergraph includes any and all automated vehicle configurations in the multi-level 200 graph. Crossing the supergraph at run time, programmer 106 searches for the best solution for the 20 route planning without requiring any intersection calculations, which were performed q // h7 e.
    In some modalities, the coordinated path planner 104 uses a heuristic to search for the multilevel graph 200 for the best solution (ie, path). For example, heuristics can be a time for automated vehicles to move between nodes. Estimates of travel times can be established and added up for all automated vehicles operating on a particular schedule. The coordinated route planner 104 repeats the route planning process that leads to the selection of the best solution when compared to
    II heuristic.
    In some modalities involving large areas with several automated vehicles, the coordinated route planner 104 uses a multilevel graph, such as the multilevel graph 200, a. order to 5 reduce a size of a search space. The coordinated route planner 104 groups several nodes. such as road map nodes and connection nodes, at top level nodes, as illustrated. A solution is found, first, for a top level part of the muit.ini.vel 200 chart, followed by a more specific solution for the next level below until 10 a route at the level of the complete road map is completed.
    The search space is further reduced by restricting the number of automated vehicles at high-level nodes. This restriction is> possible given the industrial environment schemes, which often can effectively allow only one or two automated vehicles in a given area. The multi-level graph 200 will result in a less ideal solution, since it considers that the best search at the allio level will contain the best search at the lower level, and this is proportional to the calculation time. Measurements for evaluation in relation to heuristics can be computed offline for the 200-level graph.
    In some modalities with high vehicle traffic, the solution found by coordinated route planner 104 will solve such problems by requiring that one or more automated vehicles wait until other vehicles pass through specific locations. Such resolutions are noted in the plan as dependencies between vehicles with the corresponding 25 locations. C) traffic manager 110 interprets these dependencies while the solution is being executed, and ensures that vehicles adhere to these dependencies when determining the distances at which vehicles are allowed to drive.
    In some modalities, automated vehicles will not always start or end at a position on route 216. This occurs when automated vehicles are manually operated and start anywhere in the known area or need to engage with items placed by human drivers who do not place items on the chart multi-level 200. To solve this problem, for each automated vehicle, a path from the 5 start position to a node and a path from a node to the target position 204 need to be calculated. As long as there is sufficient coverage of the road map, then an Atom route or similar route will suffice.
    There may be several options for nodes to join, and the closest node may not necessarily be the ideal one. An important advantage of the approach described in the present description is that the calculation speed allows the determination of almost ideal association sites. It can also be more efficient to join along an edge of the road map than in a node. To. To reduce the possibilities of association for an automated vehicle, a grid can be calculated that will contain 15 possible nodes that can be reached from each square of the grid.
    At run time, the possible nodes are recovered and a binary scan is performed along their connection path segments to determine the best location to associate with. The main segments of the route are chosen as options for the search, and the number at the end of the segment is used. 20 These graphic association paths must be chosen in such a way that they do not intersect with the target / target positions or the start / target nodes of other automated vehicles, and this will allow them to reach their starting node and leave their last node without causing an impasse. Calculating the walkers means that there will be some intersection 25 calculations at run time, but the areas are small and can be resolved quickly if map 102 is decomposed into a quadruple tree.
    Figure 3 is an exemplary graphic road map 300 that illustrates a depot comprising automated vehicles according to various modalities.
    () Graphic road map 300 represents three automated vehicles whose task is to capture items from the right side of a map and transport the captured items to a left side according to some modalities. A first automated vehicle 302 picks up an item, which must be returned to the other side of the warehouse. Subsequently, one of the other two automated vehicles must go and pick up one. next item on the right side. There are at least two solutions for programmer 106: using a second automated vehicle 304 or a third automated vehicle 306 to capture the item. All possible solutions, together with the movement of the first automated vehicle 302 to the left, are communicated to the coordinated route planner 104, in which routes with estimated times until completion are computed.
    Table 1
    ESTIMATED TIMES USING DIFFERENT VEHICLES
    AUTOMATED
    I I Estimated Times of Displacement |
    I I ÃF 31) 2 | A V 364 j AV 366 j
    ÃV © SéóSftd »for | ÀV: 3 (M | 34.13; 19.43 | 5.76 | right capture AV 306 | 36.30 i 10.11 i 44.74;
    The resulting time estimates are shown in Table .1, and the second automated vehicle 304 is favored for the task, as it is closer and is blocking the corridor. This solution is described in relation to figure 4. Since the departure of inactive automated vehicles may be undesirable, a cost is applied to this activity, in some modalities, This solution is described in relation to figure 5,
    In some embodiments, coordinated path planner 104 and programmer 106 consider instances where an automated vehicle must wait for another automated vehicle. Waiting positions and time estimates are computed for these instances and incorporated into the route planning and programming, as described in relation to figure 4 and figure 5, Routes with continuous curvature are used in figures 4 δ 5 in the graphic road map and association paths. The association paths are more pronounced at the ends, since automated vehicles are moving more slowly.
    Table Π represents estimated travel times for the first automated vehicle 302, the second automated vehicle 304 and the third automated vehicle 306 which takes into account the time spent turning an automated vehicle.
    Table II
    Estimated travel times | AV 402 - 1 .AV 304. AV 306 I | 39.70 [1 ^ 3-1 j 0.06 í
    1.0 Figure 4 is an exemplary graphic road map 400 that represents a solution for programming automated vehicles in. one. deposit, such as the deposit that is represented in Figure 3 according to various modalities. The first automated vehicle 302 starts beating at the start position SI (i.e., a left rectangular area of label on the graphic road map 400) and captures the item. The third automated vehicle 306 moves in order to complete the beating as quickly as possible while the first automated vehicle 302 uses an association path to reach a goal position Gl. With two potential waiting locations labeled W .1.
    As shown in the graphic road map 400, the start position S l is also the target position G.2 for the second automated vehicle 304. In this way, the second automated vehicle 304 moves to the target position G2 in order to capture the next item with. a W2 waiting location. In some embodiments, the first automated vehicle 252 stops and waits for the second automated vehicle 304 to move to the target position G2 and / or waits for the third automated vehicle 306 to move to the target position G3. In some embodiments, the third automated vehicle 306 is located in the starting position S3 and constitutes an obstruction to the movement of the first automated vehicle 302 and must move out of the way. In other embodiments, the second automated vehicle 304 is located at the start position S2. During the move to the target position G2, the second automated vehicle 304 waits, on the spot! W2, that the first automated vehicle 302 leaves an area around the target position G2, which is also labeled as the start position SI
    Figure 5 is an exemplary graphic road map 300 that represents another solution for programming automated vehicles in a warehouse, such as the warehouse that is represented in Figure 3 according to various modalities. In some embodiments, the other solution differs from the solution shown in Figure 4 in several aspects. For example, a coordinated route planner that is configured according to this solution assigns a higher cost for starting an automated vehicle. The first automated vehicle 302 starts the task in the start position S l and captures the item. While the first automated vehicle 302 uses an association path to reach a target position G1 with a potential waiting location labeled W1, the second automated vehicle 304 moves from the start position S2 and sc moves to the target position G2, which is also the starting position Sl. Even though the first automated vehicle 302 needs to travel slightly more in time, the third automated vehicle 306 does not have to start, which results in significant cost savings. The third automated vehicle 306 does not need to move from position S3 in order to complete the beating as quickly as possible.
    Figures 6A - C illustrate various levels of a multilevel 600 graphic for efficient programming of multiple automated non-holonomic vehicles using coordinated route planning according to various modalities. Figures 6A - C represent route planning between a starting position 602 and an objective position 604 by determining ideal local routes between several high level nodes, such as a high level node 606 and a high level node 608. Figure 6A and figure 6B can be referred to as high-level graphics and figure 6C can be referred to as a road map, base graphic. It is noticed that several top-level graphics can be used for coordinated route planning. For example, larger environments may require more than two top-level graphics and a base graphic roadmap.
    In some modalities. a coordinated route planner determines the ideal local routes between one or more connecting nodes, which are nodes located on a periphery of high-level nodes. The coordinated route planner can determine a route between connecting nodes 610, as shown in figure 6B. An ideal local route such as this one can connect one or more roadmap nodes (for example, roadmap node 206 in figure 2), which are located inside each high level node. In other ways, the coordinated route planner computes an ideal local route that does not pass through at least one road map node.
    Subsequently, a local path is determined between the start position 602 and a local connection node (for example, a start connection node). In some modalities, a route like this includes one or more nodes of the road map. The coordinated path planner .104 can compute a second local path between objective position 604 and 25 a local connection node (for example, a final connection node, such as the final connection node 212 in figure 2) of a similar way. In some embodiments, the coordinated route planner combines the local routes to form a final route 612 on the 600 level graph. As shown in figure 6C. In some modalities, the coordinated route planner
    104 selects a lower cost route that includes these local routes and high level routes to the local connection node associated with objective position 604, Ideal high level routes on the high level node 606 and the high level node 608 are then computed. These routes may not necessarily correspond to any part of the lowest cost route due to various factors, such as other vehicles operating at the same time, or almost the same (time. Once the coordinated route planner 104 determines an Ideal route at a lower level (that is, a road map level), coordinated route planner 104 takes this result 10 as the final route 612 according to one or more modalities,
    Figure 7 is a structural block diagram of a system. 700 for efficient scheduling for multiple automated non-holonomic vehicles using a coordinated route planner, such as n .104 coordinated route planner according to one or more 15 modalities. In some embodiments, the system 700 includes a computer 702 and a plurality of vehicles 704 (illustrated as a vehicle 704,, a vehicle 704 N ) in which one component is coupled to the others via a network 706, each. of the plurality of vehicles 704 includes a navigation module, such as navigation module 108. for operating various vehicle components, such as steering and / or movement components. It is understood that the plurality of vehicles 704 can use one or more computers to perform the navigation module 108.
    Computer 702 is a type of computing device (for example, a laptop computer, a desktop computer, a Personal Digital Assistant (PDA), and the like). Each of the 704 vehicles includes a type of device; computing (for example, a laptop computer, a desktop computer, a Digital Personal Assistant (PDA) and the like). A computing device, in general, comprises a central processing unit (CPU) 708. several support circuits 710 and a memory 712. Λ CPU 708 may comprise one or more microprocessors or microcontrollers available there that facilitate orocessing and Data storage. Several circuits of ..... # ............. ........... ............ ..... ........ ..... ..................................... .............. ........ support 710 facilitates the operation of CPU 708 and may include clock circuits, bar.ramem.os, power supplies, input circuits / exit and / or the like. Memory 712 includes exclusive read memory, random access memory, disk drive storage, optical storage, removable storage and the like. Memory 712 includes various data, such as map 110, as well as several AvVhruro packages, such as coordinated route planner 104, programming 106 and navigation module 108. These packages implement a device, such as a device 100 of figure I, for the efficient programming of automated vehicles 704.
    In some embodiments, coordinated path planner 104 includes software code (for example, instructions executable by processor) that is executed by the CPU in order to respond to queries from programmer 106, as described in the present description. Coordinated route planner 104 determines time estimates for any and all possible solutions for completing a task. These time estimates are used to evaluate possible solutions. In some modalities, the '106 programmer selects a solution for programming the automated vehicles 704 evaluated in relation to a heuristic. Programmer 106 communicates instructions (for example, a schedule) to traffic manager 110, which uses navigation module 108 to control automated vehicle operations and movements.
    The 706 network comprises a communication system that connects computers by wires, cables, fiber optics and / or wireless connections facilitated by various types of well-known network elements, such as hubs, switches, routers and the like. The 706 network can employ several well-known protocols to communicate information between the network resources. For example, the 706 network can be part of the Internet or intranet, using various communications infrastructure, such as Ethernet, D7FA ΦΪΑ / αΐ ', Gera Packet Radio Service! (GPRS) and the like,
    Although the foregoing is directed to the modalities of this invention, other additional modalities and modalities of the invention can be conceived without departing from its basic scope, and its scope is determined by the following claims.
    权利要求:
    Claims (15)
    [1]
    1. Method for coordinating the route planning for one or more automated vehicles, characterized by the feel that it comprises:
    provide a multi-level graph comprising high-level nodes corresponding to a region, where each of the high-level nodes comprises one or more connection nodes corresponding to an issue in the region, one or more local nodes and corresponding (s) to one interior of the region, and one or more local routes that link the connection nodes, the local nodes, or a combination thereof;
    [2]
    2. Method according to claim 1, characterized by the fact that it comprises removing at least a portion of the graphic road map from the set of graphic road map solution based on at least part of the heuristic of the portion of the road map graphic.
    [3]
    Method according to claim 1, çaraçterizad <}. by the tact that it additionally comprises restricting a number of the automated vehicles allowed within each of the high-level ones.
    [4]
    4. Method according to claim 3, characterized by the fact that the number of automated vehicles allowed within each of the high level nodes is two or less.
    [5]
    5 target position of each of the graphic road maps,
    20. System according to claim 1.8. characterized> .. ........................................ by the fact that the plan coordinated route requires one of the vehicles to, wait until another of the automated vehicles passes a specific location.
    5 select a current area of one of the automated vehicles. from a plurality of areas, in which the current area is indicative of a location of one of the automated vehicles: and determine, onlme, and automatically with one or more central processing units) a joining path from the 10 location to a member of the portion of the local routes, the connection nodes, and the local nodes corresponding to the current area
    5. Method according to claim 1. characterized by the fact that it additionally comprises:
    stop the automation of automated vehicles at a predetermined time; and resuming operation of the automated vehicles after the time period elapses after the predetermined time, in which the coordinated route plan is selected during the time period.
    [6]
    6. Method according to claim 1, characterized by the fact that it further comprises:
    generate a list of blocked nodes corresponding to the high-level nodes, the connection nodes, and the local nodes that are available; and stop the automated vehicles from navigating from a stop in the region corresponding to the blocked ones.
    [7]
    7. Method according to claim .1, characterized by the fact that it additionally comprises forming a modified Dubins path comprising joining paths at the modified Mo Dubins ends and a change in curvature path located between the joining paths, in which the modified Dubins path comprises sharper turns than continuous change in curved path, and that one or more local (s) of a graphic road map comprises modified Dubins path.
    [8]
    8. Method according to claim L, characterized by the fact that it comprises:
    build, off / ine, a grid associated with the multi-level graph, in which the grid marks a plurality of areas that each correspond to a portion of the local paths, the connection nodes, and the local nodes;
    [9]
    Method according to claim 8, characterized by the fact that the joining path does not intersect with the initial position and the target position of each graphic road map.
    10. Method according to claim 1, characterized by the fact that the coordinated path piano requires one of the automated vehicles to wait until another one of one or more vehicles pass a specific location.
    [10]
    10 21. Method for coordinating route planning for a plurality of automated vehicles, in which the automated vehicles are located within a warehouse and in communication with one or more signal processing unit (s), characterized by the fact that it comprises :
    10 build, q / E / ne, a set of graphical road map solution from multilevel graph, in which each of the graphical road maps comprises an initial position within one of the high level nodes connected via a final path to a target position for another of the high level nodes, and that the final path comprises a portion of the local paths of the high level nodes;
    associate a heuristic with each of the multilevel graphs, and that the heuristic is indicative of the final route of your graphic road map;
    select, orZme, and automatically with one or more 20 signal processing unit (s), a coordinated route plan for automated vehicles from the graphical road map solution set, in which the coordinated route plan is selected with based on at least part of the heuristic; and operate automated vehicles according to the coordinated route plan.
    [11]
    11. Method according to claim 1, characterized by the fact that the heuristic is indicative of travel time.
    [12]
    12. Method according to claim 1, characterized by the fact that the heuristic is indicative of the cost associated with starting an inactive vehicle from the automated vehicles.
    [13]
    13. Method according to claim 1, characterized
    25 due to the fact that the heuristic is indicative of the high-level nodes, the connection nodes, and the local nodes that are unavailable.
    [14]
    14. Method according to claim 1. characterized> .............................
    due to the fact that automated vehicles are non-holonomic.
    15. System to coordinate the route planning in storage, çaraçterizadti by the fact of understanding:
    a plurality of automated vehicles located within the warehouse, each vehicle comprising a navigation module in communication with a steering component or a component of movement: and one or more signal processing type (s) in communication with each automated vehicles, where one or more signal processing unit (s) executes instructions for:
    access a multi-level graph comprising 10 high-level nodes, each corresponding to a warehouse region, where each of the high-level nodes comprises one or more connection nodes corresponding to a warehouse region boundary, one or more local nodes corresponding to an interior of the deposit region, and one or more local routes that connect (ru) to the connection nodes, the local nodes, or a combination thereof;
    build, q ^ h'ne, a set of graphical road map solution from a multilevel graph, in which each graphical road map comprises an initial position within one of the high level nodes, and the final path comprises a portion the local routes of the 20 high-level nodes;
    associate a heuristic with each of the graphic road maps, where the heuristic is indicative of the final route of its associated graphic road map;
    select, on / ine, a coordinated route plan for the 2.5 automated vehicles from the graphical road map solution set, where the coordinated route plan is selected based on at least part of the heuristic: and communicate at least a part of the coordinated route plan for each of the automated vehicles, in which the navigation module of each of the automated vehicles to control the steering component, the movement component, or both according to the coordinated route plan.
    16. System according to claim 15, characterized by the fact that one or more central processing humidity (s) executes the instructions for:
    generate a list of blocked nodes corresponding to the high level nodes, the connection nodes, and the local nodes that are unavailable: and interrupt the automated vehicles from the navigation of a part of the warehouse region corresponding to the blocked nodes.
    17. System according to claim 15, characterized by the fact that one or more processing unit (s) carries out the instructions to form a modified Dubins path comprising joining paths at modified Dubins ends and a continuous change in a curvature path located between the joining paths, where the modified Dubins path comprises smoother turns than the curvature change in the curvature path., and one or more local path (s) of one of the maps graphic roadways comprise the modified Dubins route.
    18. System according to claim 1.5, characterized by the fact that one more central processing unit (s) executes the instructions for:
    build, qí / z / ae, a grid associated with the multilevel graph. in which the grid marks a plurality of areas of the deposit that each corresponds to a portion of the local routes, and the local nodes;
    selecting a current area of one of the automated vehicles from a plurality of areas, where the current area is indicative of a location within the warehouse of one of the automated vehicles; and determining, a joining path from the location to a member of the portion of the local paths, the connecting nodes, and the local nodes corresponding to the current area.
    19. System according to claim 13, characterized by the fact that the joining path does not intersect with the initial position and the
    [15]
    15 provide a multi-level graph comprising high-level nodes corresponding to a depot region, where each of the high-level nodes comprises one or more connection node (s) corresponding to a depot boundary, one or more connection nodes (s) corresponding to an interior of the deposit region, and one or more local connections that connect to the connection nodes, local nodes, or a combination of themselves;
    build, a solution set of graphical multi-level graphical road maps, in which each of the graphical road maps comprises an initial position within one of the high-level nodes connected via a final path to a target position of another of the nodes 25 high level, and that the final route comprises a portion of the local routes of the high level nodes, and that the final route corresponds to a road within the deposit;
    associate a heuristic with each of the graphic road maps, where the heuristic is indicative of the final route of its associated graphic road map:
    select, on / me, and automatically with one or more central processing unit (s), a coordinated route plan for automated vehicles from the set of graphic road maps solution, in which the coordinated and selected route plan with based at least in part on heuristics; and operate automated vehicles within the warehouse according to the coordinated route plan.
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    同族专利:
    公开号 | 公开日
    EP2697701A4|2014-10-01|
    RU2589869C2|2016-07-10|
    KR20140025448A|2014-03-04|
    US20160033971A1|2016-02-04|
    KR102041093B1|2019-11-06|
    US9188982B2|2015-11-17|
    AU2012243484A1|2013-05-02|
    CA2831832C|2021-06-15|
    EP3435189A1|2019-01-30|
    US20140032035A1|2014-01-30|
    EP3435189B1|2022-02-09|
    EP2697701B1|2018-10-24|
    CN107272678A|2017-10-20|
    EP2697701A2|2014-02-19|
    CN103608740A|2014-02-26|
    WO2012141601A3|2013-02-28|
    CN103608740B|2017-06-30|
    CN107272678B|2020-11-06|
    WO2012141601A2|2012-10-18|
    AU2012243484B2|2014-10-30|
    US9958873B2|2018-05-01|
    CA2831832A1|2012-10-18|
    RU2013150133A|2015-05-20|
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    法律状态:
    2019-12-10| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-12-10| B25A| Requested transfer of rights approved|Owner name: CROWN EQUIPMENT CORPORATION (US) |
    2020-03-03| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-09-01| B11B| Dismissal acc. art. 36, par 1 of ipl - no reply within 90 days to fullfil the necessary requirements|
    2021-10-05| B350| Update of information on the portal [chapter 15.35 patent gazette]|
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    PCT/NZ2012/000051|WO2012141601A2|2011-04-11|2012-04-10|Method and apparatus for efficient scheduling for multiple automated non-holonomic vehicles using a coordinated path planner|
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