CN113899383B - Multi-vehicle deadlock prevention method, system, equipment and storage medium based on short path - Google Patents

Multi-vehicle deadlock prevention method, system, equipment and storage medium based on short path Download PDF

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Publication number
CN113899383B
CN113899383B CN202111385111.5A CN202111385111A CN113899383B CN 113899383 B CN113899383 B CN 113899383B CN 202111385111 A CN202111385111 A CN 202111385111A CN 113899383 B CN113899383 B CN 113899383B
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path
waiting
vehicles
vehicle
time
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CN113899383A (en
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谭黎敏
俞铭琪
陈义东
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Shanghai Xijing Technology Co ltd
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Shanghai Xijing Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention provides a multi-vehicle deadlock prevention method, a system, equipment and a storage medium based on a short path, wherein the method comprises the following steps: performing time expansion-based path planning on each vehicle; dividing the planned path of each vehicle into a plurality of short path areas based on a preset length; applying for at least one short path area in front of the running direction based on the passing time of path planning during running of the vehicle, and generating waiting link information when an overlapping area exists; tracing the current head vehicles waiting for the link information, and supplementing the head vehicles to the waiting link information; if the head-of-line vehicle can obtain a short path area which is not overlapped with the path planning of all other vehicles in the waiting link information, applying for the head-of-line vehicle and driving into the short path area; and after the tail vehicles of the fleet pass through the overlapping area, releasing the head vehicles in the waiting link information. The method can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevents deadlock, realizes collision prevention and deadlock avoidance of vehicles, and improves the overall transportation efficiency of a fleet.

Description

Multi-vehicle deadlock prevention method, system, equipment and storage medium based on short path
Technical Field
The present invention relates to the field of path planning technologies, and in particular, to a method, system, device, and storage medium for preventing deadlock in multiple vehicles based on a short path.
Background
The development of shortest path algorithms has been a leading edge problem in engineering and graph theory. Because of the wide applicability, the independent design of path planning algorithm variants for each industry is also endless. On the premise of building an environment electronic map, for a given starting point and an end point, selecting an optimal path from the starting point to the end point. Since there may be many connection modes from the start node to the end node, the path length of each connection mode is different, so that the path planning problem is converted into the shortest path length from the start point to the end point under the environment of the established electronic map without considering the interference between vehicles, the interference of the environment and the line blockage.
In order to improve the efficiency of solving the most effective path, students at home and abroad make outstanding contributions. The research direction of domestic scholars is mainly the path planning of many cars and the obstacle avoidance problem of car in the advancing process. The main theories studied include: the shenyang automated institute Liu Guodong studied a two-stage dynamic path planning algorithm for AGV that dynamically selects and adjusts at run-time by generating a set of paths off-line. Foreign path planning research is always a hotspot and difficult problem in the field of AGV research, and a large number of algorithms are used for path planning of multiple vehicles. For example: the genetic algorithm is applied to path planning, and a genetic iteration method is adopted by classifying idle vehicles. And deadlock avoidance of multi-vehicle path planning place higher demands on the algorithm. Researchers have quickly found that the complexity of the algorithm increases significantly as the number of vehicles increases to multiple vehicles.
And, in a scenario where a large number of unmanned vehicles work together, for example: the unmanned code heads and the like simultaneously use a large number of unmanned collection cards, and if each unmanned collection card is at the discretion of planning a line by itself, the situation that a large number of vehicles are blocked and the whole area is blocked easily occurs at some special intersections.
In view of the above, the invention provides a multi-vehicle deadlock prevention method, a system, equipment and a storage medium based on short paths.
It should be noted that the information disclosed in the foregoing background section is only for enhancement of understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a multi-vehicle anti-deadlock method, a system, equipment and a storage medium based on short paths, which overcome the difficulties in the prior art, can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevent deadlock, realize vehicle collision prevention and deadlock avoidance, and improve the overall transportation efficiency of a motorcade.
The embodiment of the invention provides a multi-vehicle deadlock prevention method based on a short path, which comprises the following steps of:
S110, planning a path based on time expansion for each vehicle;
s120, dividing the planned path of each vehicle into a plurality of short path areas based on a preset length;
S130, applying for at least one short path area in front of a driving direction based on the passing time of path planning during driving of the vehicle, stopping for waiting if the short path area is applied for by another vehicle, and generating waiting link information based on a waiting relation; s140, tracing whether the waiting vehicles in the current waiting link information also have the waiting vehicles, if so, supplementing the waiting relationship to the waiting link information, and returning to the step S140; if not, executing step S150;
S150, planning a short-path area which is not overlapped with the path planning of other vehicles for the head-of-line vehicles positioned at the queue source in the waiting link information; and
S160, when the tail vehicles in the waiting link information pass through the first overlapping area, the head vehicles in the waiting link information are released.
Preferably, in the step S120, the method further includes:
Traversing the short-path areas based on the electronic map, and merging the short-path areas when two adjacent short-path areas belong to the same road node, wherein the road node is one of a curve or an intersection.
Preferably, in the step S130, it includes: and applying for at least one short path area in front of the driving direction based on the passing time of path planning during driving of the vehicle, and when the short path area is applied for by another vehicle at the time, enabling paths of the two vehicles to have a first overlapping area, stopping the vehicles for waiting, generating waiting link information based on a waiting relation, and waiting the vehicles to serve as tail vehicles in the waiting link information.
Preferably, in the step S130, the method further includes:
the waiting link information includes a plurality of pairs of vehicle data, each pair of vehicle data including a waiting vehicle and a waiting vehicle, and the waiting vehicle of the pair of vehicle data is identical to the waiting vehicle of the adjacent pair of vehicle data arranged in front thereof.
Preferably, each vehicle data pair in the waiting link information further includes position information of a short path region where an overlapping region where the waiting vehicle and the waiting vehicle overlap.
Preferably, in step S140, the method further includes:
And generating a new vehicle data pair by using the head-of-queue vehicle in the current waiting link information and the waiting vehicle of the head-of-queue vehicle, and adding the new vehicle data pair to the head of the waiting link information.
Preferably, the step S150 includes: if a short path area which is not overlapped with the path planning of all other vehicles in the waiting link information exists in other short path areas which are connected with the queue head vehicles at the queue source based on the current position and are based on the passing time, applying for the queue head vehicles and driving into the short path area
Preferably, the step S150 includes:
If the total number of vehicles in the waiting link information exceeds a preset threshold, and a short-path area which is not overlapped with the path planning of all other vehicles in the waiting link information exists in other short-path areas which are connected with the head-of-team vehicles at the queue source based on the current position in the waiting link information based on the passing time, the head-of-team vehicles apply for and drive into the short-path area, and the value range of the preset threshold is larger than 2.
Preferably, the step S110 includes:
S111, acquiring initial parameters of path planning and a two-dimensional space map, wherein the initial parameters comprise space coordinates of a starting place, starting time and space coordinates of a destination, the two-dimensional space map comprises a plurality of space nodes, and each space node has a two-dimensional space coordinate;
S112, expanding a two-dimensional space map to a three-dimensional space-time map, wherein a first dimension and a second dimension are two dimensions of the two-dimensional space map respectively, a third dimension is time, and a time range of the three-dimensional space-time map is started by the starting time;
S113, determining a starting place space-time node in the three-dimensional space-time map according to the space coordinates of the starting place and the starting time, and determining a destination straight line in the three-dimensional space-time map according to the space coordinates of the destination and the time range of the three-dimensional space-time map;
S114, searching the next path node in the three-dimensional space-time map from the starting space-time node based on an A Star path planning algorithm so as to obtain the shortest path from the starting place to the destination.
Preferably, when searching for a next path node in the three-dimensional space-time map, a space-time constraint condition is set for selection of the next path node, and the space-time constraint condition includes: the next spatiotemporal node available for searching includes a spatiotemporal node determined from the spatial coordinates of the current spatiotemporal node and the next time and a node determined from the spatial coordinates of spatially contiguous nodes of the current spatiotemporal node and the next time.
Preferably, after the expanding the two-dimensional space map to the three-dimensional space-time map, the method further comprises the following steps:
Calculating an arrival difficulty index of each space node in the three-dimensional space-time map;
and when searching the next path node in the three-dimensional space-time map, calculating the adopted heuristic function according to the arrival difficulty index of each space node in the three-dimensional space-time map.
Preferably, the calculating the arrival difficulty index of each spatial node includes the following steps:
in the two-dimensional space map, the arrival difficulty index from each space node to the adjacent node is calculated in sequence;
for each spatial node, calculating the average value of the arrival difficulty indexes of the spatial node to each adjacent node as the arrival difficulty index of the spatial node.
Preferably, the calculating the arrival difficulty index from each spatial node to its adjacent node includes calculating a manhattan distance or travel time from each spatial node to its adjacent node as the corresponding arrival difficulty index.
Preferably, the step of sequentially calculating the arrival difficulty index from each spatial node to its adjacent node includes the following steps:
finding the two furthest space nodes in the two-dimensional space map;
And sequentially searching the space nodes which are the next farthest from the two space nodes which are the farthest by using a depth-first traversal method, and iteratively generating a four-dimensional matrix, wherein a first dimension and a second dimension in the four-dimensional matrix are respectively two dimensions of the two-dimensional space map, the third dimension corresponds to time, and the fourth dimension corresponds to an arrival difficulty index from each space node to an adjacent node.
Preferably, finding the two furthest spatial nodes in the two-dimensional spatial map comprises finding the two spatial nodes in the two-dimensional spatial map that have the longest manhattan distance or the longest travel time.
Preferably, the method is applied to multi-vehicle path planning, and when searching for the next path node in the three-dimensional space-time map, the currently planned vehicle and the planned vehicle cannot pass through the same space node at the same time.
Preferably, the method is applied to multi-vehicle path planning, and when searching for the next path node in the three-dimensional space-time map, a closed list is established by adopting the following steps:
Acquiring data of a planned vehicle path according to a preset vehicle path planning sequence;
Determining the transit time of the planned vehicle path at each space node;
Determining locking nodes according to the passing time of the planned vehicle path in each space node and the space coordinates of the corresponding space node;
And adding the locking node into a closed list.
Preferably, the closed list further includes a preset default locking node, and the time corresponding to the default locking node is the time range of the three-dimensional space-time map.
Preferably, after the initial parameters of the path planning are obtained, the method further comprises the following steps:
Calculating a travel time of the origin to the destination;
A time range of the three-dimensional space-time map is defined based on the start time and the travel time.
The embodiment of the invention also provides a multi-vehicle anti-deadlock system based on the short path, which is used for realizing the multi-vehicle anti-deadlock method based on the short path, and comprises the following steps:
and the path planning module is used for planning a path based on time expansion for each vehicle.
And the path segmentation module is used for segmenting the planned path of each vehicle into a plurality of short path areas based on the preset length.
And the waiting link module is used for applying at least one short-path area in front of the driving direction based on the passing time of the path planning when the vehicle is in driving, stopping and waiting if the short-path area is applied by another vehicle, and generating waiting link information based on a waiting relation.
And the queue head tracing module traces back whether the waiting vehicles at the queue head in the current waiting link information also have the waiting vehicles, if so, the waiting relationship is supplemented to the waiting link information, and the step S140 is returned. If not, step S150 is performed.
And the path adjustment module is used for planning a short path area which is not overlapped with the path planning of other vehicles for the head-of-queue vehicles positioned at the queue source in the waiting link information. And
And the queue head releasing module releases the queue head vehicles in the waiting link information after the queue tail vehicles in the waiting link information pass through the first overlapping area.
The embodiment of the invention also provides a multi-vehicle deadlock prevention device based on the short path, which comprises:
A processor;
a memory having stored therein executable instructions of a processor;
Wherein the processor is configured to perform the steps of the short path based multi-vehicle deadlock prevention method described above via execution of executable instructions.
The embodiment of the invention also provides a computer readable storage medium for storing a program, which is used for realizing the steps of the multi-vehicle deadlock prevention method based on the short path when being executed.
The multi-vehicle anti-deadlock method, system, equipment and storage medium based on the short path can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevent deadlock, realize vehicle anti-collision and deadlock avoidance, and improve the overall transportation efficiency of a motorcade.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings.
Fig. 1 is a flow chart of the short path-based multi-vehicle deadlock prevention method of the present invention.
Fig. 2 is a flow chart of the short path based multi-vehicle deadlock prevention method of the present invention.
FIG. 3 is a schematic diagram of the use of a three-dimensional space-time map in the short-path-based multi-vehicle deadlock prevention method of the present invention.
Fig. 4 is a schematic structural diagram of the short-path-based multi-vehicle deadlock prevention system of the present invention.
Fig. 5 is a schematic structural diagram of the short-path-based multi-vehicle deadlock prevention device of the present invention. And
Fig. 6 is a schematic structural view of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus a repetitive description thereof will be omitted.
Fig. 1 is a flow chart of the short path-based multi-vehicle deadlock prevention method of the present invention. As shown in fig. 1, an embodiment of the present invention provides a multi-vehicle deadlock prevention method based on a short path, including the following steps:
S110, planning a path based on time expansion for each vehicle;
S120, dividing the planned path of each vehicle into a plurality of short path areas based on a preset length. In this embodiment, based on the traversing of the short-path regions by the electronic map, when two adjacent short-path regions belong to the same road node, the road node is one of a curve or an intersection, and the short-path regions are merged.
S130, applying for at least one short path area in front of the driving direction based on the passing time of path planning during driving of the vehicle, when the short path area is applied for by another vehicle at the time, enabling paths of the two vehicles to have a first overlapping area, stopping the vehicles for waiting, generating waiting link information based on a waiting relation, and waiting the vehicles to serve as tail vehicles in the waiting link information. The waiting link information includes a plurality of pairs of vehicle data, each pair of vehicle data including a waiting vehicle and a waiting vehicle, and the waiting vehicle of the pair of vehicle data is identical to the waiting vehicle of the pair of neighboring vehicle data arranged in front thereof. Each of the pair of vehicle data in the waiting link information further includes position information of a short path region where an overlap region where the waiting vehicle and the vehicle to be waiting overlap.
S140, tracing whether the waiting vehicles in the current waiting link information also have the waiting vehicles, if so, supplementing the waiting relationship to the waiting link information (for example, generating a new vehicle data pair by the head vehicle and the waiting vehicles of the head vehicle in the current waiting link information and adding the new vehicle data pair to the head of the waiting link information), and returning to the step S140; if not, step S150 is performed. For example: when the vehicle C1 waits for the vehicle C2 to pass through the overlap area D1, the first waiting link information is ([ vehicle C1, vehicle C2, overlap area D1 ]), where the vehicle C1 is at the end of the queue and the vehicle C2 is at the head of the queue. Subsequently, by tracing, if the vehicle C2 is found that the waiting vehicle C3 passes through the overlapping area D2, the supplementary waiting link information includes: ([ vehicle C1, vehicle C2, overlap region D1], [ vehicle C2, vehicle C3, overlap region D2 ]), wherein vehicle C1 is at the end of the fleet and vehicle C3 is at the head of the fleet. Continuing to trace, if the vehicle C3 does not have other vehicles needing to wait, the waiting link information is complete, and the tracing is stopped.
And S150, if a short path area which is not overlapped with the path planning of all other vehicles in the waiting link information exists in other short path areas which are connected based on the current position of the queue head vehicles in the waiting link information based on the passing time, applying for the queue head vehicles and driving into the short path area. In this embodiment, if the total number of vehicles in the current waiting link information exceeds a preset threshold, and there is a short path area which is not overlapped with the path plans of all other vehicles in the waiting link information in other short path areas where the head-of-queue vehicles at the queue source are connected based on the current position in the waiting link information based on the passing time, the head-of-queue vehicles are applied and driven into the short path area, the value range of the preset threshold is greater than 2, in this embodiment, the preset threshold is 4, that is, when the total number of vehicles in the waiting link information exceeds 4 vehicles, the head-of-queue vehicles are considered to be close to be locked, and an emergency starting and locking releasing step is required. In this embodiment, the first vehicle may temporarily set a path for avoiding the other vehicles in the waiting link information, and temporarily stop the vehicle in a short path area that does not interfere with the other vehicles in the waiting link information. And changing the emergency avoidance parking of the first vehicle in the waiting link information to wait for the policy running of the subsequent other vehicles in the link information.
In a modification, a new route planning may be performed for the head-of-line vehicle, and a route that is based on that the transit time does not overlap with the route planning of all other vehicles in the waiting link information may be found, and if available, the head-of-line vehicle may be caused to travel based on the new route planning; and if the short path area which is not overlapped with the path planning of all other vehicles in the waiting link information exists in other short path areas which are connected based on the current position and are not available based on the passing time, applying for the first vehicle of the team and driving into the short path area. This reduces the time loss for waiting for all vehicles in the link information and maximizes the transport efficiency.
S160, after the tail vehicles of the waiting queue pass through the first overlapping area, the head vehicles in the waiting link information are released.
In the invention, when the locking condition possibly occurs, the locking of other vehicles in the whole waiting link information is avoided by timely adjusting the head vehicles in the waiting link information, and the transportation of the head vehicles is influenced, but compared with the locking of a large number of vehicles, the whole vehicle is greatly prevented from being lost in the operation process of a vehicle team or an unmanned wharf.
In a preferred embodiment, FIG. 2 is a flow chart of the short path based multi-truck deadlock prevention method of the present invention. As shown in fig. 2, S110 in the present embodiment, performing path planning based on time expansion for each vehicle includes the following steps:
S111, acquiring initial parameters of path planning and a two-dimensional space map, wherein the initial parameters comprise space coordinates of a starting place, starting time and space coordinates of a destination, the two-dimensional space map comprises a plurality of space nodes, and each space node has a two-dimensional space coordinate;
S112, expanding the two-dimensional space map to a three-dimensional space-time map, wherein the first dimension and the second dimension are two dimensions of the two-dimensional space map respectively, the third dimension is time, and the time range of the three-dimensional space-time map takes the starting time as a starting point;
S113, determining a starting place space-time node in the three-dimensional space-time map according to the space coordinates of the starting place and the starting time, and determining a destination straight line in the three-dimensional space-time map according to the space coordinates of the destination and the time range of the three-dimensional space-time map;
S114, based on an A Star path planning algorithm, sequentially searching the next path nodes from the starting space-time node to the destination in the three-dimensional space-time map so as to obtain the shortest path from the starting place to the destination.
In the multi-vehicle deadlock prevention method based on the short path of this embodiment, the serial number of each step is only used to distinguish the steps, and is not limited by the specific execution sequence of the steps, and the execution sequence between the steps can be adjusted and changed according to the needs. For example, it is possible to first obtain a two-dimensional space map as a target search, and perform three-dimensional expansion, and then obtain parameters such as space coordinates of a start location and a destination, which are all within the scope of the present invention.
According to the method for preventing deadlock of multiple vehicles based on the short path, after the two-dimensional space map of the initial parameters and the targets is obtained through the step S111, the time dimension of the two-dimensional space map is increased through the step S112, the method can be expanded to a three-dimensional space-time map, expansion of search space is achieved, and the conditions of collision or deadlock and the like during planning of multiple vehicle paths can be avoided. According to the invention, by adding the time dimension, a single time point constraint is released to one straight line of the three-dimensional space in the step S113, when the route planning searches any point on the straight line where the destination is located, the jump-out condition is met, and the method can be well applied to multi-vehicle route planning.
In step S114, if a path is planned for a vehicle, a path search is performed in the three-dimensional space-time map based on the a Star path planning algorithm, so as to obtain each path node of the shortest path. If the route planning is performed for a plurality of vehicles, the route searching is performed in the three-dimensional space-time map for each vehicle in sequence according to the preset vehicle route planning sequence.
FIG. 3 is a schematic diagram of the use of a three-dimensional space-time map in the short-path-based multi-vehicle deadlock prevention method of the present invention. As shown in fig. 2 and 3, the present invention expands the two-dimensional space map (x, y) to three dimensions (x, y, t) on the basis of the conventional two-dimensional path search by steps S112 and S113, increasing the time t to the third dimension. Although the prior art has also proposed using time as an extension of the search space, the prior art searches on the basis of a determined point in time result, and in the case of multiple vehicles, it is difficult to find a determined point in time. In the invention, the time constraint of a single destination is released to a straight line (for example, a straight line extending in the t dimension corresponding to the node 7 shown in fig. 3) in the three-dimensional space through the step S113, so that when the bicycle path planning searches any point of the straight line, the jump-out condition is met, and the optimization based on time expansion is further optimized.
In the present invention, spatial nodes refer to two-dimensional spatial nodes on a two-dimensional spatial map of an original target, each two-dimensional spatial node having one two-dimensional (x, y) spatial coordinate (x, y). The invention expands the two-dimensional space map into the three-dimensional space-time map, adds the dimension t, and the space-time coordinates (x, y, t) of each space-time node in the three-dimensional space-time map comprise the space coordinates (x, y) and the corresponding time t.
When the next path node is searched in the three-dimensional space-time map in sequence from the starting point space-time node, not only the two-dimensional space coordinates of the path node but also the time dimension corresponding to the path node are needed to be considered. Therefore, in step S114, when searching for the next path node in the three-dimensional space-time map, a space search is required, and in the present invention, since time is added as the third dimension, only overlapping in time is allowed and falling in time is not allowed in the process of searching, and therefore, a space-time constraint condition needs to be set for selection of the next path node, which is based on a normal theory: the path forward cannot be performed while the time is reversed, nor is the waiting stopped.
In this embodiment, the space-time constraints include: the next spatiotemporal node available for searching includes (a) a spatiotemporal node determined from the spatial coordinates of the current spatiotemporal node and the next time and (b) a node determined from the spatial coordinates of spatially contiguous nodes of the current spatiotemporal node and the next time. Wherein (a) represents a spatiotemporal node that may stay in place while advancing in time; (b) Representing a space-time node that is optional in time advancing while the path advances to the next space node.
In particular, the space-time constraint can be expressed as:
N{Adj}t+1＝Nt+1+N{Adj}t
Wherein N represents space-time nodes used for searching, t represents the current time, and Adj represents a neighboring point set. The next spatiotemporal node N { Adj } t+1 available for searching includes (a) spatiotemporal node N t+1 determined from the spatial coordinates of current spatiotemporal node N t and the next time t+1 and (b) node N { Adj } t determined from the spatial coordinates of spatially contiguous nodes of current spatiotemporal node N t and the next time. The above formula indicates that the next node of the current node can only search in the neighbor set at time t+1.
In step S114, a path search is performed in the three-dimensional space-time map based on the a Star path planning algorithm. The A Star path planning algorithm will be described first.
The a Star algorithm, the a-algorithm, is a best-prioritised search, formulated from a weighted graph. From the start point node of the target map (when a two-dimensional space map is used, the start point node is the start space node, when a three-dimensional space-time map is used in the present invention, the start point node is the start point space-time node), the next path node is searched in turn until the destination node is reached (when a two-dimensional space map is used, the destination node is the destination space node, and when a three-dimensional space-time map is used in the present invention, the destination node is the straight line corresponding to the destination).
The a Star algorithm aims to find the path cost (minimum distance travelled, shortest time, etc.) for a given destination node with a minimum value. It is implemented by maintaining a tree of paths from the origin node and extending those paths one at a time until its termination criteria are met. In each iteration of its main loop, a Star needs to determine which paths to extend. It estimates the total path cost based on the cost of the current path and the cost required to extend the path to the target, to determine which path is the next node to find. Specifically, the path cost of the a Star algorithm can be expressed as the following formula:
f(n)＝g(n)+h(n)
Where n represents the next node on the path, g (n) represents the path cost from the origin node to node n, and h (n) is a heuristic function for estimating the cost of the shortest path from node n to the destination node. The A Star search terminates when it selects the path of the extension to be the path from the origin to the destination or no path can be extended. Heuristic functions are problem-specific. If the heuristic is acceptable, meaning that it never overestimates the actual cost of achieving the objective, the A Star algorithm guarantees that the lowest cost path from the origin to the destination is returned. AStar uses the priority queues to perform the repeated selection of the minimum (estimated) cost node for expansion. In each iteration of the algorithm, the node with the lowest f (n) value is removed from the queue, the f and g values of its neighbors are updated accordingly, and these neighbors are added to the queue. The algorithm continues until the f-number of the destination node is lower than the f-number of any node in the queue or the queue is empty. The f-value of the destination node is the cost of the shortest path because in the heuristic describing the destination, the h-value at the destination node is zero.
And finally, performing reverse search on the whole result queue, and sending out the reverse search from the destination to the starting place to obtain the shortest path. In graph theory, this process is referred to as an augmentation process.
For a general path planning algorithm, most of requirements can be met by adopting an A Star algorithm of traditional two-dimensional space search. However, for multi-car path planning, the conventional a Star algorithm has some limitations: (1) When each vehicle is independently planned, it is difficult to ensure that each path does not collide; (2) The planning of a single path lacks global optimization, and it is difficult to guarantee minimum completion time (ETA, ESTIMATE TIME of arrival) for horizontal transport, although the shortest path can be guaranteed without deadlock if each bicycle simply stops waiting.
In this embodiment, step S112: after expanding the two-dimensional space map to the three-dimensional space-time map, the method further comprises the following steps:
s1121: in the three-dimensional space-time map, calculating an arrival difficulty index of each space node;
in step S114, when searching for the next path node in the three-dimensional space-time map, the heuristic function is used to calculate the arrival difficulty index of each spatial node in the three-dimensional space-time map.
The heuristic function in the conventional A Star algorithm is calculated from the Manhattan distance of the current node to the destination node. Since in the present invention the destination description changes from a single point to a straight line, the original Manhattan distance will not be well suited for use in heuristic functions.
Specifically, the invention replaces the Manhattan distance between two nodes in the traditional A Star algorithm by the arrival difficulty index of each space node, and in the heuristic function, the cost of the shortest path from each space node to the destination node is estimated according to the arrival difficulty index of each space node in the three-dimensional space-time map.
Further, in step S1121, the arrival difficulty index of each spatial node is calculated, including the following steps:
S11211: in a two-dimensional space map, sequentially calculating an arrival difficulty index from each space node to an adjacent node;
S11212: for each space node, calculating an average value of the arrival difficulty indexes of the space node to each adjacent node thereof as the arrival difficulty index of the space node, for example, the arrival difficulty indexes of one space node to four adjacent nodes thereof are respectively 2,1, 2 and 1, and then the arrival difficulty index of the space node is (2+1+2+1)/4=1.5.
In this embodiment, calculating the arrival difficulty indicator for each spatial node to its neighboring node includes calculating the Manhattan distance or travel time (TRAVEL TIME) of each spatial node to its neighboring node as the corresponding arrival difficulty indicator. Manhattan distance (MANHATTAN DISTANCE) is a geometric term used in a geometric metric space to designate the sum of absolute wheelbases of two points in a standard coordinate system. The Manhattan distance comparison is applicable to scenes with roads which are horizontal and vertical and are regularly distributed, such as a path planning scene of container trucks at a wharf, and is applicable to the Manhattan distance due to the fact that roads of the wharf are horizontal and vertical. In the path planning scene in the urban actual road, the urban road shape is not particularly regular, and the influence of the inclined turnout and the road speed limit can be caused, so the method is more suitable for adopting the travel time. The travel time may be calculated based on the actual length of each link and the road speed limit for that link.
Faloutsos provides a Fast Map method, which uses the furthest iterative calculation of the physical distance of a Map to generate a distance Map for estimating each point. Specifically, as shown in fig. 3, step S11211: the method sequentially calculates the arrival difficulty index from each space node to the adjacent node of the space node, and comprises the following steps:
S11211-1: finding two space nodes with the farthest physical distance in the original two-dimensional space map;
S11211-2: sequentially searching the space nodes which are the next farthest from the two space nodes which are the farthest by using a depth-first traversal method, and iteratively generating a four-dimensional matrix;
The traditional Fast Map method expands a two-dimensional space Map to a three-dimensional scene, and the method is applied to the three-dimensional space Map and needs to be promoted to four dimensions, wherein a first dimension and a second dimension in a four-dimensional matrix are respectively two dimensions of the two-dimensional space Map, the third dimension corresponds to time, and the fourth dimension corresponds to an arrival difficulty index from each space node to an adjacent node, namely, the Fast Map dimension.
The specific implementation manner of step S11212 is: after traversing all the spatial nodes in the original two-dimensional spatial Map, the whole four-dimensional scene is compressed back to the three-dimensional space-time Map, and the distance from each spatial node to other spatial nodes is the average of all values of the fourth dimension (Fast Map dimension).
In this embodiment, in step S11211-1, when two spatial nodes with the farthest physical distance are found in the original two-dimensional spatial map, two spatial nodes with the farthest manhattan distance or the longest travel time may be found in the two-dimensional spatial map.
In this embodiment, due to the increased time dimension, the short-path-based multi-vehicle deadlock prevention method is applied to multi-vehicle path planning, and when searching for the next path node in the three-dimensional space-time map, the vehicle path planned later is constrained by the vehicle path planned earlier, i.e., the vehicle path planned later cannot pass through the same spatial node at the same time as the vehicle path planned earlier. The invention ensures that the path planning of each bicycle does not conflict with other vehicles in the multi-bicycle path planning through the closed list.
As shown in fig. 4, specifically, the multi-vehicle deadlock prevention method based on short paths is applied to multi-vehicle path planning, and in step S114, when searching for the next path node in the three-dimensional space-time map, a closed list is established by adopting the following steps:
s1141: acquiring data of a planned vehicle path according to a preset vehicle path planning sequence;
S1142: determining the transit time of the planned vehicle path at each space node;
S1143: determining locking nodes according to the passing time of the planned vehicle path in each space node and the space coordinates of the corresponding space node;
s1144: adding the locking node to the closed list.
In this embodiment, the closed list further includes a preset default locking node, where the time corresponding to the default locking node is a time range of the three-dimensional space-time map. The default locking node is, for example, a node which is not accessible for known road maintenance or road failure.
Thus, the closed list may be expressed as the following formula:
Where L this represents all the closed nodes in the current closed list, close represents the current default lock node, Represents a set of locking nodes determined using step S1143 based on the data of the planned vehicle path.
Thus, by employing the closed list of the present invention, it is not necessary to lock all of the time for each bicycle's path. That is, the lock for each physical node on the map is only temporary, if and only if the vehicle is scheduled to pass for a period of time, the node is locked, otherwise the node will be open. The present invention accomplishes this by adding the result of each seek to the closed list of the next seek.
Theoretically, the time range t_space of the three-dimensional space-time map may include all times after the start time. However, too large a time range t_space would result in an increase in the algorithm search space, and too small a time range t_space would not be able to derive a spatial path, as sufficient in-place latency may not be scheduled in a limited (too small) space. Therefore, if the appropriate size of t_space is selected, it is also a key factor affecting the algorithm.
In this embodiment, after the initial parameters of the path planning are acquired in step S111, the method further includes the following steps:
Calculating travel time from the origin to the destination;
A time range of the three-dimensional space-time map is defined according to the start time and the travel time. Specifically, the starting time is taken as a starting point of a time range of the three-dimensional space-time map, and the time obtained by adding the travel time to the starting time can be taken as an ending point of the time range of the three-dimensional space-time map, namely, the size of the time range t_space is equal to the travel time from the starting place to the destination. The travel time can also be multiplied by a preset coefficient, and then added with the starting time to be used as the ending point of the time range of the three-dimensional space-time map, and the size of the coefficient can be adjusted according to requirements.
When the path planning is performed in step S114, a plurality of operation threads may be simultaneously performed, and the value of the time range t_space is substituted into the algorithm for the path finding in each operation thread. Once one thread gets a legitimate path, the computation of the other threads is stopped.
The present invention may be embodied in the above pseudocode using the c++ language or other languages. To test the feasibility of the algorithm, the inventors constructed a series of test maps and performed algorithm verification on these maps. Of these, 3 are chosen as the most representative, with map 1 having 64 nodes, map 2 having 450 nodes, and map 3 having 80000 nodes.
For different maps, the algorithm of the invention randomly selects two points on the map as the starting place and the destination of the vehicle, iterates 10000 times, calculates the calculation time of each road finding result and takes an average value. Table 1 below shows the calculation results for a single calculation (bicycle system).
Table 1 single calculation results
Map(s) Map 1 Map 2 Map 3
Average calculation time 10ms 122ms 154ms
Average number of path-finding iteration layers 5432 11284 14538
As can be seen from the bicycle system results, the path planning method of the invention shows higher convergence speed on a large map, and when the number of nodes is increased, the average calculation time can still be within an acceptable range. And the iteration times of the algorithm are also in a reasonable interval.
For multiple calculations (multi-vehicle system), the path planning method of the present invention has the following calculation results:
TABLE 2 multiple calculation results
Map(s) Map 1 Map 2 Map 3
Average calculation time 30ms 150ms 530ms
Average number of path-finding iteration layers 6208 63289 118275
For a multi-vehicle environment, the algorithm of the invention can still obtain a relatively high speed in the medium-small map, however, when the number of vehicles is increased, the calculation time of the invention is correspondingly increased. This is because the lock nodes of multiple vehicles have an impact on the following vehicle's route finding. More locks means more iteration layers and thus more computation time.
Fig. 4 is a schematic structural diagram of the short-path-based multi-vehicle deadlock prevention system of the present invention. As shown in fig. 4, the short-path-based multi-vehicle deadlock prevention system 5 of the present invention includes:
the path planning module 51 performs time-spread-based path planning for each vehicle.
The path segmentation module 52 segments the planned path of each vehicle into a plurality of short path regions based on a preset length.
The waiting link module 53 applies for at least one short path area in front of the traveling direction based on the passage time of the path planning while the vehicle is traveling, stops waiting if the short path area has been applied for by another vehicle, and generates waiting link information based on the waiting relationship.
The queue head tracing module 54 traces back whether the waiting vehicles at the queue head in the waiting link information currently have the waiting vehicles, if so, supplements the waiting relationship to the waiting link information, and returns to step S140. If not, step S150 is performed.
The path adjustment module 55 plans a short path area for the head-of-line vehicle located at the queue source in the waiting link information, which does not overlap with the path plans of other vehicles. And
The head-of-line release module 56 releases the head-of-line vehicles in the waiting link information after the tail-of-line vehicles in the waiting link information pass through the first overlap region.
The multi-vehicle anti-deadlock system based on the short path can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevents deadlock, realizes vehicle collision prevention and deadlock avoidance, and improves the overall transportation efficiency of a fleet.
The embodiment of the invention also provides a multi-vehicle deadlock prevention device based on the short path, which comprises a processor. A memory having stored therein executable instructions of a processor. Wherein the processor is configured to execute the steps of the short path based multi-truck deadlock prevention method via execution of the executable instructions.
As described above, the multi-vehicle anti-deadlock device based on the short path can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevents deadlock, realizes vehicle collision prevention and deadlock avoidance, and improves overall transportation efficiency of a fleet.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" platform.
Fig. 5 is a schematic structural diagram of the short-path-based multi-vehicle deadlock prevention device of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 600 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, the electronic device 600 is embodied in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including memory unit 620 and processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above-described electronic prescription flow processing method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage platforms, and the like.
The embodiment of the invention also provides a computer readable storage medium for storing a program, and the steps of the short-path-based multi-vehicle deadlock prevention method are realized when the program is executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the electronic prescription stream processing method section of this specification, when the program product is run on the terminal device.
As described above, when the program of the computer readable storage medium of the embodiment is executed, the program can be applied to path collaborative planning between multiple unmanned vehicles, effectively prevent occurrence of deadlock, realize collision prevention and deadlock avoidance of vehicles, and improve overall transportation efficiency of a fleet.
Fig. 6 is a schematic structural view of a computer-readable storage medium of the present invention. Referring to fig. 6, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the short-path-based multi-vehicle anti-deadlock method, system, equipment and storage medium can be applied to path collaborative planning among multiple unmanned vehicles, effectively prevent deadlock, realize vehicle anti-collision and deadlock avoidance, and improve the overall transportation efficiency of a fleet.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.

Claims (10)

1. A multi-vehicle deadlock prevention method based on a short path is characterized by comprising the following steps:
S110, planning a path based on time expansion for each vehicle;
s120, dividing the planned path of each vehicle into a plurality of short path areas based on a preset length;
S130, applying for at least one short path area in front of a driving direction based on the passing time of path planning when the vehicle is in driving, if the short path area is applied for by another vehicle, the paths of the two vehicles have a first overlapping area, the vehicles stop and wait, waiting link information is generated based on a waiting relation, the waiting vehicles serve as tail vehicles in the waiting link information, the waiting link information comprises a plurality of vehicle data pairs, each vehicle data pair comprises a waiting vehicle and a waiting vehicle, and the waiting vehicles in the vehicle data pairs are identical with the waiting vehicles in the adjacent vehicle data pairs arranged in front of the waiting vehicles; s140, tracing whether the waiting vehicles at the head of the queue in the current waiting link information also have the waiting vehicles, if so, supplementing the waiting relationship to the waiting link information, including generating a new vehicle data pair by the head of the queue in the current waiting link information and the waiting vehicles at the head of the queue, adding the new vehicle data pair to the head of the waiting link information, and returning to the step S140; if not, executing step S150;
S150, planning a short-path area which is not overlapped with the path planning of other vehicles for the head-of-line vehicles positioned at the queue source in the waiting link information; and
S160, when the head vehicles in the waiting link information pass through the first overlapping area, releasing the tail vehicles in the waiting link information.
2. The short-path-based multi-vehicle deadlock prevention method according to claim 1, wherein in the step S120, further comprising:
traversing the short-path areas based on the electronic map, and merging the short-path areas belonging to the same road node when two adjacent short-path areas belong to the same road node, wherein the road node is one of a curve or an intersection.
3. The short-path-based multi-vehicle deadlock prevention method according to claim 1, wherein each of the pairs of vehicle data in the waiting link information further includes position information of a short-path region where an overlapping region where the waiting vehicle and the waiting vehicle overlap.
4. The short-path-based multi-vehicle deadlock prevention method according to claim 1, wherein the step S150 includes:
If the total number of vehicles in the waiting link information exceeds a preset threshold, and a short-path area which is not overlapped with the path planning of all other vehicles in the waiting link information exists in other short-path areas which are connected with the head-of-team vehicles at the queue source based on the current position in the waiting link information based on the passing time, the head-of-team vehicles apply for and drive into the short-path area, and the value range of the preset threshold is larger than 2.
5. The short-path-based multi-vehicle deadlock prevention method according to claim 1, wherein the step S110 includes:
S111, acquiring initial parameters of path planning and a two-dimensional space map, wherein the initial parameters comprise space coordinates of a starting place, starting time and space coordinates of a destination, the two-dimensional space map comprises a plurality of space nodes, and each space node has a two-dimensional space coordinate;
S112, expanding a two-dimensional space map to a three-dimensional space-time map, wherein a first dimension and a second dimension are two dimensions of the two-dimensional space map respectively, a third dimension is time, and a time range of the three-dimensional space-time map is started by the starting time;
S113, determining a starting place space-time node in the three-dimensional space-time map according to the space coordinates of the starting place and the starting time, and determining a destination straight line in the three-dimensional space-time map according to the space coordinates of the destination and the time range of the three-dimensional space-time map;
S114, searching the next path node in the three-dimensional space-time map from the starting space-time node based on an A Star path planning algorithm so as to obtain the shortest path from the starting place to the destination.
6. The short-path-based multi-vehicle deadlock prevention method of claim 5, wherein when searching for a next path node in the three-dimensional space-time map, a space-time constraint is set for selection of the next path node, the space-time constraint comprising: the next spatiotemporal node available for searching includes a spatiotemporal node determined from the spatial coordinates of the current spatiotemporal node and the next time and a node determined from the spatial coordinates of spatially contiguous nodes of the current spatiotemporal node and the next time.
7. The short-path-based multi-vehicle deadlock prevention method according to claim 5, wherein after expanding the two-dimensional space map to the three-dimensional space-time map, further comprising the steps of:
Calculating an arrival difficulty index of each space node in the three-dimensional space-time map;
and when searching the next path node in the three-dimensional space-time map, calculating the adopted heuristic function according to the arrival difficulty index of each space node in the three-dimensional space-time map.
8. A short-path-based multi-truck deadlock prevention system, the system comprising:
The path planning module is used for planning a path based on time expansion for each vehicle;
the path segmentation module segments the planned path of each vehicle into a plurality of short path areas based on a preset length;
A waiting link module, wherein the vehicle applies for at least one short path area in front of a driving direction based on the passing time of path planning in driving, if the short path area is applied for by another vehicle, paths of the two vehicles have a first overlapping area, the vehicles stop for waiting, waiting link information is generated based on a waiting relation, the waiting vehicles serve as tail vehicles in the waiting link information, the waiting link information comprises a plurality of vehicle data pairs, each vehicle data pair comprises a waiting vehicle and a waiting vehicle, and the waiting vehicles in the vehicle data pairs are identical with the waiting vehicles in the adjacent vehicle data pairs arranged in front of the waiting vehicles;
The queue head tracing module is used for tracing whether the current waiting vehicles at the queue head in the waiting link information also have waiting vehicles, if so, supplementing the waiting relation to the waiting link information, generating a new vehicle data pair by the current queue head vehicles in the waiting link information and the waiting vehicles at the queue head vehicles, adding the new vehicle data pair to the head of the waiting link information, and returning to the queue head tracing module; if not, executing a path adjustment module;
the path adjustment module is used for planning a short path area which is not overlapped with the path planning of other vehicles for the head-of-queue vehicles positioned at the queue source in the waiting link information; and
And the queue head releasing module releases the queue tail vehicles in the waiting link information after the queue head vehicles in the waiting link information pass through the first overlapping area.
9. A multi-truck deadlock prevention device based on a short path, comprising:
A processor;
A memory having stored therein executable instructions of the processor;
Wherein the processor is configured to perform the steps of the short path based multi-truck deadlock prevention method of any of claims 1 to 7 via execution of the executable instructions.
10. A computer readable storage medium storing a program, characterized in that the program when executed implements the steps of the short path based multi-vehicle deadlock prevention method according to any of claims 1 to 7.
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