CN115562356A - Flight vehicle graph search path planning method, terminal device and medium - Google Patents

Flight vehicle graph search path planning method, terminal device and medium Download PDF

Info

Publication number
CN115562356A
CN115562356A CN202211424330.4A CN202211424330A CN115562356A CN 115562356 A CN115562356 A CN 115562356A CN 202211424330 A CN202211424330 A CN 202211424330A CN 115562356 A CN115562356 A CN 115562356A
Authority
CN
China
Prior art keywords
node
path
nodes
searching
obstacle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211424330.4A
Other languages
Chinese (zh)
Inventor
赵靖
王伟达
杨超
李颖
岳兴
杨刘权
徐彬
项昌乐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN202211424330.4A priority Critical patent/CN115562356A/en
Publication of CN115562356A publication Critical patent/CN115562356A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Navigation (AREA)

Abstract

The invention discloses a method, a terminal device and a medium for planning a search path of a flying vehicle map, wherein the method comprises the following steps: inputting a starting point and an end point, and connecting the starting point serving as a first key search node with the end point; defining adjacent nodes of the first barrier crossed by the connecting line as new key searching nodes and connecting the new key searching nodes with the key searching nodes at the upper stage; judging whether the connecting line passes through the barrier; if not, recording a path, if so, defining a key search node for the adjacent node of the barrier, expanding eight adjacent child nodes by the node, repeating the connection process, and defining the child node of which the connecting line does not pass through the barrier as the key search node; the key searching node is continuously connected with the end point, a new key searching node is defined, and the connection process with the upper-level node is repeated; deleting the transition path nodes; from which the shortest path is found. The invention has the advantages that: the number of search nodes of a graph search algorithm is reduced, the planning efficiency is improved, and a multi-mode path which considers reasonable switching of the air-ground movement mode is planned.

Description

Flight vehicle graph search path planning method, terminal device and medium
Technical Field
The invention relates to the technical field of path planning, in particular to a method, terminal equipment and medium for planning a searching path of a flying vehicle map.
Background
Graph search algorithms have proven to be an effective means of solving the path planning problem. They find a shortest path by searching for path points in the grid map and calculating corresponding cost values. One basic problem of existing algorithms, such as Dijkstra, a, and its modifications, is how to find the shortest path among the fewest possible search nodes. They face a trade-off between path distance and planning efficiency.
The problems restrict the application of the graph search algorithm in the planning of the flight vehicle path. The flying vehicle has two motion modes, namely a ground running mode and an air flying mode. The path planning of a flying vehicle needs to consider the planning of the ground/air path and judge the proper time and position for switching different modes. The complex planning mechanism further requires that the algorithm has higher planning efficiency, and the shortest path is obtained by fewer search nodes.
The prior art is as follows:
(1)Dijkstra
as a classic graph search algorithm, dijkstra's algorithm can find a shortest path in a grid map. The method adopts a greedy idea to search global situation, starts from a starting point, traverses all nodes in a map, calculates the path distance cost of reaching each node, and finds a shortest path [1] 。
The disadvantages are as follows:
since Dijkstra traverses all nodes in the map, the number of search nodes is excessive and the search efficiency is poor. Does not meet the requirement of high-efficiency planning of flying vehicles.
(2)A*
The A-star algorithm adds heuristic information on the basis of the Dijkstra algorithm. In this algorithm, the path distance cost to each node of the map consists of two parts: the known path distance cost of the starting point to the current searching node and the estimated path distance cost of the current searching node to the end point. The computation of the cost of the known path distance is the same as Dijkstra. The cost of estimating the path distance is a newly added heuristic information item, and Euclidean distance calculation can be adopted. The a-algorithm generally adopts an eight-neighborhood search mode, that is, a starting point serves as a parent neighborhood and search is expanded to eight directions of child neighborhoods. The child neighborhood with the minimum path distance cost is selected as the parent neighborhood of the next search, eight-neighborhood search is continuously adopted until the terminal point is reached [2] 。
The disadvantages are that:
compared with Dijkstra, A effectively reduces the number of search nodes, improves the planning efficiency, but the improvement is relative, A still has a plurality of redundant search nodes, and the planning efficiency has a further improved space [3] 。
The prior art has the following defects:
1. the existing graph search algorithm faces the problems of searching node redundancy and low planning efficiency. How to find the shortest path among the fewest possible search nodes is a technical problem that needs to be studied intensively.
2. Due to the restriction of the problems, the conventional graph search algorithm is difficult to plan a short multi-modal path for a flying vehicle with high efficiency.
3. The multi-mode path planning of the flying vehicle needs to comprehensively consider the planning of ground/air paths and reasonably switch different motion modes. The existing multi-modal path planning technology is not complete.
Reference to the literature
[1] Corning, coal mine underground emergency path planning research [ D ] based on an improved Dijkstra algorithm, sigan university of technology, 2020.DOI;
[2] study on the application of the a-x algorithm for fusion improvement of artificial potential field in global path planning of robot [ D ]. University of cantonese, 2022. Doi;
[3]L.Xie,S.Xue,J.Zhang,M.Zhang,W.Tian,S.Haugen,“A path planning approach based on multi-direction A*algorithm for ships navigating within wind farm waters,”Ocean Engineering,vol.184,pp.311-322,2019。
disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method, a terminal device and a medium for planning a searching path of a flying vehicle map.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
a method for planning a search path of a flying vehicle map comprises the following specific steps:
step1, inputting a starting point and an end point, and connecting the starting point as a first key search node with the end point;
and creating a connection table NODE and a PATH table PATH, and recording the key search NODEs and the directed PATHs between the NODEs respectively. And inputting a starting point and an end point, and recording the starting point in the table NODE as a first key searching NODE. The key search NODE in the NODE is connected to the endpoint, and the neighboring NODE of the first obstacle that the connection crosses is defined as the new key search NODE and recorded in the table NODE.
And Step2, searching the nodes. And connecting the newly added key searching NODE in the NODE with the key searching NODE at the upper stage. Judging whether the connecting line passes through the barrier;
if not, the connection line is a directed PATH from the node at the upper stage to the newly added node, and is recorded in the table PATH.
And if the obstacle passes through the obstacle, judging whether the obstacle is a new obstacle, if the adjacent NODE of the obstacle is not defined, updating NODE, adding the adjacent NODE as a new key searching NODE, repeating the Step2 connection process, and updating the directed PATH in the PATH.
If the adjacent NODEs of the barrier are defined with key searching NODEs, the NODEs expand eight adjacent sub-NODEs outwards, the sub-NODEs repeat the connection process of Step2, the sub-NODEs of which the connecting lines do not pass through the barrier are defined as the key searching NODEs, the key searching NODEs are recorded into the NODE, and the directed PATH in the PATH is updated.
And continuing connecting the key searching NODE and the terminal point in the NODE, recording a new key searching NODE, and repeating Step2 and the judgment process.
Step3: and (6) checking the nodes. And checking whether redundant transition PATH nodes exist in the directed PATH in the PATH, and if the redundant transition PATH nodes exist, deleting the transition PATH nodes. And constructing a dynamic directed graph by using the table NODE and the key searching NODE and the directed PATH in the table PATH. As the search progresses, the dynamic directed graph is progressively refined, finding the shortest path from it.
Further, judging the Step3 redundant transition path node: and sequentially judging whether the nodes in one directed path have the condition that the nodes can be directly connected with the nodes at the non-adjacent positions, and if so, deleting all other nodes between the two nodes, namely transition path nodes.
Further, if the flying vehicle is in a ground driving mode, the adjacent nodes and the eight adjacent sub-nodes of the barrier only have two-dimensional plane nodes,
further, if the flying vehicle is in an air flight mode, a two-dimensional plane node and an obstacle height node exist in the adjacent node and the eight adjacent sub-nodes of the obstacle. Let H flight The required flying height to fly over an obstacle area; h max The maximum flying height of the flying vehicle; judgment of H flight Whether or not it is greater than H max If the number of the obstacle is larger than the preset number, defining the obstacle area to be flown, and if the number of the obstacle area is smaller than the preset number, defining the obstacle area to be flown;
further, let D ground A two-dimensional distance cost for the directed path; d flight Fly-height cost for a directed path;
the total cost of a directed path through an obstacle area to be flown also includes D ground And D flight . And the directed path not passing through the above-mentioned region only contains D ground 。
And finding the path with the minimum cost, namely the shortest distance from all the directed paths.
The invention also discloses a computer terminal device, which is installed in the aerocar and used for controlling the route planning of the aerocar, and comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the method for planning the searching path of the aerocar when executing the program.
The invention also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the above flying vehicle map search path planning method.
Compared with the prior art, the invention has the advantages that:
1. the key search nodes in the grid map are dynamically extracted, the dynamic directed graph is constructed to carry out shortest path planning, the number of the search nodes of the graph search algorithm is effectively reduced, and the planning efficiency is improved.
2. The method is applied to path planning of flying vehicles, redefines the region of the barrier to be flown and increases the flying height cost in cost calculation of the directed path. A multi-mode path considering reasonable switching of the air-ground movement modes is planned, and the driving cost can be saved in a mode of combining the air-ground movement.
Drawings
FIG. 1 is a schematic structural diagram of a method for planning a search path of a flying vehicle diagram according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a search path plan of a diagram of a flying vehicle according to an embodiment of the invention;
FIG. 3 is a flow chart of the present invention for increasing fly height cost of a flying vehicle graph search path plan.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
The invention provides a novel Graph search Algorithm, which is called Dynamic Directed Graph Algorithm (DDGA). During the planning process of the DDGA algorithm, the current searching node is connected with the terminal. The neighboring nodes of the obstacles traversed by the links are defined as key search nodes. Typically, the present invention only records the neighboring nodes of the first obstacle traversed. As the search progresses, the current search nodes change and the defined key search nodes are also dynamically updated. And establishing a dynamic directed graph by the key searching nodes and the directed paths among the nodes. In the dynamic directed graph, the minimum cost (shortest distance) directed path between the starting point and the end point is the shortest path planned finally.
As shown in fig. 1, a method for planning a search path of a flying vehicle map includes the following steps:
and Step1, initializing the algorithm. And creating a table NODE and a table PATH, and recording the key search NODE and the directed PATH between the NODEs respectively. The starting point is recorded as the first key search NODE in the table NODE. The key search NODE in the NODE is connected to the endpoint, and the neighboring NODE of the first obstacle that the connection crosses is defined as the new key search NODE and recorded in the table NODE. The key search node connected to the end point is referred to as a previous node of the newly defined key search node.
And Step2, searching the nodes. And connecting the newly added key searching NODE and the previous searching NODE in the table NODE. And judging whether the connection line passes through the barrier or not, if not, recording the connection line as a directed PATH from the upper-level node to the newly added node into the table PATH. If the barrier is crossed, whether the barrier is a new barrier is judged (namely whether the adjacent NODE of the barrier is defined as a key search NODE and is recorded in the table NODE), if the adjacent NODE of the barrier is not defined, the NODE is updated, the adjacent NODE is added as a new key search NODE, the connection process of Step2 is repeated, and the directed PATH in the PATH is updated. If the adjacent NODE of the obstacle is already defined with a key searching NODE (found in the NODE), the NODE expands eight adjacent child NODEs outwards, the child NODEs repeat the connection process of Step2, the child NODEs of which the connecting lines do not cross the obstacle are defined as key searching NODEs, the key searching NODEs are recorded in the NODE, and the directed PATH in the PATH is updated. And continuously connecting the key searching NODE and the end point in the NODE, recording a new key searching NODE, and repeating the Step2 and the judging process.
Step3: and checking the nodes. Checking whether redundant transition PATH nodes exist in the directional PATHs in the PATH, for example, if a certain directional PATH is a starting point → A → B → C, and the starting point can be directly connected with the C node, then A and B are redundant transition PATH nodes. The final updated directional path is start → C. And constructing a dynamic directed graph by using the table NODE and the key searching NODE and the directed PATH in the table PATH. As the search progresses, the dynamic directed graph is progressively refined, finding the shortest path from it.
A detailed diagram of the DDGA algorithm is shown in fig. 2.
(a) In the figure, the starting point is recorded as the first key search NODE in the table NODE. Connecting the starting point and the end point, the line crosses the obstacle 1 and the obstacle 2. Defining the adjacent NODEs of the obstacle 1 as key searching NODEs, and recording the key searching NODEs into a table NODE, such as a graph n 2 And n 3 . The starting point is n 2 And n 3 The upper level node of (1). (b) In the figure, the starting point is connected to n 2 Starting point and n 3 . Wherein the starting point is n 3 Does not cross the obstacle, so there is a directional path starting point → n 3 Recorded in the table PATH. (c) In the figure, the starting point and n 2 Has crossed the obstacle and the neighboring nodes of the obstacle have been defined as key search nodes (n) 2 、n 3 ) In n is 2 Eight adjacent child nodes are expanded outward for the center. Sequentially connecting the starting point and the child NODEs, defining the child NODEs of which the connecting lines do not pass through the barrier as new key searching NODEs, and recording the new key searching NODEs into the NODE, as shown in the graph n 4 、n 5 、n 6 、n 7 . The starting point reaches n 2 The directional path of (1) is the starting point → n 4 →n 2 Starting point → n 5 →n 2 Starting point → n 6 →n 2 Starting point → n 7 →n 2 The table PATH is updated. (d) In the figure, connection n 2 When the connection line with the terminal point does not cross any barrier, the starting point passes through n 2 The directional path to the end point is the starting point → n 4 →n 2 → end point, start point → n 5 →n 2 → end point, start point → n 6 →n 2 → end point, start point → n 7 →n 2 → endpoint, update table PATH. Connection n 3 Connecting with the terminal point, the connecting line passes through the barrier 2, defining the adjacent NODE of the barrier 2 as a key searching NODE, and recording the key searching NODE into a table NODE, such as a graph n 8 And n 9 。n 3 Is n 8 And n 9 The upper level node of (2). (e) In the figure, connection n 3 And n 8 ，n 3 And n 9 . The connecting line does not pass through the barrier and reaches n from the starting point 8 And n 9 The directional path of (1) is a starting point → n 3 →n 8 Starting point → n 3 →n 9 The table PATH is updated. (f) In the figure, connection n 8 And end point, n 9 And an endpoint. Wherein n is 8 The line connecting the end point does not cross the obstacle, and the starting point passes through n 8 The path to the end point is the starting point → n 3 →n 8 → endpoint, update table PATH. (g) In the figure, n 9 The line connecting with the terminal point crosses the obstacle, and the adjacent node of the obstacle has been defined as the key searching node (n) 8 、n 9 ) In n is given 9 Eight adjacent child nodes are expanded outward for the center. Connecting the terminal point and the child NODE in turn, recording the child NODE of which the connecting line does not pass through the barrier into the NODE, as shown in the figure n 10 、n 11 、n 12 . Starting point passing n 9 The path to the end point is the start point → n 3 →n 9 →n 10 → end point, start point → n 3 →n 9 →n 11 → end point, start point → n 3 →n 9 →n 12 → endpoint, update table PATH. (h) In the graph, whether redundant transition PATH nodes exist in the directed PATH in the PATH is checked. Upon inspection, n 4 And n 5 Can reach the end point directly, so at the start of the directed path → n 4 →n 2 → end and start → n 5 →n 2 → in endpoint, n 2 Are redundant transition path nodes. Removing n 2 The updated path is the starting point → n 4 → end and start points → n 5 → endpoint, update table PATH. In the same way, n 3 Can be reacted with n 11 And n 12 Directly connected, at the start of the directed path → n 3 →n 9 →n 11 → end and start → n 3 →n 9 →n 12 In the end point, → n 9 Are redundant transition path nodes. Removing n 9 The updated path is the starting point → n 3 →n 11 → endpoint andstarting point → n 3 →n 12 → endpoint, update table PATH. Tables NODE and PATH develop a dynamic directed graph step by step, the final complete directed graph is seen in (i). In the graph (i), the directed path having the minimum two-dimensional distance cost between the start point and the end point is the final shortest path. Shortest path as starting point → n 3 →n 8 → the endpoint.
From the analysis, the DDGA extracts key search nodes in the grid map and constructs a dynamic directed graph to perform shortest path planning. In fig. 2 (h), the DDGA only needs 13 search nodes to implement final shortest path planning, which effectively reduces the number of search nodes of the graph search algorithm and improves planning efficiency.
The DDGA algorithm can also be used for planning the path of a flying vehicle, and the specific flow is shown in figure 3. Wherein H flight The required flying height to fly over an obstacle area; h max The maximum flying height of the flying vehicle; d ground A two-dimensional distance cost for the directed path; d flight The fly height cost for a directed path.
Step1: the flying vehicle has two motion modes, namely a ground running mode and an air flying mode. H flight Not more than H max Redefined as the obstacle area to be flown. The vehicle has the ability to fly over the above area.
Step2: the DDGA algorithm plans a multi-modal path. It should be noted that, the directional PATH is allowed to pass through the barrier area to be flown, and the above directional PATH is also recorded in the table PATH; in the dynamic directed graph, the calculation cost of the directed path increases the flying height cost besides the initial two-dimensional distance cost, and the reasonable air-ground motion mode switching is ensured. And between the starting point and the end point, the directional path with the minimum total cost is the final shortest multi-modal path of the flying vehicle.
In yet another embodiment of the present invention, a terminal device for loading into an aircraft for controlling a route plan of the aircraft is provided, comprising a processor and a memory, the memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for operating the method for planning the searching path of the flying vehicle map, and comprises the following steps:
and Step1, initializing the algorithm. And creating a table NODE and a table PATH and recording the key searching NODEs and the directed PATHs between the NODEs respectively. The starting point is recorded as the first key search NODE in the table NODE. The key search NODE in the NODE is connected with the terminal, and the adjacent NODE of the first barrier crossed by the connecting line is defined as a new key search NODE and is recorded in the table NODE. The key search node connected to the end point is referred to as a previous node of the newly defined key search node.
And Step2, searching the nodes. And connecting the newly added key searching NODE and the previous searching NODE in the table NODE. And judging whether the connecting line passes through the barrier or not, if not, recording the connecting line as a directed PATH from the upper-level node to the newly added node into the table PATH. If the obstacle is crossed, whether the obstacle is a new obstacle is judged (namely whether the adjacent NODE of the obstacle is defined as a key search NODE and is recorded in the table NODE), if the adjacent NODE of the obstacle is not defined, the NODE is updated, the adjacent NODE is added as a new key search NODE, the connection process of Step2 is repeated, and the directed PATH in the PATH is updated. If the adjacent NODE of the obstacle is already defined with a key searching NODE (found in the NODE), the NODE expands eight adjacent child NODEs outwards, the child NODEs repeat the connection process of Step2, the child NODEs of which the connecting lines do not cross the obstacle are defined as key searching NODEs, the key searching NODEs are recorded in the NODE, and the directed PATH in the PATH is updated. And continuing connecting the key searching NODE and the terminal point in the NODE, recording a new key searching NODE, and repeating Step2 and the judgment process.
Step3: and (6) checking the nodes. Checking whether redundant transition PATH nodes exist in the directional PATHs in the PATH, for example, if a certain directional PATH is a starting point → A → B → C, and the starting point can be directly connected with the C node, then A and B are redundant transition PATH nodes. The final updated directional path is start → C. And building a dynamic directed graph by using the table NODE and the key searching NODE and the directed PATH in the table PATH. As the search progresses, the dynamic directed graph is progressively refined, finding the shortest path from it.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer readable storage medium may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer-readable storage medium to realize the corresponding steps of the method for planning the searching path of the flying vehicle map in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
and Step1, initializing the algorithm. And creating a table NODE and a table PATH, and recording the key search NODE and the directed PATH between the NODEs respectively. The starting point is recorded as the first key search NODE in the table NODE. The key search NODE in the NODE is connected with the terminal, and the adjacent NODE of the first barrier crossed by the connecting line is defined as a new key search NODE and is recorded in the table NODE. The key search node connected to the end point is referred to as a previous node of the newly defined key search node.
And Step2, searching nodes. And connecting the newly added key searching NODE and the previous searching NODE in the table NODE. And judging whether the connection line passes through the barrier or not, if not, recording the connection line as a directed PATH from the upper-level node to the newly added node into the table PATH. If the obstacle is crossed, whether the obstacle is a new obstacle is judged (namely whether the adjacent NODE of the obstacle is defined as a key search NODE and is recorded in the table NODE), if the adjacent NODE of the obstacle is not defined, the NODE is updated, the adjacent NODE is added as a new key search NODE, the connection process of Step2 is repeated, and the directed PATH in the PATH is updated. If the adjacent NODE of the obstacle is already defined with a key searching NODE (found in the NODE), the NODE expands eight adjacent child NODEs outwards, the child NODEs repeat the connection process of Step2, the child NODEs of which the connecting lines do not cross the obstacle are defined as key searching NODEs, the key searching NODEs are recorded in the NODE, and the directed PATH in the PATH is updated. And continuing connecting the key searching NODE and the terminal point in the NODE, recording a new key searching NODE, and repeating Step2 and the judgment process.
Step3: and checking the nodes. Checking whether redundant transition PATH nodes exist in the directional PATHs in the PATH, for example, if a certain directional PATH is a starting point → A → B → C, and the starting point can be directly connected with the C node, then A and B are redundant transition PATH nodes. The final updated directional path is start → C. And building a dynamic directed graph by using the table NODE and the key searching NODE and the directed PATH in the table PATH. As the search progresses, the dynamic directed graph is progressively refined, finding the shortest path from it.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (7)

1. A method for planning a search path of a flying vehicle map is characterized by comprising the following specific steps:
step1, inputting a starting point and an end point, and connecting the starting point as a first key search node with the end point;
creating a connection table NODE and a PATH table PATH to record key search NODEs and directed PATHs between the NODEs respectively; inputting a starting point and an end point, and recording the starting point in a table NODE as a first key searching NODE; connecting a key search NODE and a terminal point in the NODE, wherein an adjacent NODE of a first barrier crossed by a connecting line is defined as a new key search NODE and recorded in a table NODE;
step2, searching nodes; connecting the newly added key searching NODE in the table NODE with the key searching NODE at the previous stage; judging whether the connecting line passes through the barrier;
if not, the connection line is a directed PATH from the upper-level node to the newly added node, and is recorded in the table PATH;
if the obstacle passes through the obstacle, judging whether the obstacle is a new obstacle, if the adjacent NODE of the obstacle is not defined, updating NODE, adding the adjacent NODE as a new key searching NODE, repeating the Step2 connection process, and updating a directed PATH in the PATH;
if the adjacent NODEs of the barrier are defined with key searching NODEs, the NODEs expand eight adjacent sub-NODEs outwards, the sub-NODEs repeat the Step2 connection process, the sub-NODEs of which the connecting lines do not pass through the barrier are defined as key searching NODEs, the key searching NODEs are recorded into the NODE, and the directed PATH in the PATH is updated;
and continuing connecting the key searching NODE and the terminal point in the NODE, recording a new key searching NODE, and repeating Step2 and the judgment process.
Step3: checking nodes; checking whether a directed PATH in the PATH has redundant transition PATH nodes, and if the directed PATH has the transition PATH nodes, deleting the transition PATH nodes; establishing a dynamic directed graph by using the table NODE and the key searching NODE and the directed PATH in the table PATH; as the search progresses, the dynamic directed graph is progressively refined, finding the shortest path from it.
2. The method for planning the searching path of the flying vehicle map according to claim 1, wherein the method comprises the following steps: and Step3, judging redundant transition path nodes: and sequentially judging whether the nodes in one directed path have the condition that the nodes can be directly connected with the nodes at the non-adjacent positions or not, and if so, deleting all other nodes between the two nodes, namely transition path nodes.
3. The method for planning the searching path of the flying vehicle map according to claim 1, characterized in that: if the flying vehicle is in a ground driving mode, the adjacent nodes and the eight adjacent sub-nodes of the barrier only have two-dimensional plane nodes.
4. The method for planning the searching path of the flying vehicle map according to claim 1, characterized in that: if the flying vehicle is in the air flight mode, the two-dimensional plane node and the height node of the obstacle exist in the adjacent node and the eight adjacent sub-nodes of the obstacle. Let H flight The required flying height to fly over an obstacle area; h max The maximum flying height of the flying vehicle; judgment of H flight Whether or not it is greater than H max If the number of the obstacle is larger than the predetermined number, the obstacle is defined as an obstacle region to be flown, and if the number of the obstacle is smaller than the predetermined number, the obstacle region is defined as an obstacle region.
5. The method for planning the searching path of the flying vehicle map according to claim 1, characterized in that: let D ground A two-dimensional distance cost for the directed path; d flight Fly height cost for a directed path;
the total cost of a directed path through an obstacle area to be flown also includes D ground And D flight . And the directed path not passing through the above-mentioned region only contains D ground 。
And finding the path with the minimum cost, namely the shortest distance from all the directed paths.
6. A computer terminal device characterized by: the terminal device is installed in a flying automobile for controlling the route planning of the flying automobile, and comprises: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the flying vehicle graph search path planning method according to one of claims 1 to 5 when executing the program.
7. A computer-readable storage medium characterized by: the computer readable storage medium has a computer program stored thereon, and the program is used for realizing the method for planning the searching path of the flying vehicle map according to one of claims 1 to 5 when being executed by a processor.
CN202211424330.4A 2022-11-14 2022-11-14 Flight vehicle graph search path planning method, terminal device and medium Pending CN115562356A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211424330.4A CN115562356A (en) 2022-11-14 2022-11-14 Flight vehicle graph search path planning method, terminal device and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211424330.4A CN115562356A (en) 2022-11-14 2022-11-14 Flight vehicle graph search path planning method, terminal device and medium

Publications (1)

Publication Number Publication Date
CN115562356A true CN115562356A (en) 2023-01-03

Family

ID=84769809

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211424330.4A Pending CN115562356A (en) 2022-11-14 2022-11-14 Flight vehicle graph search path planning method, terminal device and medium

Country Status (1)

Country Link
CN (1) CN115562356A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117664142A (en) * 2024-02-01 2024-03-08 山东欧龙电子科技有限公司 Method for planning flight vehicle path based on three-dimensional map

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117664142A (en) * 2024-02-01 2024-03-08 山东欧龙电子科技有限公司 Method for planning flight vehicle path based on three-dimensional map
CN117664142B (en) * 2024-02-01 2024-05-17 山东欧龙电子科技有限公司 Method for planning flight vehicle path based on three-dimensional map

Similar Documents

Publication Publication Date Title
CN109059924B (en) Accompanying robot incremental path planning method and system based on A-x algorithm
CN111369066B (en) Path planning method and device, electronic equipment and readable storage medium
KR102009482B1 (en) Apparatus and method for planning path of robot, and the recording media storing the program for performing the said method
CN110823241B (en) Robot path planning method and system based on passable area skeleton extraction
KR102036716B1 (en) Path planning method for visiting multiple mission points and apparatus therefor
CN102435200B (en) Rapid path planning method
CN111811514A (en) Path planning method based on regular hexagon grid jumping point search algorithm
CN108827335B (en) Shortest path planning method based on one-way search model
CN113485369A (en) Indoor mobile robot path planning and path optimization method for improving A-x algorithm
CN110702133B (en) Path planning method, robot and device with storage function
CN112783169A (en) Path planning method and device and computer readable storage medium
CN115562356A (en) Flight vehicle graph search path planning method, terminal device and medium
CN114812566B (en) Method and device for planning driving path of mine vehicle and computer equipment
CN111998858B (en) Unmanned aerial vehicle route planning method based on improved A-algorithm
CN111664851A (en) Robot state planning method and device based on sequence optimization and storage medium
KR102552719B1 (en) Method and apparatus for automatically generating drive route
CN114371711A (en) Robot formation obstacle avoidance path planning method
CN116399364B (en) Vehicle driving road network generation method, device, chip, terminal, equipment and medium
CN113433954A (en) Underwater robot three-dimensional global path planning method based on improved RRT algorithm
Gadre et al. A topological map based approach to long range operation of an unmanned surface vehicle
CN116449826A (en) Mobile robot path planning method based on path smoothing and bidirectional jump point search
KR101585374B1 (en) Apparatus and Method for planning robot path using genetic algorithm
CN113885531B (en) Method for mobile robot, circuit, medium, and program
CN113401140B (en) Route routing method and device for bidirectional road section, computer equipment and storage medium
CN117804481A (en) A-star algorithm based on heuristic function optimization and third-order Bezier curve optimization improvement

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination