CN110006429A - A kind of unmanned boat path planning method based on depth optimization - Google Patents
A kind of unmanned boat path planning method based on depth optimization Download PDFInfo
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Abstract
The invention belongs to unmanned ship's navigation control technology field more particularly to a kind of unmanned boat path planning methods based on depth optimization.Include the following steps: the cartographic information, the setting start-stop point that read rasterizing, the starting point of acquisition and the reachable neighbor node along eight directions of the starting point are added in openlist list；The smallest point of F value in openlist list is found, present node is set as；Judge whether present node is terminal, if terminating pathfinding, if it is not, finding next jump point based on jump point search strategy using present node；Jump point is judged whether in openlist list, continually looks for the smallest point of F value；Export optimal path.The execution efficiency of Grid Method Path Planning, and path optimizing turning point, one smooth ship trajectory of final output are substantially improved based on jump point chess game optimization Preprocessing Algorithm for this method.
Description
Technical field
The invention belongs to unmanned ship's navigation control technology field more particularly to a kind of nobody based on depth optimization
Ship path planning method.
Background technique
Ship track planning refer to ship can according to navigation environment under the premise that security is guaranteed (avoid various dynamics or
Static obstruction) contexture by self goes out an optimal path (i.e. path is most short).
Wherein, the path optimizing based on Grid Method is a kind of the most frequently used at present and most effective path planning method.Grid
Method refers to background map gridding, is then based on grid and carries out path planning.
But there are still more defects for the Grid Method Path Planning applied at present, and there are also the spaces of depth optimization.It is first
First, since algorithm traversal point is excessive, will lead to execution speed is reduced, execution efficiency decline.Secondly as the algorithm is to be based on
Grid is sought a little, so the path cooked up is made of several broken lines, it is unsmooth, in actual motion, it will increase ship and exist
Control difficulty at turning point, is unfavorable for its Track control.
Summary of the invention
(1) technical problems to be solved
For existing technical problem, the present invention provides a kind of unmanned boat trajectory planning side based on depth optimization
Method, the execution efficiency of Grid Method Path Planning is substantially improved based on jump point chess game optimization Preprocessing Algorithm in this method, then leads to
Cross second order Bezier path optimizing turning point, one smooth ship trajectory of final output.
(2) technical solution
A kind of unmanned boat path planning method based on depth optimization, includes the following steps:
A1, based on A star algorithm obtain rasterizing map beginning and end, initialization openlist list and
Closedlist list；
A2, traversal openlist list, find the smallest point of F value in openlist list, are set as present node；
Judge whether present node is terminal, if present node is terminal, pathfinding terminates, step A6 is executed, if it is not,
Present node is deleted in openlist list, present node addition closedlist list is continued to execute into step A3；
A3, using present node, next jump point is found based on jump point search strategy, it is specific as follows:
Judge the direction of present node, the direction of the present node passes through the father node of present node to present node
Direction determines, if the direction of present node is rectilinear direction, finds jump point according to following three kinds of situations:
(1) if present node left back can not walk and left can be walked, i.e., left is to force neighbours, then along a present node left side
The not jump point in closedlist list is found in front and left；
(2) it if present node can be walked when front direction, is not arranged in closedlist along the current direction finding of present node
Jump point in table；
(3) if present node right back can not walk and right can be walked, i.e., right is to force neighbours, then along the present node right side
The not jump point in closedlist list is found in front and right；
If present node is diagonal when front direction, jump point is found according to following three kinds of situations:
(1) if present node can be walked when the horizontal component of front direction, the horizontal component of front direction is worked as along present node
Find the not jump point in closedlist list；
(2) if present node can be walked when front direction, along the current direction finding of present node not closedlist's
Jump point；
(3) if present node can be walked when the vertical component of front direction, the vertical component of front direction is worked as along present node
It finds not in the jump point of closedlist；
A4, jump point is judged whether in openlist list, if so, step A5 is executed, if it is not, jump point is added
Step A5 is executed after in openlist list；
The father node of A5, the G value for updating jump point and jump point, if the G value of the jump point is less than the G value of present node, the jump
The father node of point is present node, which is deleted to from openlist list and be added closedlist list, while more
The F value of the new jump point and the G value of the jump point, otherwise, return step A2 continues with A star algorithm and finds F in openlist list
It is worth the smallest point, as new present node, repeats step A2~A5；
A6, the path finally cooked up is exported, the path is according to present node set in closedlist list
The dog leg path of output；
A7, the dog leg path is optimized, obtains final path.
Further, the step A1 includes the following steps:
A11, the cartographic information for reading rasterizing, setting beginning and end；
A12, by the step A11 starting point obtained and along eight directions of the starting point reachable neighbor node be added openlist column
In table, the beginning and end that step A11 is obtained is added in closedlist list.
Further, the step A11 includes the following steps:
A111, the current position coordinates of ship are obtained according to the sensing module of ship as origination data, according to current boat
Row task obtains the position coordinates of target point as terminal；
A112, the map environment for reading rasterizing, the obstacle article coordinate in map environment is stored into barrier list,
And map environment is divided into area of feasible solutions and infeasible region；
A113, initialization openlist list and closedlist list, the openlist list is for storing wait sentence
It stops navigation waypoint, the closedlist list has judged way point for storing；
A114, judge origination data and terminal whether in same connected region: if not existing, illustrating between origination data and terminal
Without feasible route, return step A111 resets acquisition starting point, if using origination data as starting point, continuing to execute step
Rapid A12.
Further, in the step A114, judge whether are origination data and terminal using breadth-first search algorithm
In same connected region, specifically: origination data and the grid of coupled logical all areas of feasible solutions are subjected to Unified number, it will
Terminal and the grid of coupled logical all areas of feasible solutions carry out Unified number, if the area of feasible solutions of the two has intersection, he
Number will be consistent, illustrate there is feasible route；If the area of feasible solutions of the two is without intersection, their number will be different, explanation
Without feasible route.
Further, the forced neighbours are defined as follows: if node a is the neighbours of node b, and the neighbour of node a
There is barrier between two parties, and more any from node b's than other to the path length of node a again from the father node of node b to node b
Father node is to node a and short without the path of node b, then node a is the forced neighbours of node b, and node b is the jump of node a
Point.
Further, the jump point is defined as follows:
If node y is beginning or end, node y is jump point；
If node y has neighbours and is to force neighbours, node y is jump point；
If being that diagonal line is mobile, and node y is moved by horizontal or vertical direction from the father node of node y to node y
Dynamic to reach jump point, then node y is jump point.
Further, it if rectilinear direction and diagonal can move, searches for jump in rectilinear direction first
Point, then jump point is searched in diagonal.
Further, it is optimized in the step A7 using second order Bezier doubling thread path.
Further, in the step A2, F value indicates the total cost in path, F=G+H；
Wherein, G indicates that origination data is expended to the path of present node, and H indicates that present node does not consider to hinder to terminal
The path of the linear distance of object is hindered to be expended.
(3) beneficial effect
A kind of unmanned boat path planning method based on depth optimization proposed by the present invention, is located in advance by jump point chess game optimization
The speed of algorithm path searching is greatly optimized in adjustment method.Jump point chess game optimization Preprocessing Algorithm is node technology of prunning branches, is different from
The strategy for directly acquiring the reachable neighbor node of all non-closings of present node in A star algorithm to be expanded, jump point search for root
Direction according to present node and the strategy based on jump point (following two definition, three rules) extend descendant node, therefore big
Amplitude reduces the quantity of point spread, accelerates the speed of pathfinding.
It is different from traditional jump point searching algorithm, it joined node beta pruning optimization in jump point chess game optimization Preprocessing Algorithm
And unreachable two o'clock the methods of judges in advance, the execution efficiency of traditional A star algorithm can be improved multiple orders of magnitude, Ke Yijie
Most running memories are saved, the speed of service is accelerated, improve operational efficiency, and can apply in navigation environment complicated and changeable
Dynamic route planning in.
Finally, optimized with the path that second order Bezier exports A star, can with the path at breakeven point,
It is more advantageous to the Track control of ship.Depth optimization is carried out to Grid Method Path Planning by this method, is significantly being mentioned
On the basis of rising algorithm execution efficiency, cooking up can be with the most short and smooth ship trajectory of avoiding obstacles.Meanwhile the algorithm
The navigation route cooked up is highly-safe, high reliablity and the maximum economization that can guarantee navigation.
In addition, by carrying out proper treatment to the algorithm path planning in any scene can be applied, such as nobody
It vehicle/machine trajectory planning etc. and is attained by and plans same effect with ship track.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the applicating flow chart of the method for the present invention；
Fig. 3 is A star algorithm simulation result diagram in emulation experiment；
Fig. 4 is the result figure of traditional jump point searching algorithm in emulation experiment；
Fig. 5 is the result figure of the A star algorithm based on depth optimization in emulation experiment.
Specific embodiment
In order to preferably explain the present invention, in order to understand, with reference to the accompanying drawing, by specific embodiment, to this hair
It is bright to be described in detail.
The present invention introduces application of the invention to preferably illustrate by taking A star algorithm as an example, provides a kind of excellent based on depth
The unmanned boat path planning method of change, as shown in Figure 1, specifically comprising the following steps:
A1, based on A star algorithm obtain rasterizing map beginning and end, initialization openlist list and
Closedlist list.It is specific as follows:
A11, the cartographic information for reading rasterizing, setting beginning and end；
A12, by the step A11 starting point obtained and along eight directions of the starting point reachable neighbor node be added openlist column
In table, the beginning and end that step A11 is obtained is added in closedlist list.
Further, the step A11 includes the following steps:
A111, the current position coordinates of ship are obtained according to the sensing module of ship as origination data, according to current boat
Row task obtains the position coordinates of target point as terminal；
A112, the map environment for reading rasterizing, the obstacle article coordinate in map environment is stored into barrier list,
And map environment is divided into area of feasible solutions and infeasible region；
A113, initialization openlist list and closedlist list, the openlist list is for storing wait sentence
It stops navigation waypoint, the closedlist list has judged way point for storing；
Wherein, the node in openlist list needs further to explore extension, after the node in closedlist list
It is continuous to be no longer extended；
A114, judge origination data and terminal whether in same connected region: if not existing, illustrating between origination data and terminal
Without feasible route, return step A111 resets acquisition starting point, if using origination data as starting point, continuing to execute step
Rapid A12.
In traditional pathfinding algorithm, if without feasible route between start-stop point, pathfinding algorithm still can spend the time to look for
Path between stop, and the time that pathfinding is spent in the case of failure is much larger than the time that pathfinding is spent under successful instance, because
All paths are needed to be traversed in the case of failure, just can determine that two o'clock is unreachable.Therefore it in order to avoid such case, is seeking every time
Before road, judge whether beginning and end is reachable: if beginning and end is in same connected region, beginning and end is reachable,
Otherwise unreachable.Only beginning and end is reachable, just needs pathfinding.
Preferably, judge whether origination data and terminal are connected to same using breadth-first search algorithm in the present invention
Area, specifically: origination data and the grid of coupled logical all areas of feasible solutions are subjected to Unified number, by terminal and and its
The grid for all areas of feasible solutions being connected carries out Unified number, if the area of feasible solutions of the two has intersection, their number will
Unanimously, illustrate there is feasible route；If the area of feasible solutions of the two is without intersection, their number will be different, illustrate no feasible route.
A2, traversal openlist list, find the smallest point of F value in openlist list, are set as present node.Wherein: F
=G+H, F indicate the total cost in path, and G indicates that origination data is expended to the path of present node, and H indicates present node to terminal
The linear distance for not considering barrier path expend.
Further, judge whether present node is terminal, if present node is terminal, pathfinding terminates, and executes step
A6 continues to execute present node addition closedlist list if it is not, deleting present node in openlist list
Step A3.
A3, using present node, next jump point is found based on jump point search strategy, it is specific as follows:
Judge the direction of present node, the direction of the present node passes through the father node of present node to present node
Direction determines.Wherein, father node is a upper node for present node, such as: search first is started with starting point and is met the requirements
Present node, starting point is exactly the father node of present node, then searches for next satisfactory present node with present node,
A so upper present node is exactly the father node of next present node.
If the direction of present node is rectilinear direction, jump point is found according to following three kinds of situations:
(1) if present node left back can not walk and left can be walked, i.e., left is to force neighbours, then along a present node left side
The not jump point in closedlist list is found in front and left；
(2) it if present node can be walked when front direction, is not arranged in closedlist along the current direction finding of present node
Jump point in table；
(3) if present node right back can not walk and right can be walked, i.e., right is to force neighbours, then along the present node right side
The not jump point in closedlist list is found in front and right；
If present node is diagonal when front direction, jump point is found according to following three kinds of situations:
(1) if present node is when the horizontal component of front direction can be walked, (such as present node is currently northeastward, horizontal
Component is east), then along present node when the horizontal component of front direction finds the not jump point in closedlist list；
(2) if present node can be walked when front direction, along the current direction finding of present node not closedlist's
Jump point；
(3) if present node is when the vertical component of front direction can be walked, (such as present node is currently northeastward, vertically
Component is north), then along present node when the vertical component of front direction is found not in the jump point of closedlist.
During above-mentioned searching jump point, it then follows " two definition, three rules ":
One is defined, forces neighbours: if node a is the neighbours of node b, and has barrier (can not in the neighbours of node a
Row region), and from the father node of node b (a upper node for the node) to node b again to the path length of node a than it
His any father node from node b is to section a and short without the path of node b, then node a is the forced neighbours of node b, node
B is the jump point of node a.
Two are defined, jump point: if node y is beginning or end, node y is jump point；
If node y has neighbours and is to force neighbours, node y is jump point；
If being that diagonal line is mobile, and node y is moved by horizontal or vertical direction from the father node of node y to node y
Dynamic to reach jump point, then node y is jump point.
Rule one: during searching for jump point, if rectilinear direction and diagonal can move, first
Jump point is searched in rectilinear direction, then searches for jump point in diagonal.
Rule two: if being linear movement from the father node of node a to node a, node b is the neighbours of node a, if having from
The father node of node a is less than or equal to from the father node of node a without node a and path length to the path of node b and passes through
Node a is to the path of node b, then next point will not go to node b after going to node a；
If being that diagonal line is mobile from the father node of node a to node a, node b is the neighbours of node a, if having from node b
Father node to the path of node a without node a and path length is less than from the father node of node a and passes through node a to node b
Path, then next point will not go to node b after going to node a.
Rule three: openlist list can be just added in only jump point, because jump point can change direction of travel, rather than jump point is not
Direction of travel can be changed, finally searching out the path point come also can be only the subset of jump point set.
It, can be along searching present node in eight directions of present node not in closedlist in traditional A star algorithm
In reachable neighbor node, and the jump point search in the present invention only explores effective node based on search rule, significantly cuts down superfluous
Remaining node improves algorithm execution efficiency, and different from traditional jump point searching algorithm, by optimizing to search rule (with reference to fixed
Two) justice, further carries out beta pruning to intermediate jump point, so that the node of redundancy be avoided to expand operation, the execution efficiency of algorithm is mentioned
Rise an order of magnitude.
A4, jump point is judged whether in openlist list, if so, step A5 is executed, if it is not, jump point is added
Step A5 is executed after in openlist list；
The father node of A5, the G value for updating jump point and jump point, if the G value of the jump point is less than the G value of present node, the jump
The father node of point is present node, which is deleted to from openlist list and be added closedlist list, while more
The F value of the new jump point and the G value of the jump point, otherwise, return step A2 continues with A star algorithm and finds F in openlist list
It is worth the smallest point, as new present node, repeats step A2~A5；
A6, the path finally cooked up is exported, the path is according to present node set in closedlist list
The dog leg path of output；
A7, the optimization of second order Bezier is carried out to the dog leg path of step A6 output, tends to the path at turning point
Smoothly.In this way, can reduce control difficulty of the ship at turning point in actual motion, be conducive to its Track control.
In conclusion the unmanned boat path planning method proposed by the present invention based on depth optimization, is searched for excellent by jump point
Change Preprocessing Algorithm, the speed of algorithm path searching is greatly optimized.Jump point chess game optimization Preprocessing Algorithm is node technology of prunning branches,
The strategy expanded different from directly acquiring the reachable neighbor node of all non-closings of present node in A star algorithm, jump point
Direction of the search according to the present node and strategy based on jump point (following two definition, three rules) extends descendant node,
Therefore the quantity for drastically reducing point spread, accelerates the speed of pathfinding.
It is different from traditional jump point searching algorithm, it joined node beta pruning optimization in jump point chess game optimization Preprocessing Algorithm
And unreachable two o'clock the methods of judges in advance, the execution efficiency of traditional A star algorithm can be improved multiple orders of magnitude, Ke Yijie
Most running memories are saved, the speed of service is accelerated, improve operational efficiency, and can apply in navigation environment complicated and changeable
Dynamic route planning in.
Path Planning based on Grid Method is there are many method of point spread, in addition to the A star algorithm mentioned of the present invention,
There are also Dijkstra path planning algorithms, best-first search (Best-FirstSearch), depth-first search (Depth
First Search), breadth-first search algorithm (also known as breadth first search Breadth First Search) etc. be based on net
Lattice seek path planning algorithm a little, the correlation technique provided through the invention, and depth optimization, tool can be carried out to above-mentioned algorithm
Body step such as Fig. 2:
When carrying out trajectory planning by above-mentioned algorithm, judge between start-stop point using the present invention whether there is or not feasible route first,
Then route searching is carried out using respective algorithms principle, wherein exploring the node in former algorithm when exploring next node
Method is changed to the exploration of the node based on jump point search strategy, finally carries out the optimization of second order Bezier to the path of output,
So that route is smooth, that is, complete the depth optimization of respective algorithms.
Emulation experiment:
As shown in Fig. 3~Fig. 5, respectively A star algorithm simulation result, traditional jump point search result and based on depth optimization
A star algorithm simulation result, in which: grey grid represents barrier, and Dark grey grid is path beginning and end, light grey grid
For the path node that algorithm search arrives, black line segment is the final path that algorithmic rule goes out.
As shown in table 1, it is compared for the simulation result of above-mentioned three kinds of methods.
1 three kinds of method simulation result comparisons of table
| Algorithm | A star algorithm | Traditional jump point searching algorithm | The A star algorithm of depth optimization |
| Expanding node number | 98 | 10 | 4 |
| Simulation time (ms) | 2.8670 | 1.4983 | 0.3658 |
| Path length | 15.31 | 15.31 | 15.31 |
In conjunction with table 1 and Fig. 3~Fig. 5 it is found that the A star algorithm based on depth optimization during track search after less
Jump point greatly improves search efficiency.
The technical principle of the invention is described above in combination with a specific embodiment, these descriptions are intended merely to explain of the invention
Principle shall not be construed in any way as a limitation of the scope of protection of the invention.Based on explaining herein, those skilled in the art
It can associate with other specific embodiments of the invention without creative labor, these modes fall within this hair
Within bright protection scope.
Claims (9)
1. a kind of unmanned boat path planning method based on depth optimization, which comprises the steps of:
A1, the beginning and end that rasterizing map is obtained based on A star algorithm, initialize openlist list and closedlist column
Table；
A2, traversal openlist list, find the smallest point of F value in openlist list, are set as present node；
Judge whether present node is terminal, if present node is terminal, pathfinding terminates, step A6 is executed, if it is not,
Present node is deleted in openlist list, present node addition closedlist list is continued to execute into step A3；
A3, using present node, next jump point is found based on jump point search strategy, it is specific as follows:
Judge the direction of present node, the direction of the present node passes through the father node of present node to the direction of present node
It determines, if the direction of present node is rectilinear direction, finds jump point according to following three kinds of situations:
(1) if present node left back can not walk and left can be walked, i.e., left is to force neighbours, then along present node left front
The not jump point in closedlist list is found with left；
(2) if present node can be walked when front direction, along the current direction finding of present node not in closedlist list
Jump point；
(3) if present node right back can not walk and right can be walked, i.e., right is to force neighbours, then along present node right front
The not jump point in closedlist list is found with right；
If present node is diagonal when front direction, jump point is found according to following three kinds of situations:
(1) if present node can be walked when the horizontal component of front direction, along present node when the horizontal component of front direction is found
The not jump point in closedlist list；
(2) if present node can be walked when front direction, along the current direction finding of present node not in the jump point of closedlist；
(3) if present node can be walked when the vertical component of front direction, along present node when the vertical component of front direction is found
Not in the jump point of closedlist；
A4, jump point is judged whether in openlist list, if so, step A5 is executed, if it is not, openlist is added in jump point
Step A5 is executed after in list；
The father node of A5, the G value for updating jump point and jump point, if the G value of the jump point is less than the G value of present node, the jump point
Father node is present node, which is deleted to from openlist list and be added closedlist list, while updating should
The G value of the F value of jump point and the jump point, otherwise, return step A2 continues with A star algorithm and finds in openlist list F value most
Small point repeats step A2~A5 as new present node；
A6, the path finally cooked up is exported, the path is exported according to present node set in closedlist list
Dog leg path；
A7, the dog leg path is optimized, obtains final path.
2. the unmanned boat path planning method according to claim 1 based on depth optimization, which is characterized in that the step
A1 includes the following steps:
A11, the cartographic information for reading rasterizing, setting beginning and end；
A12, by the step A11 starting point obtained and along eight directions of the starting point reachable neighbor node be added openlist list
In, the beginning and end that step A11 is obtained is added in closedlist list.
3. the unmanned boat path planning method according to claim 2 based on depth optimization, which is characterized in that the step
A11 includes the following steps:
A111, the current position coordinates of ship are obtained according to the sensing module of ship as origination data, appointed according to current flight
Business obtains the position coordinates of target point as terminal；
A112, the map environment for reading rasterizing, the obstacle article coordinate in map environment is stored into barrier list, and will
Map environment is divided into area of feasible solutions and infeasible region；
A113, initialization openlist list and closedlist list, the openlist list is for storing wait judge to navigate
Waypoint, the closedlist list have judged way point for storing；
A114, whether judge origination data and terminal in same connected region: if not existing, illustrating can not between origination data and terminal
Walking along the street line, return step A111 reset acquisition starting point, if using origination data as starting point, continuing to execute step
A12。
4. the unmanned boat path planning method according to claim 3 based on depth optimization, which is characterized in that in the step
In rapid A114, using breadth-first search algorithm judge origination data and terminal whether in same connected region, specifically: will be original
The grid of starting point and coupled logical all areas of feasible solutions carries out Unified number, by terminal and coupled logical all feasible
The grid in region carries out Unified number, if the area of feasible solutions of the two has intersection, their number will be consistent, and illustrating to have can walking along the street
Line；If the area of feasible solutions of the two is without intersection, their number will be different, illustrate no feasible route.
5. the unmanned boat path planning method according to claim 4 based on depth optimization, which is characterized in that described to force
Neighbours are defined as follows: if node a is the neighbours of node b, and having barrier in the neighbours of node a, and from node b's
Father node arrives the path length of node a than other any father nodes from node b to node a and without node b to node b again
Path it is short, then node a be node b forced neighbours, node b be node a jump point.
6. the unmanned boat path planning method according to claim 4 based on depth optimization, which is characterized in that the jump point
It is defined as follows:
If node y is beginning or end, node y is jump point；
If node y has neighbours and is to force neighbours, node y is jump point；
If being that diagonal line is mobile, and node y can by horizontal or vertical direction movement from the father node of node y to node y
To reach jump point, then node y is jump point.
7. the unmanned boat path planning method according to claim 1 based on depth optimization, which is characterized in that if straight line
Direction and diagonal can move, then search for jump point in rectilinear direction first, then search for jump point in diagonal.
8. the unmanned boat path planning method according to claim 1 based on depth optimization, which is characterized in that the step
It is optimized in A7 using second order Bezier doubling thread path.
9. the unmanned boat path planning method according to claim 3 based on depth optimization, which is characterized in that the step
In A2, F value indicates the total cost in path, F=G+H；
Wherein, G indicates that origination data is expended to the path of present node, and H indicates that present node does not consider barrier to terminal
Linear distance path expend.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN111189453A (en) * | 2020-01-07 | 2020-05-22 | 深圳南方德尔汽车电子有限公司 | Bezier-based global path planning method and device, computer equipment and storage medium |
| CN111811514A (en) * | 2020-07-03 | 2020-10-23 | 大连海事大学 | Path planning method based on regular hexagon grid jumping point search algorithm |
| CN111879324A (en) * | 2020-06-29 | 2020-11-03 | 智慧航海(青岛)智能系统工程有限公司 | Path planning method and device based on ship angular speed limitation |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN112362067A (en) * | 2020-11-25 | 2021-02-12 | 江苏恒澄交科信息科技股份有限公司 | Autonomous route planning method for inland river intelligent ship |
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| CN116753970A (en) * | 2023-03-06 | 2023-09-15 | 北京京东乾石科技有限公司 | Path planning method, path planning device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110967015B (en) * | 2019-11-20 | 2021-11-12 | 中国人民解放军国防科技大学 | Path planning method and system |
| CN110975288A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Geometric container data compression method and system based on jumping point path search |
| CN110975288B (en) * | 2019-11-20 | 2023-08-29 | 中国人民解放军国防科技大学 | Geometric container data compression method and system based on jump point path search |
| CN110967015A (en) * | 2019-11-20 | 2020-04-07 | 中国人民解放军国防科技大学 | Path planning method and system |
| CN111189453A (en) * | 2020-01-07 | 2020-05-22 | 深圳南方德尔汽车电子有限公司 | Bezier-based global path planning method and device, computer equipment and storage medium |
| CN111879324A (en) * | 2020-06-29 | 2020-11-03 | 智慧航海(青岛)智能系统工程有限公司 | Path planning method and device based on ship angular speed limitation |
| CN111811514A (en) * | 2020-07-03 | 2020-10-23 | 大连海事大学 | Path planning method based on regular hexagon grid jumping point search algorithm |
| CN111811514B (en) * | 2020-07-03 | 2023-06-09 | 大连海事大学 | Path planning method based on regular hexagon grid jump point search algorithm |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN112229419B (en) * | 2020-09-30 | 2023-02-17 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN112419779A (en) * | 2020-11-09 | 2021-02-26 | 北京京东乾石科技有限公司 | Selection method and device of unmanned vehicle stop point, storage medium and electronic equipment |
| CN112419779B (en) * | 2020-11-09 | 2022-04-12 | 北京京东乾石科技有限公司 | Selection method and device of unmanned vehicle stop point, storage medium and electronic equipment |
| CN112362067A (en) * | 2020-11-25 | 2021-02-12 | 江苏恒澄交科信息科技股份有限公司 | Autonomous route planning method for inland river intelligent ship |
| CN112362067B (en) * | 2020-11-25 | 2022-09-30 | 江苏恒澄交科信息科技股份有限公司 | Autonomous route planning method for inland river intelligent ship |
| CN113607181A (en) * | 2021-08-05 | 2021-11-05 | 国网上海市电力公司 | Optimization method of jumping point search algorithm |
| CN113827973A (en) * | 2021-09-27 | 2021-12-24 | 网易（杭州）网络有限公司 | Map path finding method, device, terminal and storage medium |
| CN114047767B (en) * | 2021-11-24 | 2023-08-08 | 山东建筑大学 | Robot jump point searching method based on artificial potential field method |
| CN114047767A (en) * | 2021-11-24 | 2022-02-15 | 山东建筑大学 | Robot jumping point searching algorithm based on artificial potential field method |
| CN114564023B (en) * | 2022-03-11 | 2022-11-08 | 哈尔滨理工大学 | Jumping point search path planning method under dynamic scene |
| CN114564023A (en) * | 2022-03-11 | 2022-05-31 | 哈尔滨理工大学 | Jumping point search path planning method under dynamic scene |
| CN116753970A (en) * | 2023-03-06 | 2023-09-15 | 北京京东乾石科技有限公司 | Path planning method, path planning device, electronic equipment and storage medium |
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