CN108444490A - Robot path planning method based on Visual Graph and A* algorithm depth integrations - Google Patents
Robot path planning method based on Visual Graph and A* algorithm depth integrations Download PDFInfo
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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
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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Abstract
The present invention discloses a kind of robot path planning method based on Visual Graph and A* algorithm depth integrations, includes the following steps：（1）It determines starting point s and target point g, map environment is built using computer scanning；（2）Each node and default settings are initialized, current accessed node current is defined；（3）It connects current accessed node current and target point g and constructs ruler, relevant judgement is carried out to ruler；（4）Node listing to be visited is added in satisfactory node, Utilization assessment function F (n) is evaluated and tested, and the node n* of F value minimums is taken out；（5）It connects current accessed node current and carries out relevant judgement with node n* construction rulers；（6）Robot path is built by way of following father node.The method of the present invention is simple, regular simplification, fast response time, operation efficiency are high.
Description
Technical field
The present invention relates to a kind of robot path planning methods, more particularly relate to a kind of deep based on Visual Graph and A* algorithms
Spend the robot path planning method of fusion.
Background technology
In robot path planning method, the combination of traditional visual nomography and searching algorithm is a kind of Global motion planning
Algorithm is to find optimal path after environmental map is built complete model, then with searching algorithm.However, this method
It has the following defects：(1) as the complexity of environment increases, the speed of service and efficiency substantially reduce；(2) this method is often neglected
The Lve Liao robots structure of itself often causes the collision of robot and barrier in practice.
Currently, the improvement to this kind of method mainly simplifies Visual Graph, the quantity of node and visible light is reduced,
Searching algorithm is used afterwards.But improved method has compared with strong dependency environment, and environmental change is caused such as emergency case,
It needs all to model again, then carries out route searching, the method adaptive ability is poor, control response is slow low with operation efficiency.
Invention content
Goal of the invention：It is a kind of based on Visual Graph and A* algorithms present invention aims in view of the deficiencies of the prior art, providing
The robot path planning method of depth integration, adaptive ability is strong, and control response is fast, and operation efficiency is high.
Technical solution：A kind of robot path planning side based on Visual Graph and A* algorithm depth integrations of the present invention
The border width of method, the robot running gear is L, which is characterized in that is carried out as follows：
S1, the starting point s and target point g for determining robot, utilize the map involved by computer scanning robot motion
Environment identifies all barriers in map environment and carries out envelope by closed polygon, and it is (L/ to calculate its parallel envelope
2) * 1.1, retain figure；
Each node in S2, initialization map environment, default settings, and to accessing beginning node valuation, initialization function and beginning node
Assignment function, it is as follows：(1) G (n)=inf inity F (n)=inf inity, (2)
G (s)=0, (4) F (s)=H (s), (5) current=s, wherein G (n) indicates the distance between node n and starting point s,
Infinity indicates infinitely great；H (n) is heuristic function, and H (n) indicates the estimation of the distance between node n and target point g；F(n)
For evaluation function, F (n)=G (n)+H (n), F (n) are indicated from starting point s via the estimation of distance node n to target point g；
If S3, min (F) value is not infinity, whole path planning is carried out using global path planning algorithm.
The technical solution of the present invention is further defined as carrying out whole path using global path planning algorithm in step S3
When planning, the specific steps are：
If the value of the evaluation function F (n) of Q1, current accessed node current are not infinity, current accessed section is constructed
Point current, that is, pass through line segment current-g between starting point s and target point g, passes through line segment and intersects (N with N number of barrier
For the integer more than 0), take with first barrier for passing through line segment current-g and intersecting, in the ruler both sides,
Node farthest apart from ruler on the parallel envelope of the barrier is respectively taken, map environment boundary is seen if fall out, if so,
Then deletion of node；If it is not, then node listing to be visited, and the G values of more new node, F values and father node is added in the node
parent；
Whether Q2, N are more than 1, if not, executing step Q3；If it is, continuing to each barrier, in ruler
Both sides, respectively take from ruler more farthest, altogether (N-1) * 2, is named as Ai (i=1...2* (N-1)), connects sAi and judges
Whether intersect with barrier, if so, executing step Q3；If it is not, whether decision node exceeds processing boundary, if so, deletion of node；
Node is added to node to be visited if not, updates its G value, F values and father node parent；
Q3, Utilization assessment function F (n) test and assess to all nodes to be visited, and it is n* to take out minimum node；Connection is worked as
Preceding accessed node current and node n* as passing through line segment current-n*, if pass through line segment current-n* not with barrier
Hinder object to intersect, then node n* is updated to current accessed node current, node n* is removed into node listing to be visited, after
It is continuous to execute step Q1, until traversal completes node listing to be visited；
Optimal path between Q4, structure starting point s and target point g is found by following father node from target point g
Starting point s is to obtain planning path.
Further, in step Q1, if N=0, the ruler between starting point s and target point g is planning path.
Further, in step Q3, intersect with barrier if passing through line segment current-n*,：If passing through line segment
Current-n* intersects with N number of barrier, then n* is removed node to be visited, and first on line taking section current to the directions n*
Two farthest points of a both sides obstacle distance line segment current-n* are as node, by line segment current to the directions n* the
The G values, F values and father node of node listing to be visited and more new node is added in two nodes of one barrier；
By current accessed node current respectively with the 2-N obstacle distance line segment on line segment current to the directions n*
The farthest two points connection in the both sides current-n*, constitutes 2* (N-1) line segment, judge 2* (N-1) line segment whether with line segment
Barrier intersection on current to the directions n*；If all intersections, go to step Q3；If non-intersecting, sentence
Whether the node of the 2-N barrier exceeds the physical boundary of map environment on broken string section current to the directions n*；If beyond ground
The physical boundary of figure environment, then deletion of node；If without departing from the physical boundary of map environment, respective nodes are added to be visited
G values, F values and the father node of node listing and more new node, then the Q3 that gos to step.
Further, the G values, F values of more new node n and the function of father node are：
Evaluation function F (n)=H (n)+G (n), whereinxnAnd ynIt indicates respectively
The abscissa and ordinate of node n, xgAnd ygThe abscissa and ordinate of target point g are indicated respectively；If G (n) ＞ G
(current)+dis tan ce (current to n), then G (n)=G (current)+dis tan ce (currentto
N), G (current) indicates that the G values of current accessed node current, distance (current to n) indicate current accessed
The distance between node current to node n；The father node n.par ent=curr ent of node n.
Advantageous effect：The present invention introduces heuristic function H (n) while generating necessary Visual Graph by the way that rule is arranged and searches
Rope simplifies its rule, Visual Graph and A* algorithm depth integrations scans for and constantly update to wait visiting among the process for building figure
Ask node, improve the response speed of robot path planning, the node that greatly needs of reduction access, enhance its it is adaptive should be able to
Power and intelligence degree；The present invention dynamically establishes path+scan for according to starting point, the foundation of Visual Graph be it is dynamic,
Environment change influences it less；The present invention simplifies Visual Graph, deletes unnecessary section by the way that judgment rule is arranged
Point improves robot searches efficiency；The present invention by the parallel envelope of object of placing obstacles, avoid in robot kinematics with barrier
Hinder the collision of object, reduce collision probability, enhances reliability.
Description of the drawings
Fig. 1 is the parallel envelope schematic diagram of barrier in the present invention；
Fig. 2 is the first Visual Graph of the present invention；
Fig. 3 is the second Visual Graph of the present invention；
Fig. 4 is the third Visual Graph of the present invention；
Fig. 5 is the 4th Visual Graph of the present invention；
Fig. 6 is the 5th Visual Graph of the present invention；
Fig. 7 is the 6th Visual Graph of the present invention.
Specific implementation mode
Technical solution of the present invention is described in detail below by attached drawing, but protection scope of the present invention is not limited to
The embodiment.
Embodiment 1：A kind of robot path planning method based on Visual Graph and A* algorithm depth integrations uses complete first
Office's path planning algorithm carries out the calculating in whole path, if there are path, robot lead-in path and according to planning path before
Into until reach home, otherwise terminating.
The four-wheel drive structure that the installation of robot ambulation module uses, the mechanical arm module are to be installed in robot row
It walks on module, is tandem sixdegree-of-freedom simulation, robot marginal texture depends on ground-engaging element, width L.This reality
It applies in example, it is as follows that specific operating procedure is described in detail by the map in attached drawing 2~7：
S1, the starting point s and target point g for determining robot, utilize the map involved by computer scanning robot motion
Environment identifies all barriers in map environment and carries out envelope by closed polygon, and it is (L/ to calculate its parallel envelope
2) * 1.1, retain figure；The parallel envelope schematic diagram of barrier is as shown in Figure 1.
Each node in S2, initialization map environment, default settings, and to accessing beginning node valuation, initialization function and beginning node
Assignment function, it is as follows：(1) G (n)=inf inity F (n)=inf inity, (2)
G (s)=0, (4) F (s)=H (s), (5) current=s, wherein G (n) indicates the distance between node n and starting point s,
Infinity indicates infinitely great；H (n) is heuristic function, and H (n) indicates the estimation of the distance between node n and target point g；F(n)
For evaluation function, F (n)=G (n)+H (n), F (n) are indicated from starting point s via the estimation of distance node n to target point g；
Current current accessed nodes.
If S3, min (F) value is infinity, report path is sky, terminates path planning.
If min (F) value is not infinity, whole path planning is carried out using global path planning algorithm.
When global path planning algorithm carries out whole path planning, the specific steps are：
If the value of the evaluation function F (n) of Q1, current accessed node current are not infinity, current accessed section is constructed
Point current, that is, pass through line segment current-g between starting point s and target point g.
It passes through line segment with N number of barrier to intersect, if N=0, the ruler between starting point s and target point g is planning
Path.If (N be integer) more than 0, take with first barrier for passing through line segment current-g and intersecting, described
Ruler both sides respectively take node farthest apart from ruler on the parallel envelope of the barrier, see if fall out map environment
Boundary, if it is, deletion of node；If it is not, then by node addition node listing to be visited, and the G values of more new node,
F values and father node parent.
In the present embodiment, passes through line segment current-g and intersect with barrier, i.e. line segment current-g and N number of barrier phase
It hands over (N is more than 0 integer), in the present embodiment, N=3 passes through line segment and intersects with 3 barriers, is 2,3,4 respectively, passing through
Line both sides, each barrier respectively take from ruler more farthest, are B1, B2, C1, C2, D1, D2.
It takes and is respectively taken described in the ruler both sides with first barrier for passing through line segment current-g and intersecting
The node farthest apart from ruler (B1, B2), sees if fall out map environment boundary on the parallel envelope of barrier, if so,
Then deletion of node；If it is not, then node listing to be visited is added in the node, as a result two node B1、B2Node to be visited is added
List, and the G values of more new node, F values and father node parent.Connect sC1, sC2, sD1, sD2, judge four connecting lines whether with
Barrier intersects, as a result all intersections, as shown in Figure 2.
G (n)=infinityF (n)=inf inity
The F (s) of G (s)=0=H (s)
Current=s
At this point, judging whether N is more than 1, if N is not more than 1, Q3 is jumped to, if N is more than 1, thens follow the steps Q2,
In the present embodiment, therefore N=3 continues to execute step Q2.
Whether Q2, N are more than 1, if not, executing step Q3；If it is, continuing to each barrier, in ruler
Both sides, respectively take from ruler more farthest, altogether (N-1) * 2, is named as Ai (i=1...2* (N-1)), connects sAi and judges
Whether intersect with barrier, if so, executing step Q3；If it is not, whether decision node exceeds processing boundary, if so, deletion of node；
Node is added to node to be visited if not, updates its G value, F values and father node parent；
Q3, Utilization assessment function F (n) test and assess to all nodes to be visited, and it is n* to take out minimum node, is commented
The node n*=B of valence functional value minimum1.Connection current accessed node current is used as with node n* and passes through line segment sB1, such as Fig. 3
It is shown.
Judge whether ruler intersects with barrier, do not intersect with barrier if passing through line segment current-n*,
Node n* is updated to current accessed node current, node n* is removed into node listing to be visited, current=n*.Continue
Step Q1 is executed, until traversal completes node listing to be visited.
If passing through line segment current-n* with barrier to intersect, the present embodiment intersects with barrier 1, then waits for n* removals
Accessed node, and two farthest points of the both sides obstacle distance line segment current-n* are made on line taking section current to the directions n*
For node, two nodes of barrier on line segment current to the directions n* are added to the G of node listing to be visited and more new node
Value, F values and father node.N*=B1 is removed into node listing to be visited, for barrier 1, takes the farthest node in ruler both sides
A1, A2 are added in node listing to be visited, G values, F values and the father node parent of more new node.
In the present embodiment, step Q1, result n*=A2 are continued to execute.It connects current and constructs ruler for n*, i.e.,
SA2, such as Fig. 4, judge whether ruler intersects with barrier, and result is no, is current accessed node by node updates and removes
Node listing to be visited：Current=n*, as A2, and redirect Q1.Connecting node current and terminal g, i.e. connection source A2
With terminal g, such as Fig. 5, construction ruler A2G, as a result B1 additions node listing to be visited, and B1.parent=A2；Repeat step
Q3：As a result n*=B1；Connect A2And B1, such as Fig. 6, as a result current=B1, and jump procedure 3；Ruler B1G and barrier 3,4
Intersect, judged C1、C2、D1Node listing to be visited is added and updates each value；Step Q3 is repeated, judges current=judged
C1Thereafter jump procedure 3 repeat the above action.
By current accessed node current respectively with the 2-N obstacle distance line segment on line segment current to the directions n*
The farthest two points connection in the both sides current-n*, constitutes 2* (N-1) line segment, judge 2* (N-1) line segment whether with line segment
First barrier intersection on current to the directions n*；If all intersections, go to step Q3；If non-intersecting,
So judge whether the node of the 2-N barrier on line segment current to the directions n* exceeds the physical boundary of map environment；If
Physical boundary beyond map environment, then deletion of node；If without departing from the physical boundary of map environment, respective nodes are added
G values, F values and the father node of node listing to be visited and more new node, then the Q3 that gos to step.
Q4, g.parent=current build the optimal path between starting point s and target point g, by following father to save
Point finds starting point s to obtain planning path from target point g.
In above-mentioned steps, the function of the G values of more new node n, F values and father node is：
Evaluation function F (n)=H (n)+G (n), whereinxnAnd ynIt indicates respectively
The abscissa and ordinate of node n, xgAnd ygThe abscissa and ordinate of target point g are indicated respectively；If G (n) ＞ G
(current)+dis tan ce (currentto n), then G (n)=G (current)+dis tan ce (currentto n),
G (current) indicates that the G values of current accessed node current, distance (current to n) indicate current accessed node
The distance between current to node n；The father node n.parent=current of node n.
Fig. 2~Fig. 6, heavy line represent ruler, and dotted line represents assistant connection wire, final by continuous iteration
G.parent=D2, at this time since g nodes, its father node constantly is shifted to from each node, until reaching starting point, to complete
At the construction in optimal most complete path, as a result such as Fig. 7.
As described above, although the present invention has been indicated and described with reference to specific preferred embodiment, must not explain
For the limitation to invention itself.It without prejudice to the spirit and scope of the invention as defined in the appended claims, can be right
Various changes can be made in the form and details for it.
Claims (5)
1. the robot path planning method based on Visual Graph and A* algorithm depth integrations, the edge of the robot running gear
Width is L, which is characterized in that is carried out as follows：
S1, the starting point s and target point g for determining robot, using the map environment involved by computer scanning robot motion,
It identifies all barriers in map environment and envelope is carried out by closed polygon, and it is (L/2) * to calculate its parallel envelope
1.1, retain figure；
Each node in S2, initialization map environment, default settings, and to accessing beginning node valuation, initialization function and beginning node valuation
Function, it is as follows：(1) G (n)=inf inity F (n)=inf inity, (2)G
(s)=0, (4) F (s)=H (s), (5) current=s, wherein G (n) indicates the distance between node n and starting point s,
Infinity indicates infinitely great；H (n) is heuristic function, and H (n) indicates the estimation of the distance between node n and target point g, x and y
The respectively abscissa and ordinate of map environment；F (n) is evaluation function, and F (n)=G (n)+H (n), F (n) are indicated from starting
Point s via distance between node n to target point g estimation；
If S3, min (F) value is not infinity, whole path planning is carried out using global path planning algorithm.
2. the robot path planning method according to claim 1 based on Visual Graph and A* algorithm depth integrations, special
Sign is, when carrying out whole path planning using global path planning algorithm in step S3, the specific steps are：
If the value of the evaluation function F (n) of Q1, current accessed node current are not infinity, current accessed node is constructed
Current, that is, pass through line segment current-g between starting point s and target point g passes through line segment and intersects that (N is with N number of barrier
Integer more than 0), take with first barrier for passing through line segment current-g and intersecting, in the ruler both sides, respectively
Node farthest apart from ruler on the parallel envelope of the barrier is taken, map environment boundary is seen if fall out, if it is,
Deletion of node；If it is not, then node listing to be visited, and the G values of more new node, F values and father node is added in the node
parent；
Whether Q2, N are more than 1, if not, executing step Q3；If it is, continue to each barrier, in ruler both sides,
Respectively take from ruler more farthest, altogether (N-1) * 2, is named as Ai (i=1...2* (N-1)), connection sAi and judgement and obstacle
Whether object intersects, if so, executing step Q3；If it is not, whether decision node exceeds processing boundary, if so, deletion of node；If not will
Node is added to node to be visited, updates its G value, F values and father node parent；
Q3, Utilization assessment function F (n) test and assess to all nodes to be visited, and it is n* to take out minimum node；Connection is current to visit
Node current and node n* is asked as passing through line segment current-n*, if pass through line segment current-n* not with barrier
Node n* is removed node listing to be visited, continues to hold by intersection then node n* is updated to current accessed node current
Row step Q1, until traversal completes node listing to be visited；
Optimal path between Q4, structure starting point s and target point g finds starting point s by following father node from target point g
To obtain planning path.
3. the robot path planning method according to claim 2 based on Visual Graph and A* algorithm depth integrations, special
Sign is, in step Q1, if N=0, the ruler between starting point s and target point g is planning path.
4. the robot path planning method according to claim 2 based on Visual Graph and A* algorithm depth integrations, special
Sign is, in step Q3, intersects with barrier if passing through line segment current-n*,：If passing through line segment current-n*
Intersect with N number of barrier, then n* removes node to be visited, and first obstacle distance on line taking section current to the directions n*
Two farthest points of the both sides line segment current-n* are as node, by first barrier on line segment current to the directions n*
The G values, F values and father node of node listing to be visited and more new node is added in two nodes；
By current accessed node current respectively with the 2-N obstacle distance line segment on line segment current to the directions n*
The farthest two points connection in the both sides current-n*, constitutes 2* (N-1) line segment, judge 2* (N-1) line segment whether with line segment
Barrier intersection on current to the directions n*；If all intersections, go to step Q3；If non-intersecting, sentence
Whether the node of the 2-N barrier exceeds the physical boundary of map environment on broken string section current to the directions n*；If beyond ground
The physical boundary of figure environment, then deletion of node；If without departing from the physical boundary of map environment, respective nodes are added to be visited
G values, F values and the father node of node listing and more new node, then the Q3 that gos to step.
5. a kind of machine based on Visual Graph and A* algorithm depth integrations according to claim 2~4 any claim
People's paths planning method, which is characterized in that the function of the G values of more new node n, F values and father node is：
Evaluation function F (n)=H (n)+G (n), whereinxnAnd ynNode is indicated respectively
The abscissa and ordinate of n, xgAnd ygThe abscissa and ordinate of target point g are indicated respectively；If G (n) ＞ G (current)+
Distance (currentton), then G (n)=G (current)+distance (currentton), G (current) expressions are worked as
The G values of preceding accessed node current, distance (current to n) indicate current accessed node current to node n it
Between distance；The father node n.parent=curr ent of node n.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109062215A (en) * | 2018-08-24 | 2018-12-21 | 北京京东尚科信息技术有限公司 | Robot and barrier-avoiding method, system, equipment and medium are followed based on its target |
| CN109976350A (en) * | 2019-04-15 | 2019-07-05 | 上海钛米机器人科技有限公司 | Multirobot dispatching method, device, server and computer readable storage medium |
| CN110296704A (en) * | 2019-06-25 | 2019-10-01 | 智慧航海（青岛）科技有限公司 | A kind of path planning method based on Visual Graph modeling |
| CN110763247A (en) * | 2019-10-21 | 2020-02-07 | 上海海事大学 | Robot path planning method based on combination of visual algorithm and greedy algorithm |
| CN111047250A (en) * | 2019-11-25 | 2020-04-21 | 中冶南方（武汉）自动化有限公司 | Planning method for running path of crown block |
| CN111324998A (en) * | 2019-12-31 | 2020-06-23 | 浙江华云信息科技有限公司 | Connecting line routing layout method based on route search |
| CN111399506A (en) * | 2020-03-13 | 2020-07-10 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraints |
| CN111721307A (en) * | 2020-01-03 | 2020-09-29 | 腾讯科技（深圳）有限公司 | Road network map generation method and related device |
| CN113934218A (en) * | 2021-11-16 | 2022-01-14 | 杭州云象商用机器有限公司 | Cleaning robot path planning method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130238815A1 (en) * | 2010-11-19 | 2013-09-12 | Nec Corporation | Path selection device, program and method |
| CN104516350A (en) * | 2013-09-26 | 2015-04-15 | 沈阳工业大学 | Mobile robot path planning method in complex environment |
| CN104914870A (en) * | 2015-07-08 | 2015-09-16 | 中南大学 | Ridge-regression-extreme-learning-machine-based local path planning method for outdoor robot |
| CN106444773A (en) * | 2016-10-25 | 2017-02-22 | 大连理工大学 | Environment modeling method based on recursive reduced visibility graph |
-
2018
- 2018-03-16 CN CN201810217992.1A patent/CN108444490B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130238815A1 (en) * | 2010-11-19 | 2013-09-12 | Nec Corporation | Path selection device, program and method |
| CN104516350A (en) * | 2013-09-26 | 2015-04-15 | 沈阳工业大学 | Mobile robot path planning method in complex environment |
| CN104914870A (en) * | 2015-07-08 | 2015-09-16 | 中南大学 | Ridge-regression-extreme-learning-machine-based local path planning method for outdoor robot |
| CN106444773A (en) * | 2016-10-25 | 2017-02-22 | 大连理工大学 | Environment modeling method based on recursive reduced visibility graph |
Non-Patent Citations (1)
| Title |
|---|
| 吕太之等: "基于同步可视图构造和A* 算法的全局路径规划", 《南京理工大学学报》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109062215A (en) * | 2018-08-24 | 2018-12-21 | 北京京东尚科信息技术有限公司 | Robot and barrier-avoiding method, system, equipment and medium are followed based on its target |
| CN109976350A (en) * | 2019-04-15 | 2019-07-05 | 上海钛米机器人科技有限公司 | Multirobot dispatching method, device, server and computer readable storage medium |
| CN110296704A (en) * | 2019-06-25 | 2019-10-01 | 智慧航海（青岛）科技有限公司 | A kind of path planning method based on Visual Graph modeling |
| CN110763247A (en) * | 2019-10-21 | 2020-02-07 | 上海海事大学 | Robot path planning method based on combination of visual algorithm and greedy algorithm |
| CN111047250A (en) * | 2019-11-25 | 2020-04-21 | 中冶南方（武汉）自动化有限公司 | Planning method for running path of crown block |
| CN111324998A (en) * | 2019-12-31 | 2020-06-23 | 浙江华云信息科技有限公司 | Connecting line routing layout method based on route search |
| CN111324998B (en) * | 2019-12-31 | 2023-09-12 | 浙江华云信息科技有限公司 | Route search-based connecting wire routing layout method |
| CN111721307B (en) * | 2020-01-03 | 2022-06-10 | 腾讯科技（深圳）有限公司 | Road network map generation method and related device |
| CN111721307A (en) * | 2020-01-03 | 2020-09-29 | 腾讯科技（深圳）有限公司 | Road network map generation method and related device |
| CN111399506A (en) * | 2020-03-13 | 2020-07-10 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraints |
| CN111399506B (en) * | 2020-03-13 | 2023-04-25 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraint |
| CN113934218B (en) * | 2021-11-16 | 2022-03-25 | 杭州云象商用机器有限公司 | Cleaning robot path planning method, device, equipment and storage medium |
| CN113934218A (en) * | 2021-11-16 | 2022-01-14 | 杭州云象商用机器有限公司 | Cleaning robot path planning method, device, equipment and storage medium |
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