CN114510045B - Robot global path planning A improvement method based on safety ring - Google Patents
Robot global path planning A improvement method based on safety ring Download PDFInfo
- Publication number
- CN114510045B CN114510045B CN202210098000.4A CN202210098000A CN114510045B CN 114510045 B CN114510045 B CN 114510045B CN 202210098000 A CN202210098000 A CN 202210098000A CN 114510045 B CN114510045 B CN 114510045B
- Authority
- CN
- China
- Prior art keywords
- point
- current
- value
- distance
- path planning
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000006872 improvement Effects 0.000 title claims description 17
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 17
- 230000009191 jumping Effects 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 abstract description 8
- 238000012545 processing Methods 0.000 abstract description 3
- 238000010845 search algorithm Methods 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 2
- 231100000518 lethal Toxicity 0.000 description 2
- 230000001665 lethal effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- 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
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
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)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a safety ring-based robot global path planning A-improvement method, and belongs to the technical field of robot control and path planning. Comprising the following steps: 1) Initializing an algorithm A, creating an open table and a close table, and adding a starting point into the open table; 2) Setting a starting point in an open table as a current searching point N; 3) Judging whether the current search point N is a target point, if so, starting from the target point, arranging the cost values F in the close table from small to large until the target point, and if not, calculating the cost values F of all points adjacent to the search point N in the map, adding all the points into the open table, and adding the current search point N into the close table. The method saves the operation amount of the map preprocessing stage, and avoids the area not related to the processing task when the map is larger or the resolution ratio is higher, thereby saving the operation resource.
Description
Technical Field
The invention relates to a safety ring-based robot global path planning A-improvement method, and belongs to the technical field of robot control and path planning.
Background
With the popularity of mobile robots in various industries, higher demands are also being made on the ability of mobile robots to find an optimal path of movement according to task demands. The path planning refers to a problem of finding an optimal route to a destination when the robot performs a navigation task, and the path planning includes a global path planning for determining an overall route using an existing map, and a local path planning for determining a short-time travel track of the robot according to real-time information. The process of global path planning requires the cooperation of a graph construction algorithm and a graph search algorithm. The graph construction algorithm comprises a visual method, a Voronoi graph, a tangent graph method, a grid decomposition method, a probability road marking algorithm, a quick random number algorithm and the like, the graph search algorithm can reconstruct a complex environment map into a simple grid or topological framework graph, and then an optimal path is found in the framework graph through the graph search algorithm, and the graph search algorithm generally comprises Dijkstra, A, D and the like, wherein the A is the most common algorithm in domestic and foreign researches.
The traditional global path planning algorithm can search the shortest path between designated points on the map, but in a mobile robot working scene, if the moving track of the robot is too close to an obstacle, the moving efficiency is easy to be reduced, and other safety problems are easy to cause. In order to solve the problem, the method optimizes the graph search algorithm A of the global path planning based on the safety ring method, so that the safety distance between the A and the obstacle is kept when the A generates the path. In the ROS, which uses more robot operating systems, the system solves this problem by inserting an expansion layer in the map preprocessing stage, in which expansion areas are formed around all obstacles, which surround the obstacles like a "cushion pad", so that the robot does not approach the obstacles, the principle is that different cost values are set according to the distance between points on the map and the obstacles, and the higher the cost value, the more prone it is to not select the points when planning the path. The path planning has the advantages that the path planning is operated in a map preprocessing stage, the planning time is not influenced, and one-time calculation can be repeatedly utilized; the disadvantage is that for maps of larger scale or higher resolution, the path planning requires a lot of time in the preprocessing stage and may waste computational resources since the areas not passed by the robot are also preprocessed.
The application aims to keep the distance between the path and the obstacle by generating the safety ring on the path generated by A, and the thinking is opposite to an expansion layer, and the distance between the path and the obstacle is directly calculated by taking the searching point in the searching process of A as the center, so that a large amount of map preprocessing calculation is avoided, and the calculation of an area which does not pass through is also avoided.
Disclosure of Invention
The invention aims to solve the problems that the travel efficiency of a robot is easily influenced or the travel safety is easily influenced when the distance between a path generated by a traditional global path planning A algorithm and an obstacle is too short, and provides a safety circle-based robot global path planning A improvement method.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
The robot global path planning A improvement method comprises the following steps:
step 1, initializing an algorithm A, creating an open table and a close table, and adding a starting point into the open table;
The open table and the close table both comprise a plurality of nodes;
Step 2, setting a starting point in an open table as a current searching point N;
Step 3, initializing i to be 1;
Step 4, judging whether the current search point N is a target point, if yes, jumping to step 6, otherwise, if not, calculating cost values F of all points adjacent to the search point N in the map, adding all points into an open table, and adding the current search point N into a close table;
Wherein, all adjacent points are K in total, and all points are marked as follows: m 1、M2…MK;
in step 4, calculating cost values F of all points adjacent to the search point N, specifically: calculating a cost value F of M i, which comprises the following substeps:
Step 4.1, calculating the shortest distance G between the point M i and the starting point;
Wherein, the calculation formula of the shortest distance G is g=g' +c; g' is the G value of the minimum point of the G values in the searched points around the point M i, and C is the cost constant of each lattice distance;
Step 4.2, calculating a distance estimated value H between the point M i and the target point;
The estimated value H of the distance between the point M i and the target point is one of Euclidean distance H 1 or Manhattan distance H 2; and H 1＝[(Nx-Dx)2+(Nx-Dx)2]1/2 and H 2＝|Nx-Dx|+|Ny-Dy;
wherein, (Nx, ny) and (Dx, dy) are the coordinates of the current search point and the target point, respectively;
Step 4.3, calculating a cost value P corresponding to the distance between the point M i and the nearest obstacle based on the safety ring, specifically including the following sub-steps:
Step 4.3.1, setting a round safety radius r around the search point as a safety ring, approximating the safety ring as a square and dividing the square into a plurality of concentric squares, taking the innermost concentric square as the innermost layer, and dividing the annular space obtained by subtracting the square on the inner side of each square into one layer to divide the square, namely the safety ring into a plurality of layers;
step 4.3.2, judging whether the search of all layers of the safety ring is finished, if not, searching the next layer, if yes, returning to the P value of 0 and entering the step 4.4, wherein no obstacle exists in the range;
the next layer is searched according to the sequence from the innermost layer of the safety ring to the outside layer by layer;
step 4.3.3, judging whether all searches of the current layer are completed, if not, searching the next edge of the current layer, and if so, returning to the step 4.3.2;
The searching of the next edge of the current layer refers to searching four edges of a square in the sequence of up, right, down and left, wherein the next edge of the current edge;
Step 4.3.4, judging whether all searches on the current edge are completed, if not, continuing to detect the next point, and if so, returning to step 4.3.3;
The step of detecting the next point is to search all points on the current edge in a clockwise direction along the current edge;
Step 4.3.5, judging whether the current point is an obstacle, if not, returning to step 4.3.4, if so, calculating a P value, and then entering step 4.4;
The calculation formula of the P value is P=I exp (-r) d, wherein I is the maximum value of P, r is the safety radius of the safety ring, and d is the distance between the current point and the nearest obstacle;
step 4.4, calculating a cost value F of the point M i;
The calculation formula of the cost value F is F=G+P+H;
H is the cost corresponding to the estimated distance from the current point to the destination node, and G is the cost corresponding to the distance from the starting point to the current point; the size of the F value is determined by whether the node is occupied by an obstacle or not and the distance between the node and the starting point and the target point, the lower the F value of the node which is closer to the starting point or the target point is, the easier the node is selected as a path, the P value is added into the cost value F, the generated path can generate a new path according to the distance between the search point and the obstacle, and if the obstacle exists in the safety ring of the search point, the search point is more likely to be selected by the A;
step 4.5, judging whether i is equal to K, if so, indicating that all K adjacent points of the current search point N are searched completely, setting i as 1, and jumping to the step 5; if not, adding 1 to i, and jumping to the step 4.1;
Step 5, selecting a node with the minimum cost value F in the current open table as a current search point N, and jumping to the step 4;
and 6, finishing searching, namely starting from the target point, arranging the values F in the close table from small to large, and obtaining the optimal path until the starting point.
Advantageous effects
Compared with the existing robot global path planning method, the robot global path planning method based on the safety ring has the following beneficial effects:
1. compared with the traditional global path planning algorithm, the method has the advantages that the problem that the running efficiency of the robot is easily reduced or the running safety is easily influenced due to the fact that the distance between the calculated path and the obstacle is too short is avoided;
2. Compared with some methods running in the map preprocessing stage, the method can save the operation amount of the map preprocessing stage, and can avoid the area not related to the processing task under the condition of larger map or higher resolution, thereby saving operation resources.
Drawings
FIG. 1 is a schematic illustration of a robot planning a path on an autonomously constructed map;
Fig. 2 is a program flow chart of a major function AStarExpansion function in example 1;
FIG. 3 is a program flow diagram of the add function in example 1;
FIG. 4 is a program flow chart of the safety loop master function safeCost function in example 1;
Fig. 5 is a schematic view of a safety ring on a grid map according to an improved method of robot global path planning a based on the safety ring of the present invention;
fig. 6 is a global path planning simulation experiment performed without using a security ring;
FIG. 7 is a single-sided obstacle detour experiment without the safety loop method;
Fig. 8 is a single-sided obstacle detour experiment using the safety loop method.
Detailed Description
The following describes in detail the implementation of a safety-ring-based robot global path planning a improvement method according to the present invention with reference to the embodiments and the accompanying drawings.
Example 1
The path planning of the mobile robot is applied in various industries, and embodiment 1 applies the present invention to a service robot scenario in which the robot is required to be able to plan a short and safe moving path according to task demands at any time in an indoor scenario such as home, office, hospital, etc. When the moving task needs to be executed, the robot needs to rely on an environment map which is built independently, obtains an overall optimal path through global path planning, determines the actual movement track of the robot through local path planning and combining sensor data, and finally controls the robot to move through a movement control system, as shown in the figure 1.
Example 1 robot control based on ROS system, the safety-circle-based algorithm improvement method presented by the present invention is used as a graph search algorithm for global path planning. Example 1 only relates to the part of the algorithm a that is modified based on the safety loop method.
The program flow chart is shown in fig. 2, and the specific implementation steps are as follows:
Step 1, initializing an algorithm main function AStarExpansion, wherein the program flow chart is shown in fig. 3, and setting all data of the queue_queue as a maximum value, wherein the queue_is used for storing the searched cost value F. Corresponding to a close table;
step 2, adding the starting point coordinates into the queue head of the queue;
step 3, setting the current search point N as the point currently positioned at the head of the queue;
Step 4, verifying whether the point N is a target point, if so, finding a path, returning to true, and acquiring the path by GetPlanFromPotential functions after returning to the previous stage; if not, continuing;
step 5, calling an add function four times, and examining cost values F of four points around a point N;
setting four points around the N points as M 1、M2、M3、M4, and initializing n=1;
Step 6, the add function calculates the cost value F of the point M n, and the flowchart is shown in fig. 3, and the specific steps are as follows:
Step 6.1, determining whether the point M n is calculated or the cost value of the search point is greater than lethal _cost, if yes, jumping to step 6.6, and if not, continuing. The cost value represents the initial cost of each pixel point in the map, and is used for judging whether the pixel point is an obstacle, if the cost is greater than a constant lethal _cost_, the point M n is illustrated as being in an area which cannot be traveled;
Step 6.2, calling calculatePotential a function to calculate the potential value, that is, the minimum cost required from the start point to the point M n, wherein the principle is to find the point around the point M n where the potential value in four directions is minimum as the parent node, and the potential= (parent node potential+current point cost) of the point M n;
Step 6.3, calling a security circle main function safeCost, calculating obstacle _cost of the current search point, wherein the flow chart is shown in fig. 4, obstacle _cost represents a cost value corresponding to the distance between the current search point and an obstacle, and the security circle specifically comprises the following steps:
Step 6.3.1, initializing a safety ring, and configuring a safety radius safe_dis and a proportion parameter safe_ratio.
Step 6.3.2, judging whether the layer number r is smaller than the safe distance safe_dis, wherein as shown in fig. 5, the safety ring is simplified into concentric squares to search from inside to outside layer by layer, the layer number is expressed as r, if r is larger than or equal to safe_dis, the safety ring is searched completely, no obstacle is found, obstacle _cost=0, and the step 6.4 is skipped; if the search result is smaller than the preset search result, continuing searching;
step 6.3.3, judging whether the side is larger than 3, if so, indicating that the search of the current layer is finished, adding 1 to the layer number r, and jumping back to the step 6.3.2; if not more than 3, continuing searching;
the side represents four sides of the square after each layer of the safety ring is simplified into the square, and the value of the side is 0 to 3;
Step 6.3.4, judging whether i is smaller than 2r, searching four sides of each layer respectively in square according to the sequence of upper, right, lower and left, if i is larger than or equal to 2r, indicating that the current side searching is completed, adding 1 to side, and jumping back to step 6.3.3; if i is less than 2r, continuing searching;
the i is the label of each point on each side of the concentric square, and the value of i is 0 to 2r-1
Step 6.3.5, in the embodiment, two-dimensional coordinate values of all the search points are converted into one-dimensional serial numbers, which side of the current search is up, right, down and left is judged according to the side values, and serial numbers current_num of the currently searched pixel points are determined by using different formulas according to different sides;
The current search pixel point is different from the point M n, and is each point included in the security loop surrounding the point M n, and is hereinafter referred to as a current_num point;
The formula when the search proceeds to each edge is specifically:
Upper current_num 1 =num-r x nx_ -r+i+1
Right current_num 2 =num-r x nx_ +r+i x n u
Lower current_num 3 =num+r x nx_ +r-i
Left current_num 4 =num+r x nx_ -r-i x nx u
Wherein num is a one-dimensional serial number of a point M n, r is the number of layers searched currently, nx_is the width of the map, and i is the label of each point on each side of the concentric square;
Step 6.3.6, judging whether the current_num point is an obstacle, if not, adding 1 to i and jumping back to step 6.3.4; if so, calculating the Euclidean distance between the current_num point and the i point, and if the distance is smaller than safe_dis, indicating that the current_num point is in the range of the safety ring, and continuing the next step;
step 6.3.7, calculating obstacle _cost value, wherein the calculation formula is obstacle _cost=i×exp (-1×safe_ratio×distance), I is the maximum value of obstacle _cost, distance is the estimated distance between the current point and the target point, and the calculated obstacle _cost value is returned to the add function by using manhattan distance;
So far, from step 6.3.1 to step 6.3.7, the method avoids the problems that the required path is too close to the obstacle, and the operation efficiency of the robot is easy to be reduced or the operation safety is easy to be influenced.
Step 6.4, adding the potential value, obstacle _cost value and distance value to obtain a total cost value F;
Step 6.5, reordering the queue_to ensure that the point with the minimum F value is at the head of the queue;
and 6.6, if N is greater than or equal to 4, finishing the search of adjacent points around the N points, jumping to the step7, and if N is less than 4, adding 1 to N, and jumping back to the step 6.1.
Step 7, if all points in the map are traversed, indicating that no path is found, and returning to false; if not, jumping back to the step 3.
The procedure described above was performed as in example 1. In embodiment 1, the safety ring method functions to prevent the generation path from being too close to the obstacle.
As shown in fig. 6, the white circle is a robot, the dark area is an obstacle, and the curve is a global planned path, so that when the a-algorithm without a safety ring is used, the global planned path can be closely distributed to the obstacle under certain conditions to obtain a theoretical shortest path, the radius of the robot can only be corrected by local path planning, and the robot cannot directly advance along the path, and in embodiment 1, the limitation of the traditional global planning algorithm is proved when the robot is blocked by the obstacle for a plurality of times. Meanwhile, the method can save the operation amount of the map preprocessing stage, and can avoid the area which is not involved in the processing task under the condition of larger map or higher resolution, thereby saving the operation resource.
FIG. 7 is a one-sided obstacle detour experiment without using the safety loop method, wherein the dark rectangle is an obstacle; fig. 8 is a single-sided obstacle detour experiment using the safety loop method. By comparison, the safety loop algorithm in fig. 8 can be seen to make the global planned path obviously bypass the obstacle, and a safer path is obtained.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The improvement method of the robot global path planning A based on the safety ring is characterized in that: the method comprises the following steps:
step 1, initializing an algorithm A, creating an open table and a close table, and adding a starting point into the open table;
Step 2, setting a starting point in an open table as a current searching point N;
Step 3, initializing i to be 1;
Step 4, judging whether the current search point N is a target point, if yes, jumping to step 6, otherwise, if not, calculating cost values F of all points adjacent to the search point N in the map, adding all points into an open table, and adding the current search point N into a close table;
in step 4, calculating cost values F of all points adjacent to the search point N, specifically: calculating a cost value F of M i, which comprises the following substeps:
Step 4.1, calculating the shortest distance G between the point M i and the starting point;
Step 4.2, calculating a distance estimated value H between the point M i and the target point;
Step 4.3, calculating a cost value P corresponding to the distance between the point M i and the nearest obstacle based on the safety ring, specifically including the following sub-steps:
Step 4.3.1, setting a round safety radius r around the search point as a safety ring, approximating the safety ring as a square and dividing the square into a plurality of concentric squares, taking the innermost concentric square as the innermost layer, and dividing the annular space obtained by subtracting the square on the inner side of each square into one layer to divide the square, namely the safety ring into a plurality of layers;
step 4.3.2, judging whether the search of all layers of the safety ring is finished, if not, searching the next layer, if yes, returning to the P value of 0 and entering the step 4.4, wherein no obstacle exists in the range;
step 4.3.3, judging whether all searches of the current layer are completed, if not, searching the next edge of the current layer, and if so, returning to the step 4.3.2;
Step 4.3.4, judging whether all searches on the current edge are completed, if not, continuing to detect the next point, and if so, returning to step 4.3.3;
Step 4.3.5, judging whether the current point is an obstacle, if not, returning to step 4.3.4, if so, calculating a P value, and then entering step 4.4;
step 4.4, calculating a cost value F of the point M i;
The calculation formula of the cost value F is F=G+P+H;
H is the cost corresponding to the estimated distance from the current point to the destination node, and G is the cost corresponding to the distance from the starting point to the current point; the size of the F value is determined by whether the node is occupied by an obstacle or not and the distance between the node and the starting point and the target point, the lower the F value of the node which is closer to the starting point or the target point is, the easier the node is selected as a path, the P value is added into the cost value F, the generated path can generate a new path according to the distance between the search point and the obstacle, and if the obstacle exists in the safety ring of the search point, the search point is more likely to be selected by the A;
step 4.5, judging whether i is equal to K, if so, indicating that all K adjacent points of the current search point N are searched completely, setting i as 1, and jumping to the step 5; if not, adding 1 to i, and jumping to the step 4.1;
Step 5, selecting a node with the minimum cost value F in the current open table as a current search point N, and jumping to the step 4;
and 6, finishing searching, namely starting from the target point, arranging the values F in the close table from small to large, and obtaining the optimal path until the starting point.
2. The robot global path planning a improvement method according to claim 1, characterized by: in the step 1, the open table and the close table both comprise a plurality of nodes.
3. The robot global path planning a improvement method according to claim 1, characterized by: in step 4, all adjacent points are K in total.
4. The robot global path planning a improvement method according to claim 1, characterized by: in step 4.1, the calculation formula of the shortest distance G is g=g' +c; and G' is the G value of the minimum point of the G values in the searched points around the point M i, and C is the cost constant for every increase of one lattice distance.
5. The robot global path planning a improvement method according to claim 1, characterized by: the estimated value H of the distance between the point M i and the target point in step 4.2 is one of the euclidean distance H 1 or the manhattan distance H 2; and H 1＝[(Nx-Dx)2+(Nx-Dx)2]1/2 and H 2＝|Nx-Dx|+|Ny-Dy.
6. The robot global path planning a improvement method according to claim 1, characterized by: in step 4.2, (Nx, ny) and (Dx, dy) are the coordinates of the current search point and the target point, respectively.
7. The robot global path planning a improvement method according to claim 1, characterized by: step 4.3.2, searching the next layer refers to searching the layer outside the current layer in the sequence from the innermost layer of the security ring to the outer layer.
8. The robot global path planning a improvement method according to claim 1, characterized by: and 4.3.3, searching the next edge of the current layer refers to searching the next edge of the current edge when four edges of the square are searched in the order of up, right, down and left.
9. The robot global path planning a improvement method according to claim 1, characterized by: the step 4.3.4 of detecting the next point refers to searching all points on the current edge in clockwise direction along the current edge.
10. The robot global path planning a improvement method according to claim 1, characterized by: the calculation formula of the P value in step 4.3.5 is p=i×exp (-r×d), where I is the maximum value of P, r is the safety radius of the safety ring, and d is the distance between the current point and the nearest obstacle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210098000.4A CN114510045B (en) | 2022-01-27 | 2022-01-27 | Robot global path planning A improvement method based on safety ring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210098000.4A CN114510045B (en) | 2022-01-27 | 2022-01-27 | Robot global path planning A improvement method based on safety ring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114510045A CN114510045A (en) | 2022-05-17 |
| CN114510045B true CN114510045B (en) | 2024-06-25 |
Family
ID=81548955
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210098000.4A Active CN114510045B (en) | 2022-01-27 | 2022-01-27 | Robot global path planning A improvement method based on safety ring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114510045B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110332940B (en) * | 2019-07-30 | 2022-08-02 | 重庆邮电大学 | Firefighter indoor evacuation path planning method based on improved A-star algorithm |
| CN111006666B (en) * | 2019-11-21 | 2021-10-29 | 深圳市优必选科技股份有限公司 | Robot path planning method and device, storage medium and robot |
-
2022
- 2022-01-27 CN CN202210098000.4A patent/CN114510045B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114510045A (en) | 2022-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112000754B (en) | Map construction method, device, storage medium and computer equipment | |
| WO2018121448A1 (en) | Topology map creation method and navigation method for mobile robot, programmable device, and computer readable medium | |
| CN111811514B (en) | Path planning method based on regular hexagon grid jump point search algorithm | |
| US9116523B2 (en) | Apparatus and method of cell-based path planning for mobile body | |
| CN110006430B (en) | Optimization method of track planning algorithm | |
| CN109541634A (en) | A kind of paths planning method, device and mobile device | |
| CN106643701A (en) | Robot inter-detection method and robot inter-detection device | |
| CN109059924A (en) | Adjoint robot Incremental Route method and system for planning based on A* algorithm | |
| CN112284393B (en) | Global path planning method and system for intelligent mobile robot | |
| JP5381679B2 (en) | Route search system, method, program, and moving object | |
| WO2023125512A1 (en) | Navigation path planning method and apparatus, and storage medium | |
| CN112683275B (en) | Path planning method for grid map | |
| CN114281076B (en) | Covering and moving operation method of robot | |
| CN105869512A (en) | Multi-information mixed measurement mapping method and device | |
| CN110244716A (en) | A method of the robot based on wave front algorithm is explored | |
| CN114764239A (en) | Cleaning robot control method, cleaning robot control device, computer equipment and storage medium | |
| CN114510045B (en) | Robot global path planning A improvement method based on safety ring | |
| JP2018120482A (en) | Robot and method of controlling the same | |
| CN112393719B (en) | Grid semantic map generation method and device and storage equipment | |
| KR101297608B1 (en) | Method and system for robot coverage of unknown environment | |
| Yao et al. | Development of augmented reality indoor navigation system based on enhanced A* algorithm | |
| CN113485378A (en) | Mobile robot path planning method, system and storage medium based on traffic rules | |
| Ali et al. | Global mobile robot path planning using laser simulator | |
| CN113721603B (en) | Base station exploration method, base station exploration device, robot and readable storage medium | |
| CN111912407B (en) | Path planning method of multi-robot system |
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 | ||
| GR01 | Patent grant |