CN113188555A - Mobile robot path planning method - Google Patents
Mobile robot path planning method Download PDFInfo
- Publication number
- CN113188555A CN113188555A CN202110445371.0A CN202110445371A CN113188555A CN 113188555 A CN113188555 A CN 113188555A CN 202110445371 A CN202110445371 A CN 202110445371A CN 113188555 A CN113188555 A CN 113188555A
- Authority
- CN
- China
- Prior art keywords
- node
- path
- optimized
- mobile robot
- initial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a path planning method for a mobile robot, which comprises the following steps: constructing a grid map of the motion environment of the mobile robot; determining an initial starting node and an initial target node of the mobile robot, and planning a path local graph with n nodes between the initial starting node and the initial target node in a grid map by using an A-x algorithm; performing k equal division processing on the distance between every two adjacent nodes in the n nodes to generate a new path node set, wherein an initial starting node and an initial target node in the new path node set are kept unchanged; connecting the initial starting node with the initial target node, judging the distance between the barrier and the connecting line, and carrying out optimization processing to obtain the optimized starting node and the optimized target node; and storing the optimized starting node and the optimized target node into the new path node set, and sequentially connecting other nodes in the new path node set to obtain the optimized one-way planning path of the mobile robot.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a path planning method of a mobile robot.
Background
With the development of artificial intelligence technology, mobile robots become more and more common in daily life, and the path planning problem of mobile robots is a research hotspot at present. The problem can be described as finding a shortest or optimal safe collision-free path from a starting point to an end point in an environment with obstacles according to a certain performance index. Compared with other algorithms, the A-x algorithm is simple in model construction and high in search efficiency, a solution obtained aiming at a static scene is close to an optimal solution, and the method has strong expansibility and adaptability to different scenes. And the A-algorithm comprehensively evaluates the cost values of the expansion nodes by setting a proper heuristic function, and selects the minimum point for expansion by comparing the cost values of the expansion nodes until the target node is found.
However, although the a-algorithm can plan a safe path without collision, the planned path is not an optimal path. When the algorithm plans a path in the grid graph, the node position is limited to the center of the grid. The path planned by the A-algorithm has more inflection points and is not smooth enough. Therefore, how to make the path drawn by the mobile robot using the improved a-x algorithm more suitable for traveling in a real environment is a problem to be solved at present.
Disclosure of Invention
The invention provides a path planning method for a mobile robot, which aims to solve the technical problem of how to make a path drawn by the mobile robot by using an improved A-theorem more suitable for running in an actual environment.
The invention solves the technical problems through the following technical scheme:
a mobile robot path planning method, the method comprising:
constructing a grid map of the motion environment of the mobile robot;
determining an initial starting node and an initial target node of the mobile robot, and planning a path local graph with n nodes between the initial starting node and the initial target node in the grid map by using an A-x algorithm;
performing k equal division processing on the distance between every two adjacent nodes in the n nodes to generate a new path node set, wherein the initial starting node and the initial target node in the new path node set are kept unchanged;
connecting the initial starting node with the initial target node, judging the distance between the barrier and the connecting line, and carrying out optimization processing to obtain an optimized starting node and an optimized target node;
and storing the optimized starting node and the optimized target node into the new path node set, and sequentially connecting other nodes in the new path node set to obtain the optimized one-way planning path of the mobile robot.
Further, the method further comprises re-optimizing the unidirectional planned path:
performing k equal division processing on the distance between every two adjacent nodes of the unidirectional planning path;
and performing reverse optimization from the optimized target node to the optimized starting node, wherein the reverse optimization comprises judging the distance between the barrier and the connecting line and performing optimization processing to obtain the node and the moving path which are optimized again.
Preferably, the determining the distance between the obstacle and the connecting line and performing optimization processing includes:
when a connecting line of the initial starting node and the initial target node passes through an obstacle, moving the initial target node backwards by a plurality of nodes;
connecting the initial starting node with the initial target nodes of the backward moving nodes again, and judging whether the connecting line passes through the barrier or not;
repeating the above operations until the obstacle is not passed through, and determining the optimized starting node and target node when the obstacle is not passed through.
Preferably, the mobile robot is planned with the partial graph of the path using euclidean distance as a heuristic function of the a algorithm.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows: the path length is obviously shortened; planning a path in the grid map is not limited to a grid center position; the number of inflection points is effectively reduced; also significantly optimized in terms of smoothness.
Drawings
Fig. 1 is a flowchart of a method in an embodiment of a method for planning a path of a mobile robot according to the present invention;
fig. 2 is a partial graph of a path of an a-algorithm in an embodiment of a path planning method for a mobile robot according to the present invention;
fig. 3 is a one-way path planning diagram in an embodiment of a path planning method for a mobile robot according to the present invention;
fig. 4 is a path diagram after bidirectional optimization in an embodiment of a path planning method for a mobile robot according to the present invention;
fig. 5 is a mobile robot planning path diagram adopting the a-x algorithm in an embodiment of the mobile robot path planning method of the present invention;
fig. 6 is a mobile robot planned path diagram after an improved a-x algorithm in an embodiment of a mobile robot path planning method according to the present invention.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 shows a flow chart of a method according to an embodiment of the invention:
s01: constructing a grid map of the motion environment of the mobile robot;
in one example, a laser radar scans the surrounding environment to obtain point cloud data, a large amount of point cloud data is processed in a computer to obtain a global map, and finally the map is converted into an occupation map, namely a grid map. In this limited two-dimensional space, the direction of movement of the robot may be arbitrary and does not take into account the effect of altitude. The environmental information is constant throughout the path planning process.
S02: determining an initial starting node and an initial target node of the mobile robot, and planning a path local graph with n nodes between the initial starting node and the initial target node in the grid map by using an A-x algorithm;
in one example, as shown in fig. 2, after the start and end points are selected, a path partial graph is planned in any environment using the a-x algorithm, with n nodes between the selected initial start node and the initial target node. The A-algorithm is used as a classical heuristic search algorithm, the core of the A-algorithm is a valuation function, and heuristic cost values are added into the valuation function, so that the search process is continuously guided to a target state from the heuristic cost values, a large number of calculation processes can be saved, and the efficiency of the algorithm is improved. The valuation function is as follows:
f(s)＝g(s)+h(s)
where f(s) is the estimated value of the node s, g(s) characterizes the communication cost value from the start state Sstart to the state s, and h(s) characterizes the heuristic cost value from the state s to the target state Sgoal. The Euclidean distance is used as an enlightening function, a current node is used as a father node in the path searching process, child nodes of a connected area of the current node are searched, the evaluation value f of each child node is calculated through the formula (1), the node with the minimum value f is selected as the next searched node, the father node of the next searched node is calculated until the searched node is a target point, and finally the father node of the next searched node is traced back from the target point in a reverse direction to obtain a planned path.
S03: performing k equal division processing on the distance between every two adjacent nodes in the n nodes to generate a new path node set, wherein the initial starting node and the initial target node in the new path node set are kept unchanged;
in one example, the Euclidean distance formula is used as a heuristic function for improving the A-algorithm to carry out path planning, the planned path is subjected to adjacent node segmentation, the number of nodes is greatly increased, and therefore the nodes are not limited to the center of the grid any more. And (4) dividing the distance between every two adjacent nodes, and equally dividing the distance between the adjacent nodes into k equal parts, wherein the path contains ((n-1) × k +1) nodes.
S04: connecting the initial starting node with the initial target node, judging the distance between the barrier and the connecting line, and carrying out optimization processing to obtain an optimized starting node and an optimized target node;
in one example, b is a set of newly generated path nodes, and a start node is first stored in the set b, where s is 0 and d is (n-1) k. And connecting the starting node with the target node, and if the node passes through the barrier, performing d-1 operation to ensure that the node is continuously connected with the current node to judge whether the barrier passes through. And storing the node into the set b until the node does not pass through the barrier, and meanwhile, taking the node as an initial node.
S05: and storing the optimized starting node and the optimized target node into the new path node set, and sequentially connecting other nodes in the new path node set to obtain the optimized one-way planning path of the mobile robot.
In one example, the above operations are repeated until the target node is stored in the set b, and the elements in the set b are connected in sequence. The one-way planned path is shown by the diagonal lines in fig. 3.
In an optional example, the distance between each two adjacent nodes of the unidirectional planned path is further subjected to k equal parts of segmentation processing. And performing reverse optimization from the optimized target node to the optimized starting node, wherein the reverse optimization comprises judging the distance between the barrier and the connecting line and performing optimization processing to obtain the node and the moving path which are optimized again. As shown in fig. 4, k is 2, and then optimization is performed from the target point to the starting point, and the final optimization path of the a-x algorithm after optimization is shown by the diagonal line in fig. 4.
In one example, as shown in fig. 5 and fig. 6, the paths planned by the conventional a-algorithm and the improved a-algorithm are shown in table 1 below:
TABLE 1 comparison of A-x algorithm effects before and after improvement
As can be seen from fig. 6, compared to the conventional a-algorithm shown in fig. 5, the improved a-algorithm has a significant reduction in path length, an effective reduction in the number of corners, and a significant optimization in smoothness. The moving robot is reduced from 64.038 to 53.616 in moving path length, the number of inflection points is reduced from 9 to 3, and the path length reduction rate and the inflection point number reduction rate are respectively as high as 16.3% and 66.7%. It can be seen that the mobile robot is more efficient in completing the assigned tasks when using the method of the present invention, and can complete the same task in a shorter time.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (4)
1. A method for mobile robot path planning, the method comprising:
constructing a grid map of the motion environment of the mobile robot;
determining an initial starting node and an initial target node of the mobile robot, and planning a path local graph with n nodes between the initial starting node and the initial target node in the grid map by using an A-x algorithm;
performing k equal division processing on the distance between every two adjacent nodes in the n nodes to generate a new path node set, wherein the initial starting node and the initial target node in the new path node set are kept unchanged;
connecting the initial starting node with the initial target node, judging the distance between the barrier and the connecting line, and carrying out optimization processing to obtain an optimized starting node and an optimized target node;
and storing the optimized starting node and the optimized target node into the new path node set, and sequentially connecting other nodes in the new path node set to obtain the optimized one-way planning path of the mobile robot.
2. A method for mobile robot path planning according to claim 1, further comprising re-optimizing the uni-directionally planned path by:
performing k equal division processing on the distance between every two adjacent nodes of the unidirectional planning path;
and performing reverse optimization from the optimized target node to the optimized starting node, wherein the reverse optimization comprises judging the distance between the barrier and the connecting line and performing optimization processing to obtain the node and the moving path which are optimized again.
3. The method according to any one of claims 1 to 2, wherein the determining the distance between the obstacle and the connecting line for optimization comprises:
when a connecting line of the initial starting node and the initial target node passes through an obstacle, moving the initial target node backwards by a plurality of nodes;
connecting the initial starting node with the initial target nodes of the backward moving nodes again, and judging whether the connecting line passes through the barrier or not;
repeating the above operations until the obstacle is not passed through, and determining the optimized starting node and target node when the obstacle is not passed through.
4. A method for planning the path of a mobile robot according to claim 3, wherein the path local map is planned for the mobile robot using euclidean distance as a heuristic function of the a-algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110445371.0A CN113188555A (en) | 2021-04-25 | 2021-04-25 | Mobile robot path planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110445371.0A CN113188555A (en) | 2021-04-25 | 2021-04-25 | Mobile robot path planning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113188555A true CN113188555A (en) | 2021-07-30 |
Family
ID=76978431
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110445371.0A Pending CN113188555A (en) | 2021-04-25 | 2021-04-25 | Mobile robot path planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113188555A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113720344A (en) * | 2021-08-30 | 2021-11-30 | 深圳市银星智能科技股份有限公司 | Path searching method and device, intelligent device and storage medium |
| CN114323045A (en) * | 2021-12-24 | 2022-04-12 | 浙江中控技术股份有限公司 | Path planning method and device |
| CN114485611A (en) * | 2021-12-28 | 2022-05-13 | 中科星图股份有限公司 | Three-dimensional space shortest path planning method and device based on Beidou grid code |
| CN113720344B (en) * | 2021-08-30 | 2024-06-04 | 深圳银星智能集团股份有限公司 | Path searching method, path searching device, intelligent equipment and storage medium |
-
2021
- 2021-04-25 CN CN202110445371.0A patent/CN113188555A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113720344A (en) * | 2021-08-30 | 2021-11-30 | 深圳市银星智能科技股份有限公司 | Path searching method and device, intelligent device and storage medium |
| CN113720344B (en) * | 2021-08-30 | 2024-06-04 | 深圳银星智能集团股份有限公司 | Path searching method, path searching device, intelligent equipment and storage medium |
| CN114323045A (en) * | 2021-12-24 | 2022-04-12 | 浙江中控技术股份有限公司 | Path planning method and device |
| CN114323045B (en) * | 2021-12-24 | 2024-04-19 | 浙江中控技术股份有限公司 | Path planning method and device |
| CN114485611A (en) * | 2021-12-28 | 2022-05-13 | 中科星图股份有限公司 | Three-dimensional space shortest path planning method and device based on Beidou grid code |
| CN114485611B (en) * | 2021-12-28 | 2024-04-26 | 中科星图股份有限公司 | Three-dimensional space shortest path planning method and device based on Beidou grid codes |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2023155371A1 (en) | Stable movement global path planning method for indoor mobile robot | |
| CN113188555A (en) | Mobile robot path planning method | |
| CN110487290B (en) | Unmanned vehicle local path planning method based on variable step size A star search | |
| CN110456825B (en) | Unmanned aerial vehicle online motion planning method based on improved rapid random search tree | |
| CN113485360B (en) | AGV robot path planning method and system based on improved search algorithm | |
| CN114115362B (en) | Unmanned aerial vehicle track planning method based on bidirectional APF-RRT algorithm | |
| CN112947594B (en) | Unmanned aerial vehicle-oriented track planning method | |
| Xu et al. | A fast path planning algorithm fusing prm and p-bi-rrt | |
| CN114489052B (en) | Path planning method for improving RRT algorithm reconnection strategy | |
| CN113359775A (en) | Dynamic variable sampling area RRT unmanned vehicle path planning method | |
| CN110954124A (en) | Adaptive path planning method and system based on A-PSO algorithm | |
| Petereit et al. | Mobile robot motion planning in multi-resolution lattices with hybrid dimensionality | |
| CN114593743A (en) | Path planning method and device based on improved bidirectional RRT algorithm | |
| CN113778093A (en) | AMR autonomous mobile robot path planning method based on improved sparrow search algorithm | |
| CN112197783B (en) | Two-stage multi-sampling RRT path planning method considering locomotive direction | |
| CN116817947B (en) | Random time path planning method based on variable step length mechanism | |
| Zeng et al. | An efficient path planning algorithm for mobile robots | |
| Zhang et al. | An improved dynamic step size RRT algorithm in complex environments | |
| CN113741484A (en) | Path planning method, system and medium based on probability model | |
| Gochev et al. | Anytime tree-restoring weighted A* graph search | |
| CN113325834A (en) | Path planning method of improved A-x algorithm based on graph preprocessing | |
| Abdouni et al. | A new sampling strategy to improve the performance of mobile robot path planning algorithms | |
| CN111912407B (en) | Path planning method of multi-robot system | |
| CN115454106B (en) | AUV (autonomous Underwater vehicle) docking path planning method based on bidirectional search RRT (remote radio transmitter) | |
| Liu et al. | Learning-Based Neural Ant Colony Optimization |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210730 |