CN114200933A - Three-dimensional path planning method for bridge crane - Google Patents
Three-dimensional path planning method for bridge crane Download PDFInfo
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- 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
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- 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
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Abstract
The invention relates to a three-dimensional path planning method for a bridge crane. The technical problems that time and energy are wasted, a target point is completely surrounded by an obstacle, the target can not be reached, and the swinging problem caused by frequent braking due to too many nodes is mainly solved. The technical scheme of the invention is as follows: a three-dimensional path planning method for a bridge crane comprises the following steps: s1, collecting and processing the working environment information of the bridge crane through a signal collecting and processing unit; s2, generating a three-dimensional grid map from the environment information, and identifying the obstacles and the obstacle-free grids by different colors; s3, searching out an optimal path by adopting a three-dimensional A-star algorithm according to the three-dimensional grid map; and S4, optimizing the generated operation path by adopting an interpolation method. The invention has the advantages of saving time and energy, improving the working efficiency of the bridge crane and the like.
Description
Technical Field
The invention belongs to the technical field of bridge crane path planning, and particularly relates to a three-dimensional path planning method for a bridge crane.
Background
At present, path research on a crane is usually aimed at the research in a two-dimensional plane, and the research on the lifting height is ignored, because the path research of the crane can be generally satisfied only by the two-dimensional plane. The main movement mechanisms of the bridge crane comprise a big and a small vehicle movement mechanism and a hoisting mechanism, so that more possibilities are provided for path planning of the bridge crane, U-shaped obstacles are not lacked in a workshop, and if a traditional two-dimensional A-star algorithm is adopted, the obstacles are bypassed, so that the required time and energy are greatly wasted, and the working efficiency is reduced. When target points with different heights or target points surrounded by obstacles need to be controlled, the two-dimensional path planning alone cannot meet the working requirement, so the existing method has the defects of low working efficiency and incapability of meeting the working requirement of the two-dimensional path planning.
In addition, the traditional two-dimensional A-star algorithm has the defects that the nodes are too many, the crane is easily braked frequently, and the lifted object swings indefinitely.
Disclosure of Invention
The invention aims to solve the technical problems that time and energy are wasted, a target point is completely surrounded by an obstacle to cause a target unreachable phenomenon, and nodes are too many to cause frequent braking to cause swinging in the traditional two-dimensional path planning of the conventional bridge crane, and provides a three-dimensional path planning method of the bridge crane.
In order to solve the technical problems, the invention adopts the technical scheme that:
a three-dimensional path planning method for a bridge crane comprises the following steps:
s1, collecting and processing the working environment information of the bridge crane through a signal collecting and processing unit;
s2, generating a three-dimensional grid map from the environment information, and identifying the obstacles and the obstacle-free grids by different colors;
s3, searching out an optimal path by adopting a three-dimensional A-star algorithm according to the three-dimensional grid map;
and S4, optimizing the generated operation path by adopting an interpolation method.
Further, in step S1, the signal acquiring and processing unit is configured to acquire and process an environment image of a working area of the bridge crane in real time, and create an environment map according to the environment image.
Further, the step S2 of generating the three-dimensional grid map from the environment information is: and (3) carrying out regularization treatment on the irregular barrier by adopting a three-dimensional grid method to form a standardized three-dimensional grid, and ensuring that each three-dimensional grid has the same value.
Further, the step S2 identifies the obstacle and the obstacle-free grid as: by performing binarization processing on the stereoscopic grid map, the threshold value of the area without obstacles is set to 1, the threshold value with obstacles is set to 0 by white display, and the threshold value is displayed by black.
Further, the step of searching for the optimal path by using the three-dimensional a-x algorithm in step S3 includes the following steps:
s31, determining a starting point and a target point, creating an open table and a close table, and initializing to be empty;
s32, adding the starting point into the open table, and setting the starting point as a father node;
s33, searching feasible child nodes around the father node, and adding the feasible child nodes into an open table;
s34, removing the father node from the open table, adding the father node into the close table, and calculating the cost value of each child node in the open table according to an evaluation function f (n);
s35, selecting the child node with the minimum cost value in the open table as a father node of the next expansion, and expanding;
s36, judging whether the child node is a target point; if so, proceed to the next step, if not return to S33, continue the loop;
s37, after finding the target point, backtracking from the target point to the starting point in a close table to obtain the shortest path;
s38, the three-dimensional a algorithm ends.
Further, the determination of the start point and the target point in step S31 is: and respectively selecting the operation starting point and the target point of the bridge crane as a path searching starting point and a path searching terminal point.
Further, the step S33 of searching feasible child nodes around the parent node refers to: selecting a central point of each three-dimensional grid in a three-dimensional grid map as an expandable node, expanding adjacent nodes from a search starting point in the three-dimensional grid map to determine a next node, and setting the number of the adjacent nodes in an expansion domain around a path search father node to be six, namely front, back, left, right, up and down.
Further, in step S34, the evaluation function f (n) is a cost function f (n), and the expression is:
f(n)＝g(n)+w(n)*h(n)
h(n)＝(abs(n.x-goal.x)+abs(n.y-goal.y)+abs(n.z-goal.z)
where (n) is a cost function of node n, g (n) is an actual cost from an origin to node n, h (n) is an estimated cost from node n to a destination, w (n) is a weight coefficient of h (n) in a dynamic scaling algorithm, where w (n) > 1, w (n) is greater and closer to the BFS algorithm, and w (n) is relatively smaller and closer to the Dijkstra algorithm; n.x is the abscissa of the node, n.y is the ordinate of the node, n.z is the ordinate of the node, good.x is the abscissa of the target point, good.y is the ordinate of the target point, and good.z is the ordinate of the target point.
Further, the step S35 of selecting the child node with the smallest cost value in the open table as the parent node of the next expansion refers to: when the cost functions f of a plurality of adjacent nodes are equal, setting priorities for the adjacent nodes, wherein the priority is the highest priority closest to the target point, and the priorities of the adjacent nodes are sequentially arranged according to anticlockwise rotation.
Further, in step S4, the optimizing the generated operation path by using an interpolation method specifically includes: firstly, determining an initial point and a target point, then carrying out coordinate processing on all nodes, and carrying out path optimization by adopting cubic spline interpolation; if the optimized path interferes with the obstacle, node supplementation can be carried out, constraint is increased, and path optimization is carried out again until the optimization is successful.
The invention has the beneficial effects that:
the invention overcomes the defect that time and energy are wasted because the function of a hoisting mechanism is ignored in the traditional two-dimensional path planning of the bridge crane; the target point is completely surrounded by the barrier to cause the phenomenon of unreachable target; and too many nodes cause the problem of sway caused by frequent braking. The intelligent working requirement of the bridge crane is better met, the time and the energy are saved, and the working efficiency and the working quality of the bridge crane are improved.
Drawings
FIG. 1 is a work flow block diagram of the present invention.
Fig. 2 is a flow chart of the algorithm a of the present invention.
Fig. 3 is a schematic diagram of the node expansion of the present invention.
Fig. 4 is a flow chart of the path optimization of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
As shown in fig. 1, the method for planning a three-dimensional path of a bridge crane in this embodiment includes the following steps:
s1, collecting and processing the working environment information of the bridge crane through a signal collecting and processing unit;
s2, generating a three-dimensional grid map from the environment information, and identifying the obstacles and the obstacle-free grids by different colors;
s3, searching out an optimal path by adopting a three-dimensional A-star algorithm according to the three-dimensional grid map;
and S4, optimizing the generated operation path by adopting an interpolation method.
Further, in step S1, the signal acquiring and processing unit is configured to acquire and process an environment image of a working area of the bridge crane in real time, and create an environment map according to the environment image.
Further, the step S2 of generating the three-dimensional grid map from the environment information is: and (3) carrying out regularization treatment on the irregular barrier by adopting a three-dimensional grid method to form a standardized three-dimensional grid, and ensuring that each three-dimensional grid has the same value.
Further, the step S2 identifies the obstacle and the obstacle-free grid as: by performing binarization processing on the stereoscopic grid map, the threshold value of the area without obstacles is set to 1, the threshold value with obstacles is set to 0 by white display, and the threshold value is displayed by black.
As shown in fig. 2, the step of searching for the optimal path by using the three-dimensional a-x algorithm in step S3 includes the following steps:
s31, determining a starting point and a target point, creating an open table and a close table, and initializing to be empty;
s32, adding the starting point into the open table, and setting the starting point as a father node;
s33, searching feasible child nodes around the father node, and adding the feasible child nodes into an open table;
s34, removing the father node from the open table, adding the father node into the close table, and calculating the cost value of each child node in the open table according to an evaluation function f (n);
s35, selecting the child node with the minimum cost value in the open table as a father node of the next expansion, and expanding;
s36, judging whether the child node is a target point; if so, proceed to the next step, if not return to S33, continue the loop;
s37, after finding the target point, backtracking from the target point to the starting point in a close table to obtain the shortest path;
s38, the three-dimensional a algorithm ends.
Further, the determination of the start point and the target point in step S31 is: and respectively selecting the running starting point and the target point of the bridge crane as the starting point and the end point of the path search.
As shown in fig. 3, the step S33 of searching for feasible child nodes around the parent node refers to: selecting a central point of each three-dimensional grid in a three-dimensional grid map as an expandable node, expanding adjacent nodes from a search starting point in the three-dimensional grid map to determine a next node, and setting the number of the adjacent nodes in an expansion domain around a path search father node to be six, namely front, back, left, right, up and down.
In step S34, the evaluation function f (n) is a cost function f (n), and its expression is:
f(n)＝g(n)+w(n)*h(n)
h(n)＝(abs(n.x-goal.x)+abs(n.y-goal.y)+abs(n.z-goal.z)
where (n) is a cost function of node n, g (n) is an actual cost from an origin to node n, h (n) is an estimated cost from node n to a destination, w (n) is a weight coefficient of h (n) in a dynamic scaling algorithm, where w (n) > 1, w (n) is greater and closer to the BFS algorithm, and w (n) is relatively smaller and closer to the Dijkstra algorithm; n.x is the abscissa of the node, n.y is the ordinate of the node, n.z is the ordinate of the node, good.x is the abscissa of the target point, good.y is the ordinate of the target point, and good.z is the ordinate of the target point.
Further, the step S35 of selecting the child node with the smallest cost value in the open table as the parent node of the next expansion refers to: when the cost functions f of a plurality of adjacent nodes are equal, setting priorities for the adjacent nodes, wherein the priority is the highest priority closest to the target point, and the priorities of the adjacent nodes are sequentially arranged according to anticlockwise rotation.
As shown in fig. 4, the step S4 adopts an interpolation method to optimize the generated operation path, specifically: firstly, determining an initial point and a target point, then carrying out coordinate processing on all nodes, and carrying out path optimization by adopting cubic spline interpolation; if the optimized path interferes with the obstacle, node supplementation can be carried out, constraint is increased, and path optimization is carried out again until the optimization is successful.
The invention is based on better meeting the requirement of a bridge crane to complete work and improving efficiency, provides a three-dimensional path planning method by further expanding the traditional two-dimensional path planning, can more easily cross time-consuming barriers in the two-dimensional A-x algorithm path planning on one hand and greatly save time and energy on the other hand, and can better meet the problem that the two-dimensional path planning cannot be realized because a target point in an unmanned workshop is not on the ground. By introducing the weight coefficient of h (n), the running speed of the algorithm is greatly increased, and the working efficiency of the bridge crane is further improved; and a cubic spline interpolation method is adopted for path optimization, so that the running path is smoother.
Claims (10)
1. A three-dimensional path planning method for a bridge crane is characterized by comprising the following steps:
s1, collecting and processing the working environment information of the bridge crane through a signal collecting and processing unit;
s2, generating a three-dimensional grid map from the environment information, and identifying the obstacles and the obstacle-free grids by different colors;
s3, searching out an optimal path by adopting a three-dimensional A-star algorithm according to the three-dimensional grid map;
and S4, optimizing the generated operation path by adopting an interpolation method.
2. The three-dimensional path planning method for the bridge crane according to claim 1, wherein the three-dimensional path planning method comprises the following steps: and step S1, the signal acquisition processing unit is configured to acquire and process an environment image of a working area of the bridge crane in real time, and create an environment map according to the environment image.
3. The three-dimensional path planning method for the bridge crane according to claim 1, wherein the three-dimensional path planning method comprises the following steps: in step S2, the generating of the three-dimensional grid map from the environment information includes: and (3) carrying out regularization treatment on the irregular barrier by adopting a three-dimensional grid method to form a standardized three-dimensional grid, and ensuring that each three-dimensional grid has the same value.
4. The three-dimensional path planning method for the bridge crane according to claim 1, wherein the three-dimensional path planning method comprises the following steps: the step of identifying the obstacle and non-obstacle grids in different colors in step S2 is: by performing binarization processing on the stereoscopic grid map, the threshold value of the area without obstacles is set to 1, the threshold value with obstacles is set to 0 by white display, and the threshold value is displayed by black.
5. The three-dimensional path planning method for the bridge crane according to claim 1, wherein the three-dimensional path planning method comprises the following steps: the step of searching for the optimal path by using the three-dimensional a-x algorithm in step S3 includes the following steps:
s31, determining a starting point and a target point, creating an open table and a close table, and initializing to be empty;
s32, adding the starting point into the open table, and setting the starting point as a father node;
s33, searching feasible child nodes around the father node, and adding the feasible child nodes into an open table;
s34, removing the father node from the open table, adding the father node into the close table, and calculating the cost value of each child node in the open table according to an evaluation function f (n);
s35, selecting the child node with the minimum cost value in the open table as a father node of the next expansion, and expanding;
s36, judging whether the child node is a target point; if so, proceed to the next step, if not return to S33, continue the loop;
s37, after finding the target point, backtracking from the target point to the starting point in a close table to obtain the shortest path;
s38, the three-dimensional a algorithm ends.
6. The three-dimensional path planning method for the bridge crane according to claim 5, wherein the three-dimensional path planning method comprises the following steps: the determination of the start point and the target point in step S31 is: and respectively selecting the running starting point and the target point of the bridge crane as the starting point and the end point of the path search.
7. The three-dimensional path planning method for the bridge crane according to claim 5, wherein the three-dimensional path planning method comprises the following steps: the step of searching for feasible child nodes around the parent node in step S33 is: selecting a central point of each three-dimensional grid in a three-dimensional grid map as an expandable node, expanding adjacent nodes from a search starting point in the three-dimensional grid map to determine a next node, and setting the number of the adjacent nodes in an expansion domain around a path search father node to be six, namely front, back, left, right, up and down.
8. The three-dimensional path planning method for the bridge crane according to claim 5, wherein the three-dimensional path planning method comprises the following steps: in step S34, the evaluation function f (n) is a cost function f (n), and its expression is:
f(n)＝g(n)+w(n)*h(n)
h(n)＝(abs(n.x-goal.x)+abs(n.y-goal.y)+abs(n.z-goal.z)
where (n) is a cost function of node n, g (n) is an actual cost from an origin to node n, h (n) is an estimated cost from node n to a destination, w (n) is a weight coefficient of h (n) in a dynamic scaling algorithm, where w (n) > 1, w (n) is greater and closer to the BFS algorithm, and w (n) is relatively smaller and closer to the Dijkstra algorithm; n.x is the abscissa of the node, n.y is the ordinate of the node, n.z is the ordinate of the node, good.x is the abscissa of the target point, good.y is the ordinate of the target point, and good.z is the ordinate of the target point.
9. The three-dimensional path planning method for the bridge crane according to claim 5, wherein the three-dimensional path planning method comprises the following steps: the step S35 of selecting the child node with the minimum cost value in the open table as the parent node of the next expansion refers to: when the cost functions f of a plurality of adjacent nodes are equal, setting priorities for the adjacent nodes, wherein the priority is the highest priority closest to the target point, and the priorities of the adjacent nodes are sequentially arranged according to anticlockwise rotation.
10. The three-dimensional path planning method for the bridge crane according to claim 1, wherein the three-dimensional path planning method comprises the following steps: in step S4, the generated operation path is optimized by using an interpolation method, specifically: firstly, determining an initial point and a target point, then carrying out coordinate processing on all nodes, and carrying out path optimization by adopting cubic spline interpolation; if the optimized path interferes with the obstacle, node supplementation can be carried out, constraint is increased, and path optimization is carried out again until the optimization is successful.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976535A (en) * | 2023-06-27 | 2023-10-31 | 上海师范大学 | Path planning algorithm based on fusion of few obstacle sides and steering cost |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491070A (en) * | 2017-08-31 | 2017-12-19 | 成都通甲优博科技有限责任公司 | A kind of method for planning path for mobile robot and device |
| CN110146085A (en) * | 2019-05-30 | 2019-08-20 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
| CN113156886A (en) * | 2021-04-30 | 2021-07-23 | 南京理工大学 | Intelligent logistics path planning method and system |
-
2021
- 2021-12-06 CN CN202111472533.6A patent/CN114200933A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491070A (en) * | 2017-08-31 | 2017-12-19 | 成都通甲优博科技有限责任公司 | A kind of method for planning path for mobile robot and device |
| CN110146085A (en) * | 2019-05-30 | 2019-08-20 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
| CN113156886A (en) * | 2021-04-30 | 2021-07-23 | 南京理工大学 | Intelligent logistics path planning method and system |
Non-Patent Citations (2)
| Title |
|---|
| 李敏: "桥式起重机吊装避障路径规划方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, pages 1 - 45 * |
| 龚源: "汽车起重机虚拟吊装环境下的路径规划算法", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, pages 1 - 44 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976535A (en) * | 2023-06-27 | 2023-10-31 | 上海师范大学 | Path planning algorithm based on fusion of few obstacle sides and steering cost |
| CN116976535B (en) * | 2023-06-27 | 2024-05-17 | 上海师范大学 | Path planning method based on fusion of few obstacle sides and steering cost |
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Application publication date: 20220318 |