CN103496632A - Cloud-computing-based crane three-dimensional simulation route planning method - Google Patents
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- CN103496632A CN103496632A CN201310426717.8A CN201310426717A CN103496632A CN 103496632 A CN103496632 A CN 103496632A CN 201310426717 A CN201310426717 A CN 201310426717A CN 103496632 A CN103496632 A CN 103496632A
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Abstract
The invention discloses a cloud-computing-based crane three-dimensional simulation route planning method. Cloud computing is applied in a three-dimensional hoisting simulation system, a computer is responsible for master node computing, and a plurality of computers are responsible for slave node computing, so that a cloud-computing-based three-dimensional hoisting simulation platform is formed. On the platform, with parallel computing advantages of cloud computing, slave node partition is performed on the master node according to degree of freedom of the master node, logically parallel cloud computing is formed among the slave nodes, and the computing real-time performance of the route planning method is ensured while the optimal route is obtained. The system routing speed is improved, the problem that the computing real-time performance during the three-dimensional hoisting simulation process of an automobile crane is bad caused by complicated scene is solved, and the method has an important practical value.
Description
Technical field
The present invention relates to three-dimensional hoisting simulation system, particularly a kind of car hosit three-dimensional artificial paths planning method based on cloud computing.
Background technology
Car hosit is the visual plant in modernization construction, and its lifting operation difficulty increases day by day, and the lifting accuracy requirement improves further, and the lifting environment becomes increasingly complex.Formulating rapidly and accurately Hoisting Program by hoisting simulation system, can effectively improve feasibility and the safety of lifting operation, is the successful key of lifting operation.
But be the authenticity of the three-dimensional hoisting simulation system of raising car hosit and the feasibility of Hoisting Program, the lifting scene construction is more and more meticulousr, the lifting function is more and more perfect, the method that function realizes becomes increasingly complex, this just causes the calculated amount of system to increase, calculate the time expand of cost, affect real-time simulation speed and the Hoisting Program output of system.
In recent years, cloud computing had obtained the fast speed development, and it can solve effectively because complicated calculations causes the problem that gauge pressure is large, was widely used in the every field such as safety, storage, education, game and operating mode emulation.
Summary of the invention
Technical matters to be solved by this invention is, for the long shortcoming of existing car hosit lifting Path Planning Technique simulation time, a kind of car hosit three-dimensional artificial paths planning method based on cloud computing is provided, improve three-dimensional hoisting simulation system and seek footpath speed, solve the poor problem of hoisting process calculating real-time that complex scene causes.
For solving the problems of the technologies described above, the technical solution adopted in the present invention is: a kind of car hosit three-dimensional artificial paths planning method based on cloud computing, and the method is:
1) parameter initialization: initialization automobile counterweight, lifting object weight, the combination of car hosit jib, car hosit rated load, final position information, start position information empty feasible path collection and optimal path collection information;
2) using the lifting object initial position as major joint;
3) judge whether major joint is the lifting terminal, if so, accepts optimal path collection information, jumps to 9); Otherwise, enter 4);
4) degree of freedom of mark major joint;
5) on the cloud platform, major joint is carried out from node division: major joint is divided according to the maximum degree of freedom of car hosit single step operation, each degree of freedom is divided into two direction of travel, the next coordinate position of each direction of travel is from node, the twice that is the major joint degree of freedom from the number of node;
6) upgrade the feasible path collection: judge whether each degree of freedom from node is zero, and the node that degree of freedom is zero is infeasible, rejects; The feasible path that non-vanishing node is saved in separately by degree of freedom is concentrated, and upgrades the feasible path collection;
7) upgrade the optimal path collection: select the feasible path after step 6) is upgraded to concentrate the element of path the shortest the major joint from the lifting object initial position to this iteration as the optimal path collection;
8) determine the major joint of next iteration: the non-vanishing major joint from node as next iteration using degree of freedom in step 6), return to step 3);
9) finish.
In described step 4), the degree of freedom of major joint is 1～3, and generally, the degree of freedom of major joint is 3.
As preferred version, in described step 6), from the degree of freedom of node, be 0～3.
Compared with prior art, the beneficial effect that the present invention has is: adopt heuristic search, the car hosit three-dimensional artificial paths planning method of design based on cloud computing, cloud computing is applied in three-dimensional hoisting simulation system, with the computing of a computing machine supervisor major joint, many computing machine supervisors are from the node computing, thus the three-dimensional hoisting simulation platform of formation based on cloud computing.On this platform, use the parallel computation advantage of cloud computing, major joint is undertaken from node division by its degree of freedom, each is from forming again cloud parallel computation in logic between node, when obtaining optimal path, the calculating real-time of paths planning method is guaranteed, improve system and sought footpath speed, solve the poor problem of car hosit hoisting simulation process computation real-time that complex scene causes, there is important practical value.
The accompanying drawing explanation
Fig. 1 is one embodiment of the invention method flow diagram;
Fig. 2 is that the node of one embodiment of the invention based on cloud computing calculates schematic diagram;
Fig. 3 is the path planning exemplary plot of one embodiment of the invention based on cloud computing.
The specific embodiment
As shown in Figure 1, one embodiment of the invention concrete steps are as follows:
(1) parameter initialization, as initialization automobile crane counterweight, lifting object weight, the combination of car hosit jib, car hosit rated load, final position information, start position information, empty feasible path collection and optimal path collection.
(2) using starting point as major joint, the maximum degree of freedom 3 that its degree of freedom is the car hosit single step operation.
(3) judge whether major joint is the lifting terminal, and if so, the path planning process finishes so, accepts optimal path collection information.Otherwise, continue following step.
(4) degree of freedom of mark major joint.In lifting, the degree of freedom of each node is 3 to the maximum, but, because the border of scene is conditional, causes the cartographic information bounded of emulation, and the degree of freedom of node may be different.
(5) major joint is carried out from node division.Major joint is divided according to the maximum degree of freedom of single step operation, each degree of freedom is divided into two direction of travel, and (car hosit has three kinds of operations: revolution, luffing and hoist, every kind of operation has the action on both direction), the next coordinate position of each direction of travel is from node, the twice that is therefore the major joint degree of freedom from the number of node.Concrete partition process as shown in Figure 2.
In Fig. 2,
m,
m + 1,
m+
f 1mean hoisting crane scenario node numbering,
n,
n + 1 means the iterations in crane hanging component path planning process,
f 1/ 2,
f 2,
f 3the degree of freedom that means hoisting crane when lifting object is positioned at major joint.The expression mode of major joint be (
m,
n,
f 1/ 2), wherein,
mfor master node number,
nfor iterations,
f 1/ 2 is degree of freedom, the twice that is the major joint degree of freedom from the number of node.The resource of distributing by cloud computing equity dispatching strategy, each calculates respectively action and state separately from node.If the degree of freedom of single step operation is 3, major joint just can be divided into 6 from node so.
(6) upgrade the feasible path collection.A position of each lifting object from the corresponding lifting of node scene, judge this degree of freedom from node, determines that whether it is feasible.If degree of freedom is non-vanishing, feasible from node, this is saved in to feasible path from node and concentrates, upgrade the feasible path collection.What the feasible path collection was stored is the difference lifting path from the lifting object initial position to major joint, and the nodal information difference comprised according to every paths is upgraded.
Degree of freedom is 3 to represent car hosit revolution, suspension arm variable-amplitude, hook lifting, and wherein car hosit, arm and suspension hook disturb due to the performance of car hosit own or external factor the situation that there will be motion to be obstructed, and cause the degree of freedom of node to reduce.Following 6 kinds of situations are specifically arranged: 1, the arm increasing degree reaches the limit (arm amplitude higher limit is 85 ° ~ 89 °, is specifically determined by the car hosit type) of car hosit mechanical technology restriction, and the arm amplitude can not rise; 2, the arm width that falls reaches the limit of car hosit mechanical technology restriction (arm amplitude lower limit is 3 ° ~ 7 ° and does not wait, specifically by the car hosit type, determined) or object on ground level or lifting object reach the border of scene, the arm amplitude can not descend; 3, suspension hook reaches the top (concrete numerical value is determined by the car hosit type) that car hosit allows arrival, and lifting object can not rise; 4, suspension hook reach bottom or lifting object on ground level, lifting object can not descend; 5, when bumping against, car hosit cw or turning anticlockwise and object cause path infeasible; 6, lifting object moves meeting and lifts non-hoisting object in scene and bumps and cause lifting infeasible on certain direction.Above-mentioned six kinds of situations can cause the degree of freedom of node to reduce.
(7) upgrade the optimal path collection.The optimal path collection is that feasible path is concentrated the major joint from the lifting object initial position to this iteration apart from the set of shortest path, may be one, also may be several.After upgrading each time the feasible path collection, the concentrated routing information of feasible path can change to some extent, therefore need to again upgrade the optimal path collection.
(8) determine the major joint of next iteration.Using the feasible major joint from node as next iteration, owing to using cloud computing technology, each is between node being concurrent operation, and therefore, major joint is a set of node.Return to step (3).
In this car hosit hoisting simulation paths planning method based on cloud computing, be the mode with the cloud parallel computation between node, start separately new iteration.Below the 3rd iteration in calculating by certain path planning be that example describes, iterative process is as shown in Figure 3.
Choosing the node that is numbered A is major joint, and its degree of freedom is 2, is expressed as (A, 3,2).Four of node A is B from node
1, B
2, B
3with B
4, degree of freedom is respectively 1,0,1,2, is expressed as respectively (B
1, 4,1), (B
2, 4,0), (B
3, 4,1) and (B
4, 4,2).Wherein, from Node B
2degree of freedom be 0, be unavailable node, the major joint of next iteration is B
1, B
3with B
4.These three become new major joint separately from node, in the mode of cloud parallel computation, start to generate new for node, and node serial number is: C
1, C
2, C
3, C
4, C
5, C
6.Carry out in this way iterative computation, can when reducing search node, obtain optimal path.After path planning finishes, the path in optimal path integrates, as shortest path, if there are many, can be selected arbitrarily one for optimum lifting path.
This method is applied in the three-dimensional hoisting simulation system based on cloud computing, and simulated effect is good.From the above, the method has realized the car hosit hoisting simulation paths planning method based on cloud computing on the cloud platform, many computing machines are responsible for major joint and from the node computing simultaneously, use the parallel computation advantage of cloud computing that major joint is undertaken from node division by its degree of freedom, and each is from forming cloud parallel computation in logic between node, when obtaining optimal path, the calculating real-time of paths planning method is guaranteed.
Claims (4)
1. the car hosit three-dimensional artificial paths planning method based on cloud computing, is characterized in that, the method is:
1) parameter initialization: initialization automobile crane counterweight, lifting object weight, the combination of car hosit jib, car hosit rated load, final position information, start position information empty feasible path collection and optimal path collection information;
2) using the lifting object initial position as major joint;
3) judge whether major joint is the lifting terminal, if so, accepts optimal path collection information, jumps to 9); Otherwise, enter 4);
4) degree of freedom of mark major joint;
5) on the cloud platform, major joint is carried out from node division: major joint is divided according to the maximum degree of freedom of car hosit single step operation, and each degree of freedom is divided into two direction of travel, and the next coordinate position of each direction of travel is from node;
6) upgrade the feasible path collection: judge whether each degree of freedom from node is zero, and the node that degree of freedom is zero is infeasible, rejects; By degree of freedom, non-vanishing node is saved in feasible path and concentrates, and upgrades the feasible path collection;
7) upgrade the optimal path collection: select feasible path after step 6) is upgraded to concentrate the element of the shortest path of distance the major joint from the lifting object initial position to this iteration as the optimal path collection;
8) determine the major joint of next iteration: the non-vanishing major joint from node as next iteration using degree of freedom in step 6), return to step 3);
9) finish.
2. the car hosit three-dimensional artificial paths planning method based on cloud computing according to claim 1, is characterized in that, in described step 4), the degree of freedom of major joint is 1～3.
3. the car hosit three-dimensional artificial paths planning method based on cloud computing according to claim 1, is characterized in that, in described step 6), from the degree of freedom of node, is 0～3.
4. the car hosit three-dimensional artificial paths planning method based on cloud computing according to claim 1, is characterized in that, in described step 5), and the twice that is the major joint degree of freedom from the number of node.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484510A (en) * | 2014-11-28 | 2015-04-01 | 大连理工大学 | Novel hoisting action planning method for crane |
| CN104528540A (en) * | 2014-12-01 | 2015-04-22 | 长安大学 | Real-time generation method of hoisting scheme of jib crane in vehicle controller and method thereof |
| CN106966298A (en) * | 2017-04-17 | 2017-07-21 | 山东建筑大学 | The intelligent hanging method of assembled architecture based on machine vision and system |
| CN108320047A (en) * | 2017-12-29 | 2018-07-24 | 中国建筑第八工程局有限公司 | A kind of selection method of the optimal travel route of crane based on BIM technology |
| CN112069698A (en) * | 2020-09-27 | 2020-12-11 | 中国化学工程第六建设有限公司 | Hoisting simulation construction method and system based on BIM |
| CN113911919A (en) * | 2021-09-14 | 2022-01-11 | 杭州大杰智能传动科技有限公司 | Intelligent tower crane material transportation control method and system based on stack model simulation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0881179A (en) * | 1994-09-12 | 1996-03-26 | Ishikawajima Harima Heavy Ind Co Ltd | Operating method for cable crane |
| JPH10330077A (en) * | 1997-05-30 | 1998-12-15 | Nkk Corp | Setting method for carrying route of crane |
| KR20060031143A (en) * | 2004-10-07 | 2006-04-12 | 주식회사 포스코 | Transfer methode of overhead crane for coil transfer |
| CN101665216A (en) * | 2009-09-29 | 2010-03-10 | 三一集团有限公司 | Control method of move track of crane container spreader, system and device |
| CN102040160A (en) * | 2010-08-30 | 2011-05-04 | 湖南中联重科专用车有限责任公司 | Method for controlling movement locus of hook of crane |
-
2013
- 2013-09-18 CN CN201310426717.8A patent/CN103496632B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0881179A (en) * | 1994-09-12 | 1996-03-26 | Ishikawajima Harima Heavy Ind Co Ltd | Operating method for cable crane |
| JPH10330077A (en) * | 1997-05-30 | 1998-12-15 | Nkk Corp | Setting method for carrying route of crane |
| KR20060031143A (en) * | 2004-10-07 | 2006-04-12 | 주식회사 포스코 | Transfer methode of overhead crane for coil transfer |
| CN101665216A (en) * | 2009-09-29 | 2010-03-10 | 三一集团有限公司 | Control method of move track of crane container spreader, system and device |
| CN102040160A (en) * | 2010-08-30 | 2011-05-04 | 湖南中联重科专用车有限责任公司 | Method for controlling movement locus of hook of crane |
Non-Patent Citations (4)
| Title |
|---|
| 安剑奇等: "基于空间二维映射的汽车起重机三维路径规划算法", 《第三十二届中国控制会议论文集》, 26 July 2013 (2013-07-26), pages 5982 - 5987 * |
| 李元春等: "随车吊机械臂的吊装轨迹规划方法", 《长春工业大学学报》, vol. 33, no. 5, 31 October 2012 (2012-10-31), pages 543 - 547 * |
| 李纪明: "基于动态解区间的操作臂路径规划寻优算法", 《机械设计》, vol. 25, no. 7, 31 July 2008 (2008-07-31), pages 28 - 30 * |
| 舒世龙等: "基于B/S架构和缓存设计的三维吊装仿真", 《第三十一届中国控制会议论文集》, 25 July 2012 (2012-07-25), pages 5524 - 5529 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484510A (en) * | 2014-11-28 | 2015-04-01 | 大连理工大学 | Novel hoisting action planning method for crane |
| CN104484510B (en) * | 2014-11-28 | 2017-06-23 | 大连理工大学 | A kind of new crane hanging component action planning method |
| CN104528540A (en) * | 2014-12-01 | 2015-04-22 | 长安大学 | Real-time generation method of hoisting scheme of jib crane in vehicle controller and method thereof |
| CN104528540B (en) * | 2014-12-01 | 2016-06-08 | 长安大学 | Hoisting Program Real-time Generation and system in arm derrick crane Vehicle Controller |
| CN106966298A (en) * | 2017-04-17 | 2017-07-21 | 山东建筑大学 | The intelligent hanging method of assembled architecture based on machine vision and system |
| CN106966298B (en) * | 2017-04-17 | 2018-08-14 | 山东建筑大学 | Assembled architecture intelligence hanging method based on machine vision and system |
| CN108320047A (en) * | 2017-12-29 | 2018-07-24 | 中国建筑第八工程局有限公司 | A kind of selection method of the optimal travel route of crane based on BIM technology |
| CN112069698A (en) * | 2020-09-27 | 2020-12-11 | 中国化学工程第六建设有限公司 | Hoisting simulation construction method and system based on BIM |
| CN112069698B (en) * | 2020-09-27 | 2024-04-19 | 中国化学工程第六建设有限公司 | BIM-based hoisting simulation construction method and system |
| CN113911919A (en) * | 2021-09-14 | 2022-01-11 | 杭州大杰智能传动科技有限公司 | Intelligent tower crane material transportation control method and system based on stack model simulation |
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