CN113901611A - Tower crane lifting path planning method and device improved based on A-x algorithm - Google Patents
Tower crane lifting path planning method and device improved based on A-x algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
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- B66C13/48—Automatic control of crane drives for producing a single or repeated working cycle; Programme control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C23/00—Cranes comprising essentially a beam, boom, or triangular structure acting as a cantilever and mounted for translatory of swinging movements in vertical or horizontal planes or a combination of such movements, e.g. jib-cranes, derricks, tower cranes
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Abstract
The invention relates to the technical field of automatic control of tower cranes, and particularly discloses a method and a device for planning a lifting path of a tower crane improved based on an A-x algorithm, wherein the method comprises the following steps: establishing a three-dimensional grid model of a construction site under the column coordinates, and generating a grid node set; generating an obstacle point set according to obstacles in a construction site and a grid node set; acquiring coordinates of a lifting starting point and coordinates of a lifting end point under the column coordinates; planning a lifting path from the lifting starting point to the lifting end point according to the lifting starting point, the lifting end point, the grid node set, the obstacle point set and a preset lifting path planning strategy. The method optimizes a set data structure, greatly shortens calculation time, improves path planning efficiency, avoids the problem of visual blind areas in manual driving by generating an obstacle point set, enables the planned hoisting path to better accord with the working scene and hoisting logic of the tower crane by adopting a specific heuristic function, and improves hoisting safety and efficiency.
Description
Technical Field
The invention relates to the technical field of automatic control of tower cranes, in particular to a method and a device for planning a lifting path of a tower crane improved based on an A-x algorithm.
Background
The tower crane is a key mechanical device in building work, is visible everywhere in a tower crane in a building construction place, and can effectively save manpower, reduce construction cost and improve construction progress. However, the position of the tower crane cab is high, the visual field of a tower crane operator is greatly limited, buildings and obstacles exist in the field to shield, the blind lifting and the mountain-separating lifting are frequently caused, the driver can work only by combining the instructions of ground personnel and depending on experience, the blindness and the operation difficulty are high, certain potential safety hazards exist, meanwhile, the tower crane driver repeatedly works in a narrow high-altitude cab for a long time, fatigue easily occurs, and the safety production of the lifting operation of the tower crane is greatly influenced. In order to solve the problem, in the prior art, the operation monitoring system of the tower crane is mainly arranged, and the auxiliary system provides assistance for a driver and reduces the visual blind area by acquiring the operation parameters and the video images of the tower crane. However, the tower crane driving is greatly influenced by human factors, and the problem of fatigue driving of a driver still exists. Therefore, the unmanned tower crane capable of being automatically driven is an important direction for the research and development of the current novel tower crane.
The invention patent applications CN110182696A, CN110482409A and CN109610850A propose using SLAM algorithm, fast search random tree (RRT) algorithm and ant colony algorithm to search for the lifting path, respectively. In addition, the a-algorithm is also an algorithm for realizing path planning, but the conventional a-algorithm is used for path planning in a tower crane working scene, so that the problems of low algorithm efficiency, low hoisting height and the like exist, and the practicability and safety need to be improved.
Therefore, there is a need to find a new technical solution to solve the above problems.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method and a device for planning a hoisting path of a tower crane improved based on an A-x algorithm.
The invention discloses an improved tower crane lifting path planning method based on an A-x algorithm, which comprises the following steps:
establishing a three-dimensional grid model of a construction site under the column coordinates, and generating a grid node set;
generating an obstacle point set U according to obstacles and a grid node set in a construction site0；
Obtaining the coordinates of a lifting starting point S and the coordinates of a lifting end point G under the column coordinates, wherein the coordinates are respectively (r)S,θS,hS) And (r)G,θG,hG)；
According to a lifting starting point S, a lifting end point G, a grid node set and an obstacle point set U0Planning a lifting path from a lifting starting point S to a lifting end point G by a preset lifting path planning strategy;
wherein, the handling route planning strategy includes:
s101: establishing an reachable point set open list and a no-attention point set close list;
s102: putting a lifting starting point S into a reachable point set open list, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node of the lifting starting point S;
s103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Total cost f (n) of node n is g (n) + h (n), g (n) is the actual cost of node n, h (n) is the heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a preset safe height value which needs to be ensured before the horizontal movement with large amplitude;
s104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to an obstacle point set U0Or no focus set close list; wherein the coordinate of the node m is (r)m,θm,hm) (ii) a If not, the user can not select the specific application,
s105: judging whether the node m is already in the reachable point set open list or not;
s106: if the node m is not in the reachable point set open list, adding the node m to the reachable point set open list, and setting the node n as a parent node of the node m, and calculating the total cost f (m) ═ g (m) + h (m), wherein g (m) represents the actual cost of moving from the lifting starting point S to the node m via the parent node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hG|；
S107: if the node m is already in the reachable point set open list, the cost l (m, n) ═ g (n) +| r for the node m from the lifting starting point S via the non-father node n is calculatedm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m);
after traversing all the adjacent nodes m of the node n through S104 to S107, executing S108;
s108: adding the node n into a close list which does not need to pay attention to the point set;
repeating S103 to S108 until the node selected from the reachable point set open list is a lifting end point G;
s109: and (5) backtracking along the father node of each node from the lifting end point G until the lifting start point S, and sequentially connecting the backtracking nodes, namely the lifting path.
Further, establishing a three-dimensional grid model of the construction site under the column coordinates and generating a grid node set, including:
equally dividing an angle coordinate theta into a parts by a set step length by taking a central axis of a tower body of the tower crane as a longitudinal axis of a column coordinate;
converting the angle coordinates theta of all the nodes into integers from 0 to a-1;
and taking the angle coordinate of the node as an index of the primary list, and taking the radius coordinate r and the height coordinate h of the node as a secondary list.
Further, the step length is set to 1 °, and the angular coordinate θ is equally divided into 360 parts.
Further, in S104: judging whether the node m belongs to the barrier point set or the close list without the attention point set, including:
angular coordinate theta of node mmIndexing in a first-level list in a barrier point set or a close list without attention to a point set to find a corresponding second-level list;
find the corresponding (r) in the corresponding secondary listm,hm)。
Further, establish the three-dimensional grid model of construction site under the post coordinate, and generate the net node set, still include:
establishing a three-dimensional model of a construction site under rectangular coordinates;
and converting the three-dimensional model into a three-dimensional grid model, and determining a grid node set with set plane grid precision.
Further, a weight function k1The expression of (a) is:
d (n, S) represents the Euclidean distance between the node n and the lifting starting point S, d (n, G) represents the Euclidean distance between the node n and the lifting end point G, and rho is a preset radius range.
Further, a weight function k2The expression of (a) is:
wherein, lambda is a constant which is more than d (S, G) by one order of magnitude, and d (S, G) represents the Euclidean distance between the lifting starting point S and the lifting end point G.
Further, a weight function k3The expression of (a) is:
weight function k4Equal to the constant 1.
Further, the safety height value is:
hmid＝max(hS,hG)+(hmax-max(hS,hG))/2；
wherein h ismaxThe maximum height allowed to be lifted by the tower crane.
The invention also comprises a tower crane lifting path planning device improved based on the A-x algorithm, wherein the tower crane lifting path planning device comprises a three-dimensional grid model establishing module, an obstacle point set generating module, a lifting start-end point coordinate obtaining module, a lifting path planning module and a lifting control module; wherein:
the three-dimensional grid model building module is connected with the obstacle point set generating module and the lifting path planning module and used for building a three-dimensional grid model of a construction site under a column coordinate and generating a grid node set;
the barrier point set generation module is connected with the three-dimensional grid model building module and the lifting path planning module and is used for generating a rootGenerating an obstacle point set U according to obstacles and a grid node set in a construction site0；
And the hoisting start-end point coordinate acquisition module is connected with the hoisting path planning module and is used for acquiring the coordinates of a hoisting start point S and the coordinates of a hoisting end point G under the column coordinates, wherein the coordinates are respectively (r)S,θS,hS) And (r)G,θG,hG)；
The lifting path planning module is connected with the three-dimensional grid model establishing module, the obstacle point set generating module, the lifting start and end point coordinate obtaining module and the lifting control module and used for obtaining the coordinates of the lifting start point S, the lifting end point G, the grid node set and the obstacle point set U according to the lifting start point S, the lifting end point G, the grid node set and the obstacle point set U0Planning a lifting path from a lifting starting point S to a lifting end point G by a preset lifting path planning strategy; the hoisting path planning strategy comprises the following steps: s101: establishing an reachable point set open list and a no-attention point set close list; s102: putting a lifting starting point S into a reachable point set open list, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node of the lifting starting point S; s103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Total cost f (n) of node n is g (n) + h (n), g (n) is the actual cost of node n, h (n) is the heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a preset safe height value which needs to be ensured before the horizontal movement with large amplitude; s104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to an obstacle point set or a close list without paying attention to the point set; wherein the coordinate of the node m is (r)m,θm,hm) (ii) a If not, S105: judging whether the node m is already in the reachable point set open list or not; s106: if node m is not reachableIn the point set open list, the node m is added into the reachable point set open list, the node n is set as a father node of the node m, and the total cost f (m) ═ g (m) + h (m) of the node m is calculated, wherein g (m) represents the actual cost of moving from the lifting starting point S to the node m through the father node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hGL, |; s107: if the node m is already in the reachable point set open list, the cost l (m, n) ═ g (n) +| r for the node m from the lifting starting point S via the non-father node n is calculatedm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m); after traversing all the adjacent nodes m of the node n through S104 to S107, executing S108; s108: adding the node n into a close list which does not need to pay attention to the point set; repeating S103 to S108 until the node selected from the reachable point set open list is a lifting end point G; s109: backtracking along a father node of each node from a lifting end point G until a lifting start point S, and sequentially connecting backtracking nodes to form a lifting path;
and the hoisting control module is connected with the hoisting path planning module and is used for generating a tower crane operation instruction according to the hoisting path.
The method and the device for planning the lifting path of the tower crane improved based on the A-star algorithm replace the mode of purely depending on manual driving, and a three-dimensional grid model of a construction site established by column coordinates is used as the basis for planning the lifting path, so that an aggregate data structure is optimized, the calculation time is greatly shortened, and the efficiency of path planning is improved; by generating the barrier point set, the problem of visual blind areas existing in manual driving is avoided, and related nodes can be avoided in the path planning process, so that collision between articles and barriers in the hoisting process is avoided; by adopting a specific heuristic function, the planned hoisting path better conforms to the working scene and the hoisting logic of the tower crane, the safety of the hoisting path is improved, the hoisting efficiency is higher, and the time and the labor are saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart (one) of steps of a method for planning a lifting path of a tower crane improved based on an a-x algorithm according to an embodiment of the present invention;
fig. 2 is a flow chart (ii) of steps of a method for planning a lifting path of a tower crane improved based on an a-x algorithm according to an embodiment of the present invention;
fig. 3 is a structural composition diagram of a tower crane swing path planning device improved based on the a-x algorithm according to an embodiment of the present invention;
fig. 4 is a structural composition diagram of a tower crane swing path planning system improved based on the a-x algorithm according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the working scene of the tower crane, because the area condition close to the ground or the obstacle is complex, people and machines often work or irregular obstacles exist, and the like, for the sake of safety, a tower crane driver generally lifts a hoisted object to a certain height when driving, and starts the operations of amplitude variation and rotation after the hoisted object is away from the ground and the obstacle by a certain distance, so that the hoisted object is prevented from horizontally moving to a large extent in the area with equal distance to the obstacle on the ground. Similarly, when the crane is lowered, the driver moves the weight to a position right above the target point, and then slowly lowers the crane.
The core of the algorithm is that a heuristic path searching algorithm is introduced, and a bootstrap program is led to tend to select nodes closer to an end point each time the node is selected, so that the program can quickly obtain a feasible path. Common heuristic functions in the a-algorithm are:
euclidean distance:
manhattan distance:
chebyshev distance:
in the formula, pi、qiAnd (i ═ 1, 2.., n) are coordinate values of the current node and the end point respectively. The distance is used as an heuristic function, the planned path is often a path from a starting point to a terminal point, and the situation that the path is attached to the ground and a barrier area moves horizontally to a large extent often occurs, so that the method adopts the heuristic function of the conventional A-x algorithm to plan the lifting path of the tower crane, the obtained path is not suitable for the working scene of the tower crane, and certain potential safety hazards exist.
Therefore, the present invention provides an embodiment of a method for planning a lifting path of a tower crane based on an a-x algorithm, as shown in fig. 1, including:
step S10: and establishing a three-dimensional grid model of the construction site under the column coordinates, and generating a grid node set.
To implement this step, a three-dimensional model of the construction site under rectangular coordinates may be established first, and the three-dimensional model may be established by combining oblique photography with an image analysis technique or three-dimensional laser scanning modeling, wherein the implementation process of combining oblique photography with an image analysis technique may include: (1) arranging a plurality of video monitoring cameras at the periphery of the building main body and the tower crane; the video monitoring camera is used for collecting field images around the building main body and the tower crane, and is fixedly installed, so that a shooting visual angle is always kept in the image collecting process. (2) Acquiring a field image by a video monitoring camera; (3) carrying out image analysis on a site image shot by a video monitoring camera to generate a three-dimensional model of a construction site under rectangular coordinates; extracting the tower crane, the building and the like shot in the field image, reducing the tower crane, the building and the like in proportion according to the position and the image size, and finally determining the three-dimensional model of the embodiment.
After a three-dimensional model of the construction site under the rectangular coordinate is established, the three-dimensional model is converted into a three-dimensional grid model, and a grid node set is determined according to the set plane grid precision (for example, less than or equal to 1 m). The three-dimensional mesh model of the present embodiment is built under the cylindrical coordinates, and may include: (1) equally dividing an angle coordinate theta into a parts by a set step length by taking a central axis of a tower body of the tower crane as a longitudinal axis of a column coordinate; (2) converting the angle coordinates theta of all the nodes into integers from 0 to a-1; (3) and taking the angle coordinate of the node as an index of the primary list, and taking the radius coordinate r and the height coordinate h of the node as a secondary list. Preferably, the step size is set to 1 °, the angle coordinate θ is equally divided into 360 parts, the angle coordinates θ of all the nodes are converted into integers between 0 and 359, and then the angle coordinate is used as an index of the first list, and the radius coordinate r and the height coordinate h of the node are stored in the second list. Namely:
step S20: generating an obstacle point set U according to obstacles and a grid node set in a construction site0。
In order to ensure the tower crane to perform lifting operationIn the process of operation, the lifted object does not collide with the obstacles in the construction site, and an obstacle point set U needs to be determined according to the grid node set, the positions, the sizes and the shapes of the obstacles in the construction site and the like0。
One skilled in the art can set a redundancy l to ensure securityextraThen, the obstacle points are collected into U0Redundancy addition range lextraAnd (3) removing from the grid node set, wherein the set formed by the residual nodes is a reachable point set open list, and the removed node set is a close list without paying attention to the point set.
Step S30: obtaining the coordinates of a lifting starting point S and the coordinates of a lifting end point G under the column coordinates, wherein the coordinates are respectively (r)S,θS,hS) And (r)G,θG,hG)。
The manner of acquiring the coordinates of the lifting starting point S and the lifting end point G under the column coordinates can be used for reference in the prior art, and will not be limited herein.
Step S40: according to a lifting starting point S, a lifting end point G, a grid node set and an obstacle point set U0And planning a preset hoisting path planning strategy to plan a hoisting path from the hoisting starting point S to the hoisting end point G.
As shown in fig. 2, the lifting path planning strategy includes:
s101: a reachable point set open list and a focus free list are established.
The reachable point set open list and the non-attention point set close list are provided by the grid node set, the barrier point set and the set redundancy l in the foregoing embodimentsextraIs established according to the basis.
S102: and putting the lifting starting point S into the reachable point set open list, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node.
S103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Node ofn total cost f (n) g (n) h (n), g (n) actual cost of node n, h (n) heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a safety height value which needs to be ensured before the horizontal movement with a preset large amplitude.
Specifically, the weight function k in this embodiment1Mainly used for controlling the lifting, the path can firstly move upwards to the target height hmidWeight function k1The expression of (a) is:
d (n, S) represents the Euclidean distance between the node n and the lifting starting point S, d (n, G) represents the Euclidean distance between the node n and the lifting end point G, and rho is a preset radius range.
Weight function k in this embodiment2Mainly used for integrally improving the height h of the target near the starting pointmidThe cost of the following nodes is ensured, the nodes outside the region can be successfully searched after being hoisted to the target height, and if lambda is a constant which is at least one order of magnitude larger than d (S, G), the weight function k is2The expression of (a) is:
wherein d (S, G) represents the Euclidean distance between the lifting starting point S and the lifting end point G.
Weight function k in this embodiment3Mainly used for controlling horizontal movement towards the direction of a hoisting terminal G, and a weight function k3The expression of (a) is:
right letter in this embodimentNumber k4Mainly used for controlling the vertical movement towards the direction of a hoisting terminal G, and a weight function k4May take the constant 1.
Safety height hmidAccording to the height of the lifting starting point S and the lifting end point G and the maximum height h allowed to be lifted by the tower cranemaxComprehensively determining:
the safety height value is: h ismid＝max(hS,hG)+(hmax-max(hS,hG))/2。
S104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to an obstacle point set U0Or no focus set close list; wherein the coordinate of the node m is (r)m,θm,hm)。
This step determines whether node m belongs to the set of obstacle points or a close list without paying attention to the set of points, and the coordinate (r) of node m is required to be determinedm,θm,hm) And obstacle point set U0And comparing the coordinates of each point in the close list without paying attention to the point set one by one, and when the coordinates of the obstacle point set U are in the close list, comparing the coordinates of each point in the close list with the coordinates of each point in the close list0And when the close list of the point set is not needed to be paid attention to, the comparison times are greatly increased, so that the comparison process is slowed down, and the efficiency of planning the path is reduced. Especially for the obstacle point set U0Because the tower crane work site is large, in order to ensure the accuracy of the tower crane path, the site is divided into space grids with the side length of each unit not more than 1m according to the previous steps, so that the number of the obstacle points is at least hundreds of thousands, and a large amount of comparison work is generated.
To this end, embodiments of the present invention take the form of the aforementioned nested list, comprising: angular coordinate theta of node mmIndexing in a first-level list in a barrier point set or a close list without attention to a point set to find a corresponding second-level list; find the corresponding (r) in the corresponding secondary listm,hm). Whether the query node m belongs to the barrier point set U or not0Or when the focus set close list is not needed, only the angle coordinate theta is neededmAs an index, the thetamWhether there is a correspondence of (r) in the second listm,hm) The coordinates areBy adopting the method, most elements in the list can be directly excluded during comparison, and the judgment times are greatly reduced. Similarly, the radius coordinate r or the height coordinate h can be used as an index, but in a tower crane working scene, the angle coordinate θ can often contain more integers, so that the angle is more recommended to be used as the index.
If the node m does not belong to the barrier point set U0Or without the focus set close list, S105 is executed.
S105: it is determined whether node m is already in the reachable point set open list.
S106: if the node m is not in the reachable point set open list, adding the node m to the reachable point set open list, and setting the node n as a parent node of the node m, and calculating the total cost f (m) ═ g (m) + h (m), wherein g (m) represents the actual cost of moving from the lifting starting point S to the node m via the parent node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hG|。
S107: if the node m is already in the reachable point set open list, the cost l (m, n) ═ g (n) +| r for the node m from the lifting starting point S via the non-father node n is calculatedm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m);
the node n has a plurality of adjacent nodes m, so after traversing all the adjacent nodes m of the node n through S104 to S107, executing S108;
s108: adding the node n into a close list which does not need to pay attention to the point set;
repeating S103 to S108 until the node selected from the reachable point set open list is a lifting end point G;
s109: and (5) backtracking along the father node of each node from the lifting end point G until the lifting start point S, and sequentially connecting the backtracking nodes, namely the lifting path.
After the lifting path is determined by the embodiment of the invention, the lifting article is operated according to the overhead lifting path.
The invention also comprises a tower crane lifting path planning device improved based on the A-x algorithm, as shown in fig. 3, the tower crane lifting path planning device 10 comprises a three-dimensional grid model establishing module 101, an obstacle point set generating module 102, a lifting start and end point coordinate obtaining module 103, a lifting path planning module 104 and a lifting control module 105; wherein:
the three-dimensional grid model building module 101 is connected with the obstacle point set generating module 102 and the lifting path planning module 104, and the three-dimensional grid model building module 101 is used for building a three-dimensional grid model of a construction site under a column coordinate and generating a grid node set;
the obstacle point set generation module 102 is connected with the three-dimensional grid model building module 101 and the lifting path planning module 104, and the obstacle point set generation module 102 is used for generating an obstacle point set U according to obstacles and grid node sets in a construction site0；
A lifting start and end point coordinate obtaining module 103 connected with the lifting path planning module 104, wherein the lifting start and end point coordinate obtaining module 103 is used for obtaining coordinates of a lifting start point S and coordinates of a lifting end point G under the column coordinates, which are respectively (r)S,θS,hS) And (r)G,θG,hG)；
A lifting path planning module 104 connected with the three-dimensional grid model establishing module 101, the obstacle point set generating module 102, the lifting start and end point coordinate obtaining module 103 and the lifting control module 105, wherein the lifting path planning module 104 is used for establishing a lifting start point S, a lifting end point G, a grid node set and an obstacle point set U according to the lifting start point S, the lifting end point G, the grid node set and the obstacle point set U0And planning a preset hoisting path planning strategy to plan a hoisting path from the hoisting starting point S to the hoisting end point G.
The hoisting path planning strategy comprises the following steps:
s101: establishing an reachable point set open list and a no-attention point set close list;
s102: putting a lifting starting point S into a reachable point set open list, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node of the lifting starting point S;
s103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Total cost f (n) of node n is g (n) + h (n), g (n) is the actual cost of node n, h (n) is the heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a preset safe height value which needs to be ensured before the horizontal movement with large amplitude;
s104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to an obstacle point set U0Or no focus set close list; wherein the coordinate of the node m is (r)m,θm,hm) (ii) a If not, the user can not select the specific application,
s105: judging whether the node m is already in the reachable point set open list or not;
s106: if the node m is not in the reachable point set open list, adding the node m to the reachable point set open list, and setting the node n as a parent node of the node m, and calculating the total cost f (m) ═ g (m) + h (m), wherein g (m) represents the actual cost of moving from the lifting starting point S to the node m via the parent node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hG|；
S107: if the node m is already in the reachable point set open list, the cost l (m, n) ═ g (n) +| r for the node m from the lifting starting point S via the non-father node n is calculatedm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m);
after traversing all the adjacent nodes m of the node n through S104 to S107, executing S108;
s108: adding the node n into a close list which does not need to pay attention to the point set;
repeating S103 to S108 until the node selected from the reachable point set open list is a lifting end point G;
s109: and (5) backtracking along the father node of each node from the lifting end point G until the lifting start point S, and sequentially connecting the backtracking nodes, namely the lifting path.
And the hoisting control module 105 is connected with the hoisting path planning module 104, and the hoisting control module 105 is used for generating a tower crane operation instruction according to the hoisting path.
For the specific implementation process of the steps in the path planning strategy, reference may be made to the description in the foregoing method embodiments, and details are not described here.
The invention also provides an improved tower crane lifting path planning system based on the A-x algorithm, as shown in fig. 4, the system comprises a tower crane, a tower crane lifting path planning device 10, a driving device 20 and a braking device 30, the driving device 20 and the braking device 30 are in communication connection with the tower crane lifting path planning device 10, wherein: the tower crane lifting path planning device 10 is used for planning a lifting path from a lifting starting point S to a lifting end point G and generating a tower crane operation instruction according to the lifting path; the driving device 20 is mounted on the tower crane and used for driving the tower crane to carry out lifting operation according to the operation instruction of the tower crane; the braking device 30 is mounted on the tower crane and used for braking the tower crane in operation according to the operation instruction of the tower crane.
The method and the device for planning the lifting path of the tower crane improved based on the A-star algorithm replace the mode of purely depending on manual driving, and a three-dimensional grid model of a construction site established by column coordinates is used as the basis for planning the lifting path, so that an aggregate data structure is optimized, the calculation time is greatly shortened, and the efficiency of path planning is improved; by generating the barrier point set, the problem of visual blind areas existing in manual driving is avoided, and related nodes can be avoided in the path planning process, so that collision between articles and barriers in the hoisting process is avoided; by adopting a specific heuristic function, the planned hoisting path better conforms to the working scene and the hoisting logic of the tower crane, the safety of the hoisting path is improved, the hoisting efficiency is higher, and the time and the labor are saved.
The present invention has been further described with reference to specific embodiments, but it should be understood that the detailed description should not be construed as limiting the spirit and scope of the present invention, and various modifications made to the above-described embodiments by those of ordinary skill in the art after reading this specification are within the scope of the present invention.
Claims (10)
1. A tower crane lifting path planning method based on A-x algorithm improvement is characterized by comprising the following steps:
establishing a three-dimensional grid model of a construction site under the column coordinates, and generating a grid node set;
generating an obstacle point set U according to obstacles in a construction site and the grid node set0；
Obtaining the coordinates of a lifting starting point S and the coordinates of a lifting end point G under the column coordinates, wherein the coordinates are respectively (r)S,θS,hS) And (r)G,θG,hG)；
According to the lifting starting point S, the lifting end point G, the grid node set and the obstacle point set U0Planning a lifting path from a lifting starting point S to a lifting end point G by a preset lifting path planning strategy;
wherein, the lifting path planning strategy comprises:
s101: establishing an reachable point set open list and a no-attention point set close list;
s102: putting the lifting starting point S into the reachable point set openlist, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node of the lifting starting point S;
s103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Total cost f (n) of node n is g (n) + h (n), g (n) is the actual cost of node n, h (n) is the heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a preset safe height value which needs to be ensured before the horizontal movement with large amplitude;
s104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to the barrier point set or the close list of the point set without attention; wherein the coordinate of the node m is (r)m,θm,hm) (ii) a If not, the user can not select the specific application,
s105: judging whether the node m is already in the reachable point set open list or not;
s106: if the node m is not in the reachable point set openlist, adding the node m to the reachable point set openlist, and setting the node n as a parent node of the node m, and calculating the total cost f (m) ═ g (m) + h (m), wherein g (m) represents the actual cost of moving from the lifting starting point S to the node m via the parent node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hG|；
S107: if the node m is already in the reachable point set open list, calculating the distance from the lifting starting point S to the non-lifting starting point SThe cost of parent node n reaching node m, i (m, n) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m);
it is determined whether or not the above-described operations are performed on all the adjacent nodes m of the node n, and if yes, S108 is executed, and if no, the processes of steps S104 to S107 are performed on the other adjacent nodes m.
After all the adjacent nodes m of the node n are traversed through S104 to S107, S108 is executed;
s108: adding a node n into the focus-free set close list;
repeating S103 to S108 until the node selected from the reachable point set openlist is the lifting end point G;
s109: and (4) backtracking along the father node of each node from the lifting end point G to the lifting start point S, and sequentially connecting backtracking nodes to form a lifting path.
2. The improved tower crane lifting path planning method based on the a-x algorithm according to claim 1, wherein the building of the three-dimensional grid model of the construction site under the column coordinates and the generation of the grid node set comprise:
equally dividing an angle coordinate theta into a parts by a set step length by taking a central axis of a tower body of the tower crane as a longitudinal axis of a column coordinate;
converting the angle coordinates theta of all the nodes into integers from 0 to a-1;
and taking the angle coordinate of the node as an index of the primary list, and taking the radius coordinate r and the height coordinate h of the node as a secondary list.
3. The improved tower crane lifting path planning method based on the A-x algorithm as claimed in claim 2, wherein the set step is 1 degree, and the angle coordinate theta is equally divided into 360 parts.
4. The improved tower crane lifting path planning method based on the a-x algorithm according to claim 2, wherein in S104: judging whether the node m belongs to the barrier point set or the close list of the attention-free point set, including:
angular coordinate theta of node mmIndexing in a primary list in the barrier point set or the close list of the point set without attention to find a corresponding secondary list;
find the corresponding (r) in the corresponding secondary listm,hm)。
5. The improved tower crane lifting path planning method based on the a-x algorithm according to claim 2, wherein the method comprises the steps of building a three-dimensional grid model of a construction site under the column coordinates and generating a grid node set, and further comprises the following steps:
establishing a three-dimensional model of a construction site under rectangular coordinates;
and converting the three-dimensional model into the three-dimensional grid model, and determining the grid node set with the set plane grid precision.
6. The improved tower crane lifting path planning method based on A-algorithm as claimed in claim 1, wherein the weight function k is1The expression of (a) is:
d (n, S) represents the Euclidean distance between the node n and the lifting starting point S, d (n, G) represents the Euclidean distance between the node n and the lifting end point G, and rho is a preset radius range.
7. The improved tower crane lifting path planning method based on A-algorithm as claimed in claim 6, wherein the weight function k is2The expression of (a) is:
wherein, lambda is a constant which is more than d (S, G) by one order of magnitude, and d (S, G) represents the Euclidean distance between the lifting starting point S and the lifting end point G.
8. The improved tower crane lifting path planning method based on A-algorithm as claimed in claim 7, wherein the weight function k is3The expression of (a) is:
weight function k4Equal to the constant 1.
9. The improved tower crane lifting path planning method based on the a-x algorithm according to claim 1, wherein the safety height value is as follows:
hmid＝max(hS,hG)+(hmax-max(hS,hG))/2；
wherein h ismaxThe maximum height allowed to be lifted by the tower crane.
10. A tower crane lifting path planning device improved based on an A-x algorithm is characterized by comprising a three-dimensional grid model establishing module, an obstacle point set generating module, a lifting start-end point coordinate obtaining module, a lifting path planning module and a lifting control module; wherein:
the three-dimensional grid model building module is connected with the obstacle point set generating module and the lifting path planning module, and is used for building a three-dimensional grid model of a construction site under a column coordinate and generating a grid node set;
the obstacle point set generation module is connected with the three-dimensional grid model building module and the lifting path planning module, and is used for generating an obstacle point set U according to obstacles in a construction site and the grid node set0；
The lifting start and end point coordinate acquisition module is connected with the lifting path planning module, and is used for acquiring coordinates of a lifting start point S and coordinates of a lifting end point G under the column coordinates, wherein the coordinates are respectively (r)S,θS,hS) And (r)G,θG,hG)；
The lifting path planning module is connected with the three-dimensional grid model establishing module, the obstacle point set generating module, the lifting start and end point coordinate obtaining module and the lifting control module, and is used for carrying out lifting path planning according to the lifting start point S, the lifting end point G, the grid node set and the obstacle point set U0Planning a lifting path from a lifting starting point S to a lifting end point G by a preset lifting path planning strategy; the hoisting path planning strategy comprises the following steps: s101: establishing an reachable point set open list and a point set closed list without attention; s102: putting the lifting starting point S into the reachable point set openlist, and making the total cost f (S) and the actual cost g (S) of the lifting starting point S be 0 and making the father node be the father node of the lifting starting point S; s103: selecting a node n with the minimum total cost f (n) from the reachable point set open list, and deleting the node n from the reachable point set open list; wherein the coordinate of the node n is (r)n,θn,hn) Total cost f (n) of node n is g (n) + h (n), g (n) is the actual cost of node n, h (n) is the heuristic function of node n, and h (n) k1(hmid-hn)+k2+k3(|rn-rG|+|θn-θG|)+k4|hn-hG|，k1,k2,k3,k4Is a predetermined weight function, hmidThe height value is a preset safe height value which needs to be ensured before the horizontal movement with large amplitude; s104: acquiring a node m adjacent to the node n, and judging whether the node m belongs to the barrier point set or the close list of the point set without attention; wherein the coordinate of the node m is (r)m,θm,hm) (ii) a If not, S105: judging whether the node m is already in the reachable point set openlist or not; s106: if node m is not at the reachable pointIn the set openlist, adding the node m into the reachable point set openlist, and setting the node n as the father node of the node m, and calculating the total cost f (m) ═ g (m) + h (m), where g (m) represents the actual cost of moving from the lifting starting point S to the node m via the father node n thereof, and g (m) ═ g (n) +| rm-rn|+rn·|θm-θn|+|hm-hnL, < h > (m) is the heuristic function of node m, and h (m) < k1(hmid-hm)+k2+k3(|rm-rG|+|θm-θG|)+k4|hm-hGL, |; s107: if the node m is already in the reachable point set open list, the cost l (m, n) ═ g (n) +| r for the node m from the lifting starting point S via the non-father node n is calculatedm-rn|+rn·|θm-θn|+|hm-hnComparing the cost l (m, n) with the actual cost g (m), if l (m, n)<g (m), setting the node n as a parent node of the node m, and making the actual cost g (m) l (m, n), and updating the total cost f (m); after traversing all the adjacent nodes m of the node n through S104 to S107, executing S108; s108: adding a node n into the focus-free set close list; repeating S103 to S108 until the node selected from the reachable point set open list is the lifting end point G; s109: backtracking along father nodes of each node from the lifting end point G to the lifting start point S, and sequentially connecting backtracking nodes to form a lifting path;
the lifting control module is connected with the lifting path planning module and is used for generating a tower crane operation instruction according to the lifting path.
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| CN202111190913.0A CN113901611A (en) | 2021-10-13 | 2021-10-13 | Tower crane lifting path planning method and device improved based on A-x algorithm |
| PCT/CN2021/138684 WO2022143193A1 (en) | 2020-12-31 | 2021-12-16 | Automatic hoisting method for tower crane |
| US17/920,039 US20230159308A1 (en) | 2020-12-31 | 2021-12-16 | Automatic hoisting and transporting method for tower crane |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578104A (en) * | 2023-07-14 | 2023-08-11 | 山东中建众力设备租赁有限公司 | Unmanned tower crane control method based on deep learning |
| WO2024021924A1 (en) * | 2022-07-29 | 2024-02-01 | 浙江三一装备有限公司 | Hoisting path planning model construction method, hoisting path planning method, and crane |
| CN117733308A (en) * | 2024-02-19 | 2024-03-22 | 浙江大学 | Ultrasonic welding robot path planning method and device |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024021924A1 (en) * | 2022-07-29 | 2024-02-01 | 浙江三一装备有限公司 | Hoisting path planning model construction method, hoisting path planning method, and crane |
| CN116578104A (en) * | 2023-07-14 | 2023-08-11 | 山东中建众力设备租赁有限公司 | Unmanned tower crane control method based on deep learning |
| CN116578104B (en) * | 2023-07-14 | 2023-09-19 | 山东中建众力设备租赁有限公司 | Unmanned tower crane control method based on deep learning |
| CN117733308A (en) * | 2024-02-19 | 2024-03-22 | 浙江大学 | Ultrasonic welding robot path planning method and device |
| CN117733308B (en) * | 2024-02-19 | 2024-05-17 | 浙江大学 | Ultrasonic welding robot path planning method and device |
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