CN113778097A - Intelligent warehouse logistics robot path planning method for improving A-STAR algorithm through L-shaped path trend - Google Patents
Intelligent warehouse logistics robot path planning method for improving A-STAR algorithm through L-shaped path trend Download PDFInfo
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Abstract
The invention discloses an intelligent warehouse logistics robot path planning method for improving an A-STAR algorithm by an L-shaped path trend, which is characterized in that after an initial path is planned by the A-STAR algorithm, optimization is carried out through a path trend identifier according to the principle of enabling a local path to be consistent with a path trend formed by S, T as far as possible. In the local path, when three adjacent nodes k-1, k and k +1 form an L-shaped link, judging whether the L-shaped link is matched with the trend identifier of the current L-shaped path, if so, judging whether a diagonal node D of a middle node k of the local link is an obstacle, and if not, replacing the diagonal node D with the node k. The L-shaped path trend improved A-STAR calculation method provided by the invention can effectively reduce the turning times and accumulated turning angles of the local path in the path planning of the intelligent warehouse logistics robot at a tiny algorithm operation time cost.
Description
Technical Field
The invention relates to the technical field of intelligent warehousing, in particular to an intelligent warehousing logistics robot path planning method for improving an A-STAR algorithm based on an L-shaped path trend.
Background
The path planning is an important link in the navigation process of the logistics robot, the logistics robot belongs to one of the mobile robots, and the mobile robot path planning means that the robot plans a collision-free and safe feasible path from a starting point to a terminal point based on the environmental information and optimizes the path as much as possible[1]. Common methods for robot path planning include a visual graph method, an artificial potential field method, an A-STAR algorithm, an artificial intelligence algorithm and the like. The A-STAR algorithm is an extension of Dijikstra's algorithm, which uses an equal cost search and a heuristic search to efficiently compute an optimal priority search in less time[2]. However, the path of the mobile robot drawn by the A-STAR algorithm has the problems of more broken lines, more turning times, large accumulated turning angle and the like. Literature reference[3]On the basis of the initial path planning of the A-STAR algorithm, all nodes in the path are traversed, redundant nodes are deleted, a smooth A-STAR model is established, the length, the turning times and the turning angles of the path planned by the mobile robot are effectively reduced with extremely low calculation time loss, and the method is suitable for path planning in a complex environment. Literature reference[4]On the basis of an A-STAR algorithm initial path, the path length and the turning angle are effectively reduced by dividing the path step length and deleting redundant path nodes, and the method is suitable for path planning of the mobile robot under the environment with multiple task points and high obstacle rate. Literature reference[5]And introducing a Past list and a Frequency list, redefining a closing list in the basic A-STAR algorithm, and realizing path planning of the multi-robot trolley by combining a dynamic collision avoidance rule. Literature reference[6]The mobile robot multi-task scheduling problem in warehouse logistics is that a complex diagonal distance algorithm is provided, an heuristic function of an A-STAR algorithm is improved, the shortest total task completion time of the mobile robot in a scheduling system is achieved, and redundant nodes still exist in a planned path. The intelligent storage environment is different from the working environment of a common robot, the feasible space of the logistics robot in the storage environment is limited, and the shape of the obstacle is regular, so that the path planning of the logistics robot in the intelligent storage environment is different from that of the common mobile robot.
Disclosure of Invention
The invention aims to provide a path planning method of an intelligent warehouse logistics robot for improving an A-STAR algorithm by an L-shaped path trend.
The technical scheme adopted by the invention is as follows:
the intelligent warehouse logistics robot path planning method of the L-type path trend improved A-STAR algorithm comprises the following steps:
and 8, taking the updated planned path as the optimized path.
Further, a grid method is adopted in the step 1 to construct an intelligent storage environment map.
Further, the intelligent storage environment map in the step 1 mainly comprises a picking workbench, a shelf, a logistics robot and the like.
Further, in the step 2, the starting position S is positioned at the lower left of the target position T, and a straight line where the starting position S and the target position T are positioned is extended to form a right angle so as to construct an L1 type path trend mark; similarly, when the starting position S is at the upper left of the target position T, an L2 type path trend identifier is constructed; when the starting position S is at the upper right of the target position T, constructing an L3 type path trend identifier; when the starting position S is at the lower right of the target position T, the path trend mark of L4 type is constructed.
Further, in step 5, the local L-shaped link is determined to be matched when the path trend identifier formed by the local L-shaped link and the starting position S and the target position T just form a loop.
Further, in step 6, the picking table, the shelf, and other logistics robots are considered as obstacles for the logistics robots.
By adopting the technical scheme, compared with the reduction of the total path length, the requirement on reducing the turning times and the turning angles is more urgent for the intelligent storage system environment with full shelves and limited feasible space, so that the steering of the logistics trolley is convenient to control, and the operation safety is more facilitated. Meanwhile, the running time of the L-type path trend improved A-STAR algorithm is shorter than that of the smooth A-STAR algorithm, and the algorithm running time is obviously more advantageous in large-scale intelligent warehousing environment path planning.
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The invention is described in further detail below with reference to the accompanying drawings and the detailed description;
FIG. 1 is a schematic diagram of an L-shaped path trend marker;
FIG. 2 is a schematic flow chart of a method for planning a path of an intelligent warehouse logistics robot according to the invention, wherein the method comprises an L-type path trend improved A-STAR algorithm;
FIG. 3 is a diagram of a smart warehouse logistics robot path planning result based on a prior art smoothing A-STAR algorithm;
FIG. 4 shows the planning result of the intelligent warehouse logistics robot path planning method based on the L-shaped path trend improved A-STAR algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The path of the mobile robot drawn by the A-STAR calculation rule in the prior art has the problems of more broken lines, more turning times, large accumulated turning angle and the like. The intelligent storage environment is different from the working environment of a common robot, the feasible space of the logistics robot in the storage environment is limited, and the shape of the obstacle is regular, so that the path planning of the logistics robot in the intelligent storage environment is different from that of the common mobile robot.
As shown in one of fig. 1 to 4, the invention discloses a method for planning a path of an intelligent warehouse logistics robot by an L-type path trend improved a-STAR algorithm, which comprises the following steps:
and 8, taking the updated planned path as the optimized path.
Further, a grid method is adopted in the step 1 to construct an intelligent storage environment map.
Further, the intelligent storage environment map in the step 1 mainly comprises a picking workbench, a shelf, a logistics robot and the like.
Further, in the step 2, the starting position S is positioned at the lower left of the target position T, and a straight line where the starting position S and the target position T are positioned is extended to form a right angle so as to construct an L1 type path trend mark; similarly, when the starting position S is at the upper left of the target position T, an L2 type path trend identifier is constructed; when the starting position S is at the upper right of the target position T, constructing an L3 type path trend identifier; when the starting position S is at the lower right of the target position T, the path trend mark of L4 type is constructed.
Further, in step 5, the local L-shaped link is determined to be matched when the path trend identifier formed by the local L-shaped link and the starting position S and the target position T just form a loop.
Further, in step 6, the picking table, the shelf, and other logistics robots are considered as obstacles for the logistics robots.
The following is a detailed description of the specific working principle of the present invention:
compared with the reduction of the total path length, the requirement for reducing the turning times and the turning angles is more urgent for the intelligent storage system environment with full shelves and limited feasible space, on one hand, the steering of the logistics trolley is convenient to control, and on the other hand, the operation safety is more facilitated. Meanwhile, the running time of the L-type path trend improved A-STAR algorithm is shorter than that of the smooth A-STAR algorithm, and the algorithm running time is obviously more advantageous in large-scale intelligent warehousing environment path planning.
The intelligent storage environment is different from the environment of a general mobile robot, and the obstacles in the intelligent storage environment are mainly shelves, picking workbenches, random obstacles and the like, are regular, ordered and relatively fixed, and have approximately rectangular outlines. At the same time, aisles, i.e. the available space, in a smart warehousing environment are relatively limited, and therefore the "zigzag" path occurs mainly in local areas near the picking station, i.e. areas that need to be heavily optimized. In the smart warehousing environment, the final path formed from the starting position S to the target position T can be approximately regarded as an L trend, and when all paths are consistent with the L trend, the number of turning nodes of the path is minimum.
As shown in FIG. 1, the present invention constructs four L-shaped path trend indicators, L1, L2, L3 and L4 respectively, according to the geometric relationship between the start position S and the target position T.
A method for constructing path trend identification. Taking the route trend indicator of the L1 model as an example, the starting position S is at the lower left of the target position T, the straight line where the extension S, T is located forms a right angle (D), and the route trend indicator of the L1 model is constructed. Similarly, when S is at the upper left of T, constructing a path trend identifier of L2 type; when S is positioned at the upper right of T, an L3 type path trend identifier is constructed; and when the S is at the lower right of the T, constructing an L4 type path trend identifier.
As shown in fig. 2, a method of using path trend identification. After the A-STAR algorithm plans out the initial path, optimization is carried out through path trend identification according to the principle of enabling the local path to be consistent with the path trend formed by S, T as far as possible. The specific rule is as follows: in the local path, when three adjacent nodes (k-1, k +1) form an L-shaped link, whether the L-shaped link is matched with the current L-shaped path trend identification is judged (according to the fact that the L-shaped link and the path trend identification L formed by S, T just form a loop), if the L-shaped link and the path trend identification L are matched, whether a diagonal node D (diagonal angle forming the loop) of a middle node k of the local link is an obstacle is judged, and if the diagonal node D is not the obstacle, the node k is replaced by the diagonal node D.
As shown in fig. 1, the optimization of the type L1 path trend labels is taken as an example. Currently, S and T form an L1 type path trend, and a link (shown by a chain line) formed by three nodes of local paths k-1, k and k +1 in an initial path presents an L type shape (a zigzag shape appears from S, k-1, k +1 and T paths, and 3 turns exist), so that optimization is needed. Judging whether the node D is an obstacle (a picking workbench, a goods shelf, other obstacles and the like) or not, and if not, replacing the node k with a node D; if so, no processing is performed. At this time, the links become S, k-1, D, k +1, T, and there are only 1 turn in the link, thereby reducing the nodes of the turn and smoothing the path. Other L-shaped path trend labeling optimization methods and so on.
In the experiment, the initial path obtained by the A-STAR algorithm is optimized by using the smoothing A-STAR algorithm and the L-shaped path trend identification proposed by the invention, and the result is shown in FIGS. 3 and 4.
TABLE 1A-comparison of smart warehouse logistics robot path planning results before and after STAR Algorithm improvement
Compared with the initial path planned by the A-STAR algorithm, the path length in the smoothing A-STAR algorithm is reduced by 8.7 percent compared with the initial path; the turning nodes are reduced by 2, and the reduction rate is 40%; the accumulated turning angle is reduced by 225 degrees, the reduction rate is 50 percent, namely, the effects of reducing the total length of the path, the number of turning nodes and the accumulated turning angle are achieved to a certain extent. Compared with the initial path of the A-STAR algorithm, the L-type path trend improves the A-STAR algorithm, the total path length is not reduced, but turning nodes are reduced by 3, and the reduction rate is 60%; the cumulative turning angle is reduced by 270 degrees, and the reduction rate is 60 percent. The reduction rate of the turning node of the L-type path trend improved A-STAR algorithm is 20% higher than that of the smooth A-STAR algorithm, and the reduction rate of the accumulated turning angle of the L-type path trend improved A-STAR algorithm is 10% higher than that of the smooth A-STAR algorithm.
By adopting the technical scheme, the invention effectively reduces the turning times and the accumulated turning angles in the local path at the cost of micro algorithm running time. Compared with the reduction of the total path length, the requirement for reducing the turning times and the turning angles is more urgent for the intelligent storage system environment with full shelves and limited feasible space, on one hand, the steering of the logistics trolley is convenient to control, and on the other hand, the operation safety is more facilitated. Meanwhile, the running time of the L-type path trend improved A-STAR algorithm is shorter than that of the smooth A-STAR algorithm, and the algorithm running time is obviously more advantageous in large-scale intelligent warehousing environment path planning.
It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The embodiments and features of the embodiments in the present application may be combined with each other without conflict. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
Reference documents:
[1] and (4) a geographical area, a simple area, a rigid area, a rolling path planning [ J ] of the robot under a dynamic uncertain environment, an automatic chemical newspaper 2002,28(2):161 and 175.
[2]KNUTH D E.A generalization of Dijkstra's algorithm[J].Information Processing Letters,1977,6(1):1-5.
[3] Wangwanghongwei, Maryong, Xie, Guo Ming. moving robot Path planning [ J ] based on the algorithm of smoothness A-. university of Tongji (Nature science edition), 2010,38(11): 1647-.
[4] Sun Wei, Lu Yunfeng, Tang Wei, Schumann moving robot path planning [ J ]. university of Hunan proceedings (Nature science edition), 2017,44(4):94-101 based on an improved A-STAR algorithm.
[5] Gao Xiao Jie, robot car path planning algorithm research in warehouse system [ D ]. Beijing: Beijing post and telecommunications university, 2017.
[6] Wangxuhong, Liuxuehao, Wangyongcheng, research on task scheduling and path optimization of warehouse logistics mobile robots based on improved A-' algorithm [ J ] industrial engineering, 2019,22(06):34-39.
Claims (6)
1. The intelligent warehouse logistics robot path planning method of the L-type path trend improved A-STAR algorithm is characterized in that: which comprises the following steps:
step 1, constructing an intelligent warehousing environment map, determining an initial position S and a target position T, and simultaneously obtaining an initial planned path and the number N of nodes on the path by utilizing an A-STAR algorithm;
step 2, respectively setting different L-shaped path trend marks based on the relative position relation of the starting position S and the target position T;
step 3, judging whether the number of the nodes is more than 2; if yes, executing step 4; otherwise, executing step 8;
step 4, judging whether the current node k is not larger than the node number N, wherein k is an integer not smaller than 1 and the initial value is 2; if yes, executing step 5; otherwise, executing step 8;
step 5, judging whether a local L-shaped link formed by the node k-1, the node k and the node k +1 is matched with the current L-shaped path trend mark or not; if yes, executing step 6; otherwise, executing step 7;
step 6, judging whether an obstacle exists on a diagonal node D of a middle node k of the local L-shaped link; if yes, keeping the current planning path unchanged and executing the step 7; otherwise, replacing the node k with the node D to update the current planned path and executing the step 7;
step 7, the current node k = k +1 to indicate the next node, and step 4 is performed;
and 8, taking the updated planned path as the optimized path.
2. The warehousing robot path planning method for the L-shaped path trend improved A-STAR algorithm according to claim 1, wherein: and step 1, performing intelligent storage environment map by adopting a grid method.
3. The warehousing robot path planning method for the L-shaped path trend improved A-STAR algorithm according to claim 1, wherein: the intelligent storage environment map in the step 1 comprises a picking workbench, a goods shelf and a logistics robot.
4. The warehousing robot path planning method for the L-shaped path trend improved A-STAR algorithm according to claim 1, wherein: in the step 2, the initial position S is positioned at the lower left of the target position T, and a straight line where the initial position S and the target position T are positioned is extended to form a right angle so as to construct an L1 type path trend identifier; similarly, when the starting position S is at the upper left of the target position T, an L2 type path trend identifier is constructed; when the starting position S is at the upper right of the target position T, constructing an L3 type path trend identifier; when the starting position S is at the lower right of the target position T, the path trend mark of L4 type is constructed.
5. The warehousing robot path planning method for the L-shaped path trend improved A-STAR algorithm according to claim 1, wherein: and in the step 5, judging that the local L-shaped link is matched with the path trend mark formed by the starting position S and the target position T when the local L-shaped link just forms a loop.
6. The warehousing robot path planning method for the L-shaped path trend improved A-STAR algorithm according to claim 1, wherein: in step 6, the picking workbench, the goods shelf and other logistics robots are regarded as obstacles for the logistics robots.
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