CN113156886A - Intelligent logistics path planning method and system - Google Patents
Intelligent logistics path planning method and system Download PDFInfo
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Abstract
The invention discloses an intelligent logistics path planning method and a system, wherein the method comprises the following steps: collecting environmental information and establishing a map according to the collected environmental information; representing a map through a two-dimensional array and rasterizing the map; setting a starting point, an end point and a speed change rate of the robot, and setting a starting point, an end point and a coordinate axis speed change, a safety distance and a threat distance of the barrier; performing global path planning by combining a heuristic function according to an A-algorithm to obtain a global optimal path; according to the global optimal path, judging a dynamic barrier in the moving process of the robot, if the robot meets the dynamic barrier, replanning the path, and if the robot does not meet the dynamic barrier, finishing path planning; the system comprises: the method comprises the steps of establishing a map module, a map preprocessing module, a map initialization module, a global path planning module and a local dynamic obstacle avoidance module. The invention can complete the path planning from the starting point to the end point and can automatically avoid dynamic obstacles in the moving process.
Description
Technical Field
The invention relates to the technical field of robot path planning, in particular to an intelligent logistics path planning method and system.
Background
The rapid development of e-commerce in the country has changed people's life patterns greatly over the past 20 years. More and more people choose to buy the required goods on the E-commerce platform, and the consumption requirements of people also reflect the characteristics of multiple varieties, short period and small quantity. A large amount of express delivery parcel letter sorting tasks that from this produce can only be accomplished through piling up manpower overtime, along with the increase of people's consumption desire, need keep higher customer satisfaction, and the enterprise needs pay more cost on the manpower.
The current situation of the labor force in China still hardly changes essentially in a short time. The substantial increase in labor costs is a serious concern for labor-intensive logistics enterprises. With the continuous development of human society to intellectualization, intelligent robots are increasingly applied to various industries. Logistics enterprises are also beginning to turn the eyes to storage automation and intellectualization. The prior art discloses an intelligent parcel sorting system path planning method based on an ant colony algorithm, wherein pheromones in an environment at an initial moment are completely the same, ants construct path solutions in a random mode, and the solutions have good and bad scores. When the pheromone is updated, the ant colony algorithm leaves more pheromones on a path through which a better solution passes, and the more pheromones attract more ants, so that the initial difference can be enlarged in the positive feedback process, and the whole system is guided to develop towards the direction of the optimal solution. Although the algorithm has a faster convergence speed due to the positive feedback, if the optimal solution obtained at the beginning of the algorithm is the suboptimal solution, the suboptimal solution is dominant due to the positive feedback, so that the algorithm is locally optimal and is difficult to jump out, and finally, the planned path is not the optimal path and has a large number of turning points, and the path can meet the actual requirement by further smoothing. Therefore, how to solve the problems that the obstacle avoidance effect is not satisfactory and the optimal path cannot be planned when the operation environment of the robot trolley is complex, which are common in the field, is a problem that needs to be solved by the technical staff in the field urgently.
Disclosure of Invention
The invention aims to provide an intelligent logistics path planning method and system, which can complete path planning from a starting point to a terminal point and can automatically avoid dynamic obstacles in the moving process.
The technical scheme for realizing the purpose of the invention is as follows: an intelligent logistics path planning method comprises the following steps:
collecting environmental information and establishing a map according to the collected environmental information;
representing a map through a two-dimensional array and rasterizing the map;
setting a starting point, an end point and a speed change rate of the robot, and setting a starting point, an end point and a coordinate axis speed change, a safety distance and a threat distance of the barrier;
performing global path planning by combining a heuristic function according to an A-algorithm to obtain a global optimal path;
and according to the global optimal path, judging the dynamic barrier in the moving process of the robot, if the robot meets the dynamic barrier, replanning the path, and if the robot does not meet the dynamic barrier, finishing path planning.
Further, the representing the map by the two-dimensional array and rasterizing the map specifically includes:
the logistics warehouse is divided into square grids by a grid method, the two-dimensional codes are placed on nodes of the squares, and each node in the grids represents a grid; the area where the obstacle is located appears black in the grid map, the map matrix is marked with 1, the freely passable area appears white in the grid map, and the map matrix is marked with 0.
Further, the side length of each square is equal to 1 meter, and the number of grids is 100 x 100.
Furthermore, the two-dimensional code contains the position information of the current node, and when a dynamic obstacle is encountered, the two-dimensional code is scanned by the camera to obtain the current position information, and the path is re-planned.
Further, the performing global path planning by combining the heuristic function according to the a-algorithm to obtain a global optimal path includes:
step 1, creating lists OpenList and CloseList, wherein the initial states of the OpenList and the CloseList are both empty;
step 2, starting from a starting point A, and adding the starting point into an OpenList;
step 3, checking all adjacent nodes of the starting point A, removing the adjacent nodes which are already in the CloseList and have obstacles, and adding other adjacent nodes into the OpenList, wherein the point A is a 'father node' of the nodes;
step 4, deleting the point A from the OpenList and adding the point A into the CloseList;
step 5, calculating an evaluation value F of a node adjacent to the point A in the OpenList according to a preset formula, and selecting a node with the lowest F value as a new starting point A;
step 6, adding the new starting point into the OpenList, and repeating the steps 3-5 until the target node is added into the CloseList;
and 7, according to the CloseList, starting from the target node, moving along the father node of each node until returning to the initial starting point, and drawing a global optimal path.
Further, the preset formula is as follows:
F(n)＝G(n)+W(n)×H(n)
wherein, F (n) is an evaluation function of the node n, G (n) is an actual cost function of the node n and represents the moving cost of the node n from the starting point A to the node n, H (n) represents the estimated moving cost of the node n to the target node, W (n) is a weight coefficient, W (n) is larger than or equal to 1, the larger W (n) is, the closer to the BFS algorithm, and the smaller W (n) is, the closer to the Dijkstra algorithm.
Further, h (n) adopts the Manhattan method to estimate, the number of the squares passing through when the current node moves horizontally or vertically to reach the target node is calculated, the diagonal movement is ignored, and h (n) is:
H(n)＝|x1–x2|+|y1–y2|
wherein x1 and y1 represent the horizontal and vertical coordinates of the current node, and x2 and y2 represent the horizontal and vertical coordinates of the target node.
Further, if a dynamic obstacle is encountered, the replanning the path includes:
step 1, detecting the distance between the dynamic barrier and the robot at the current time t, if the distance between the dynamic barrier and the robot is less than a threat distance td, re-planning a path, executing step 2, and if not, keeping the original planned path;
step 2, detecting the distance between the dynamic barrier and the robot at the time t and the time t +0.2s, and if the distance between the barrier and the robot is increased, keeping the original motion state and the original planned path of the robot;
if the distance between the obstacle and the robot becomes smaller, the following two cases are distinguished:
(1) if the moving speed vrb of the robot is the same as the moving speed vdo of the dynamic obstacle, but the speed direction theta rb of the robot is different from the speed direction theta do of the dynamic obstacle, the moving acceleration arb of the robot is changed to be opposite to the moving speed vrb of the robot; when the distance between the dynamic barrier and the robot is larger than the safe distance sd and the distance detected by the sensor is increased, recovering the motion state before the time t, and executing the step 3;
(2) the moving speed vrb of the robot is opposite to the moving speed vdo of the dynamic obstacle, at this time, the moving speed vrb of the robot is changed to be opposite to the moving speed before the time t, at the same time, the speed at this time is half of the speed before the time t, namely vrb/2, when the distance between the dynamic obstacle and the robot is greater than the safe distance sd and the detection distance is increased, the moving state before the time t is recovered, and the step 3 is executed;
and 3, acquiring the position of the current node, taking the current node as a starting point and the end point as a target node, and then planning the shortest path by combining an A-x algorithm and a heuristic function.
An intelligent logistics path planning system, comprising:
the map building module is used for collecting environmental information and building an original map according to the collected environmental information;
the map preprocessing module is used for representing a map through a two-dimensional array and rasterizing the map;
the map initialization module is used for setting a starting point, an end point and a speed change rate of the robot and a starting point, an end point and coordinate axis speed change, a safety distance and a threat distance of the barrier;
the global path planning module is used for carrying out global path planning according to an A-x algorithm to obtain a global optimal path, and after the A-x algorithm is operated, each node is expanded to the node direction of the minimum total cost;
and the local dynamic obstacle avoidance module is used for judging dynamic obstacles in the moving process, replanning the path if the dynamic obstacles are met, and finishing path planning if the dynamic obstacles are not met.
Further, the system further comprises:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the method comprises the steps of establishing a map module, a map preprocessing module, a map initialization module, a global path planning module and a local dynamic obstacle avoidance module.
Compared with the prior art, the invention has the following remarkable effects: the A-star algorithm is combined with the dynamic obstacle avoidance algorithm, so that the robot trolley can complete path planning from a starting point to a terminal point, and can automatically avoid dynamic obstacles in the moving process; compared with the traditional A-x algorithm, the weight coefficient Weights of H (n) is added, the original H (n) is changed into W (n) -x H (n), and the search process can be influenced by changing W (n) in the search process; the improved A-x algorithm has higher real-time performance, can flexibly adjust parameters according to actual conditions to obtain an optimal path, and can realize local path planning of dynamic obstacles, so that the trolley can process the encountered emergency in the running process.
Drawings
Fig. 1 is a schematic step diagram of an intelligent logistics path planning method according to an embodiment of the present invention.
Fig. 2 is a schematic view of a center modeling of a material flow according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of an improved a-algorithm incorporating a heuristic function according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a MATLAB dynamic obstacle avoidance simulation result according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of an intelligent logistics path planning system module according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, 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.
Example 1
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram illustrating steps of an intelligent logistics path planning method according to an embodiment of the present invention, and the steps are as follows:
s100, collecting environmental information and establishing an original map according to the collected environmental information; collecting environmental information by using a camera on the robot trolley, and establishing an original map according to the environmental information;
step S110, map preprocessing, namely representing a map through a two-dimensional array and rasterizing the map; the map is represented by a two-dimensional array and rasterized, with each cell being walkable (no obstacles) and non-walkable (obstacles).
The logistics warehouse is divided into square grids by a grid method, the two-dimensional codes are placed on nodes of the squares, and each node in the grids represents a grid; the area where the obstacle is located appears black in the grid map, the map matrix is marked with 1, the freely passable area appears white in the grid map, and the map matrix is marked with 0. The side length of each square is equal to 1 meter, and the number of the grids is 100 x 100; the two-dimensional code comprises the position information of the current node, the current position information can be obtained by scanning the two-dimensional code through the camera, and the path is planned again.
Step S120, initializing a map, and setting a starting point, an end point and a speed change rate of the robot, and a starting point, an end point and a coordinate axis speed change, a safety distance and a threat distance of the barrier;
step S130, global path planning is carried out according to an A-algorithm and a heuristic function, and after the algorithm is operated, each node is expanded to the node direction with the minimum total cost to obtain a global optimal path;
step S140, in the moving process of the robot, the dynamic barrier is judged, if the robot encounters a dynamic barrier, the path is re-planned, the current point is taken as the starting point, the end point is taken as the target point, the shortest path is found in the moving area of the dynamic barrier by combining the traditional kinematics analysis method, the position of the dynamic barrier is considered in the path, after the moving area of the dynamic barrier, the shortest path planning only under the static barrier is carried out by continuously utilizing the improved A-x algorithm and combining the heuristic function, and if the robot does not encounter the dynamic barrier, the path planning is judged to be finished.
In step S110, the map rasterization is to decompose the map space where the vehicle is located into independent cells, that is, grids, and the grids are connected but not overlapped with each other, as shown in fig. 2. If no obstacle exists in the grid, the trolley can freely pass through the grid, otherwise, if the obstacle exists in the grid, the grid is called an obstacle grid (the grid occupying only partial space for the obstacle is also regarded as the obstacle grid), and the obstacle grid is not marked and expanded when the A-x algorithm is operated.
The flowchart of the global path planning according to the a-algorithm and the heuristic function in step S130 is shown in fig. 3, and the specific steps are as follows:
s131, creating an open list (OpenList) and a closed list (CloseList), wherein the initial states of the OpenList and the CloseList are both empty;
s132, start from the beginning and add it to the OpenList. Only one item in the OpenList, namely the starting point, is added slowly later (a node to be processed);
s133, looking up the nodes adjacent to the starting point a, removing the adjacent nodes already in the CloseList and having obstacles, adding other adjacent nodes to the OpenList, and saving the point a as a "parent" of all these nodes;
s134, deleting the point A from the OpenList, and adding the point A into a CloseList;
s135, calculating an evaluation value F of a node adjacent to the point A in the OpenList according to a formula (1), and selecting a node with the lowest F value as a new starting point A and a new node # 1;
F(n)＝G(n)+W(n)×H(n) (1)
wherein, F (n) is an evaluation function of the node n, G (n) is an actual cost function of the node n and represents the moving cost of the current node n from the starting point A, W (n) is a weight coefficient, W (n) is more than or equal to 1, H (n) represents the estimated moving cost of the current node n to the target node;
there are many ways to estimate the value of h (n), for example, here we use the Manhattan method to calculate the number of squares that pass to reach the target from the current square by moving either laterally or longitudinally, ignoring the diagonal movement, as shown in equation (2):
H(n)＝|x1–x2|+|y1–y2| (2)
wherein x1 and y1 represent the horizontal and vertical coordinates of the current node, and x2 and y2 represent the horizontal and vertical coordinates of the target node.
Calculating F values of all surrounding points of the point A, wherein the value G is a starting point, and the sum of Euclidean distances between every two adjacent nodes in the process from a father node selected in the searching process to the current node; the H value is the Manhattan distance from the current node to the end point, i.e., the sum of the distances traveled in the horizontal direction and the vertical direction from the current node to the end point, regardless of obstacles. And selecting the node with the lowest F value from the OpenList, and recording the node as the node # 1. It is deleted from OpenList and then added to CloseList.
S136, checking all adjacent nodes of the #1 node, and skipping over those nodes which are already in the CloseList or are not passable, specifically, the following cases are discussed:
A. if the nodes are not in the OpenList, adding the nodes into the OpenList, and setting the nodes selected by us as father nodes of the newly added nodes;
B. if a certain adjacent node is already in the OpenList, checking whether the path is more optimal, namely whether the node reached through the #1 node has a smaller G value, and if not, not doing any operation; if the G value is smaller, setting the father node of the node as a #1 node, and then recalculating the F value and the G value of the node;
C. if the G values of all nodes are not lower, all adjacent nodes in OpenList are abandoned as the next nodes of the #1 node;
D. if the neighbors of node #1 are already in the OpenList and the checked G values are not low, then we drop node #1 as the next node for A. Then selecting other nodes in OpenList as the next node of A according to the principle of lowest F value;
E. if the #1 square grid has a node newly added into the OpenList, checking all adjacent nodes newly added into the OpenList, and selecting the node with the lowest F value as the next node of the #1 node;
s137 repeats the above process until the target node is added into CloseList;
s138 starts from the target, moves along the parent node of each node until returning to the starting node, and draws the optimal path sought. The improved a algorithm effect graph is shown in fig. 4.
The total cost calculation formula of the improved a algorithm is formula (1), and unlike the traditional a, the weight coefficient Weights of h (n) is added in the initially set parameters, so that the original h (n) is changed into w (n) × h (n), wherein w (n) is larger than or equal to 1. W (n) may affect the evaluation value. During the search process, we can influence the search process by changing w (n). It can be inferred that the larger W (n), the closer to the BFS algorithm, and the relatively smaller W (n), the relatively closer to the Dijkstra algorithm. Because the influence brought by different weight coefficients is different, sometimes the speed of searching a path can be accelerated, but the shortest path can not be found, so that the parameter needs to be adjusted flexibly in practical situations.
In step S130, the corner smoothing process is performed when the last path is generated. Although optimization in search efficiency and speed is performed, it can be found that the found route is only a mathematically optimal route, and the planned route has some turns which can be completely omitted, because the final goal is to control the real object to move, the number of turns needs to be reduced as much as possible, and then the route is further optimized, so that the number of turns is reduced as much as possible on the basis of ensuring that the route is not increased. The specific method comprises the steps of finding a node with the minimum total cost from a list, then finding a source of the node through direction information stored in the node, further obtaining a father node and the direction information of the father node, further obtaining the position of a node expected to be walked next, checking whether the point is located in an OpenList or not after obtaining the position of the node expected to be walked, if so, calculating the total cost of the point, and if the cost is equal to the cost of the original point to be walked, replacing the original point with the expected point for optimization, otherwise, not performing optimization.
Based on the above path planning method, an embodiment of the present invention further provides an intelligent logistics path planning system as shown in fig. 5, including:
the map building module is used for collecting environmental information and building an original map according to the collected environmental information;
the map preprocessing module is used for representing a map through a two-dimensional array and rasterizing the map;
the map initialization module is used for setting a starting point, an end point and a speed change rate of the robot and a starting point, an end point and coordinate axis speed change, a safety distance and a threat distance of the barrier;
the global path planning module is used for carrying out global path planning according to an improved A-algorithm, and each node is expanded towards the node direction of the minimum total cost after the A-algorithm is operated;
the system also includes a memory, a processor, and a communication interface, which are electrically connected, directly or indirectly, to each other to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by executing the software programs and modules stored in the memory. The communication interface may be used for communicating signaling or data with other node devices.
The memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an erasable read only memory (EPROM), an electrically erasable read only memory (EEPROM), and the like.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and may include more or fewer components than shown in fig. 5, or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Example 2
Based on the logistics path planning method described in embodiment 1, in the moving process of the trolley, if a dynamic obstacle is encountered, a path planning algorithm with the dynamic obstacle is performed, and the sizes of the grid map and the dynamic obstacle are specified firstly as same as the path planning with only a static obstacle. In order to be closer to the actual environment of the logistics warehouse, 20 12 × 18 shelves are added into the grid map in the second step, the shelves are divided into two groups, the abscissa of the two groups is the same, and the distance between two nearest squares between the two groups is 16 squares; within a group, two nearest squares of two shelves with the same abscissa are separated by four squares, and two shelves with the same ordinate are separated by five squares. Since the introduction of dynamic obstacles includes random static obstacles, no other static obstacles are introduced except for ten shelves. Then, the size of the dynamic obstacle is defined in the grid map, and the size of the dynamic obstacle is limited to (2-8) multiplied by 1. Other required motion parameters in the dynamic obstacle avoidance algorithm are specified as follows:
t: timing a certain moment when the robot starts to move as 0;
vrb: the speed at which the robot moves at time t;
vdo: the speed at which the dynamic barrier moves at time t;
arb: acceleration of the robot moving at time t;
ado: acceleration of the movement of the dynamic obstacle at time t;
θ rb: the positive angle of the robot relative to the x axis when the robot moves at the moment t;
θ do: the positive angle of the dynamic barrier relative to the x axis when the dynamic barrier moves at the moment t;
td: detecting a dynamic obstacle by the robot, and starting a minimum distance of a dynamic obstacle avoidance program;
sd: the minimum safe distance that the robot does not collide with the dynamic barrier;
meanwhile, in consideration of actual conditions, when the robot sorts goods in the logistics warehouse and sorts goods between adjacent goods shelves in each group, the distance between the robot and the goods shelves is small, so that the robot basically cannot collide with dynamic obstacles, and when the dynamic obstacles are arranged, the robot can reach areas with more squares by only arranging the movable dynamic obstacles between two groups of goods shelves.
In combination with a path planning algorithm under only static obstacles, the path planning algorithm is described as follows in a working environment:
(1) when t is 0, the robot starts to move, and moves to the global optimal path planned by combining the A-x algorithm with the heuristic function only before moving to the grid with the minimum vertical coordinate of the first group of shelves;
(2) after the robot reaches the first group of the rack ordinate minimum squares, the dynamic barrier starts to move, and because only one dynamic barrier exists in the map at the moment, in order to simplify calculation and quickly respond, the microcomputer can control the robot to perform path planning without adopting an A-star algorithm and adopt the most traditional kinematics algorithm; in the establishment of the grid map, sign changing processing is carried out on an x axis and a y axis of a grid, but in dynamic obstacle avoidance, because the position, the speed and the acceleration parameters of a robot and a dynamic obstacle need to be considered very inconvenient, the motion states of the robot and the dynamic obstacle are described in a speed-angle mode;
path planning using the most traditional kinematics algorithm includes:
after the robot enters a dynamic obstacle moving area, the distance between the dynamic obstacle and the robot is detected through the ultrasonic sensor, when the distance between the dynamic obstacle and the robot is smaller than a threat distance td, the dynamic obstacle avoidance is started, and the distance between the dynamic obstacle and the robot at the time t and the time t +0.2s is detected. If the distance between the barrier and the robot is increased, the robot does not need to change the motion state and only needs to keep the original motion state; if the distance between the obstacle and the robot becomes smaller, the following two cases are discussed:
A. the moving speed vrb of the robot is the same direction (same sign) as the moving speed vdo of the dynamic obstacle, but the speed directions theta rb and theta do are different, so that collision can occur when the robot continues to move, and at this time, the moving acceleration arb of the robot is only required to be changed to be opposite to the moving speed vrb of the robot. When the distance between the dynamic barrier and the robot is larger than the safe distance sd and the distance detected by the sensor is increased, recovering the motion state before the time t;
B. the moving speed vrb of the robot is opposite (opposite in sign) to the moving speed vdo of the dynamic obstacle, and when the collision may occur due to the continuous movement, the moving speed vrb of the robot needs to be changed to be opposite (opposite in sign) to the moving speed before the time t. Meanwhile, in order to ensure that the consumed time of the movement process is shorter, the speed at the moment is only half of the speed before the time t, namely vrb/2, and then when the distance between the dynamic barrier and the robot is larger than the safe distance sd and the distance detected by the sensor is increased, the movement state before the time t is recovered;
(3) and the robot avoids the dynamic barrier, acquires the position of the current grid as a starting point when moving to the grid with the maximum vertical coordinate of the second group of shelves, and continues to adopt the A-star algorithm and a heuristic function to plan a path so as to reach the end point.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
In summary, the method and system for planning the intelligent logistics path provided by the embodiment of the application combine the improved a-algorithm with the dynamic obstacle avoidance algorithm, so that the robot trolley can complete path planning from the starting point to the end point, and can automatically avoid dynamic obstacles in the moving process. Compared with the traditional A-algorithm, the weight coefficient Weights of H (n) is increased, and the original H (n) is changed into W (n) H (n). During the search process, the search process can be influenced by changing w (n). The improved A-x algorithm has higher real-time performance, can flexibly adjust parameters according to actual conditions to obtain an optimal path, and can realize local path planning of dynamic obstacles, so that the trolley can process the encountered emergency in the running process.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An intelligent logistics path planning method is characterized by comprising the following steps:
collecting environmental information and establishing a map according to the collected environmental information;
representing a map through a two-dimensional array and rasterizing the map;
setting a starting point, an end point and a speed change rate of the robot, and setting a starting point, an end point and a coordinate axis speed change, a safety distance and a threat distance of the barrier;
performing global path planning by combining a heuristic function according to an A-algorithm to obtain a global optimal path;
and according to the global optimal path, judging the dynamic barrier in the moving process of the robot, if the robot meets the dynamic barrier, replanning the path, and if the robot does not meet the dynamic barrier, finishing path planning.
2. The intelligent logistics path planning method of claim 1, wherein the representing of the map by the two-dimensional array and the rasterizing of the map are specifically as follows:
the logistics warehouse is divided into square grids by a grid method, the two-dimensional codes are placed on nodes of the squares, and each node in the grids represents a grid; the area where the obstacle is located appears black in the grid map, the map matrix is marked with 1, the freely passable area appears white in the grid map, and the map matrix is marked with 0.
3. The intelligent logistic path planning method of claim 2 wherein each square has a side length equal to 1 meter and the number of grids is 100 x 100.
4. The intelligent logistics path planning method of claim 2, wherein the two-dimensional code comprises position information of a current node, and when a dynamic obstacle is encountered, the two-dimensional code is scanned by a camera to obtain the current position information, and the path is planned again.
5. The method according to claim 1, wherein the performing global path planning according to a-algorithm in combination with a heuristic function to obtain a global optimal path comprises:
step 1, creating an open list OpenList and a closed list CloseList, wherein the initial states of the OpenList and the CloseList are both empty;
step 2, starting from a starting point A, and adding the starting point into an OpenList;
step 3, looking up all adjacent nodes of the starting point A, removing the adjacent nodes which are already in the CloseList and have obstacles, if the adjacent nodes are not in the Open List, adding the adjacent nodes into the Open List, wherein the point A is a 'father node' of the nodes;
step 4, deleting the point A from the OpenList and adding the point A into the CloseList;
step 5, calculating an evaluation value F of a node adjacent to the point A in the OpenList according to a preset formula, and selecting a node with the lowest F value as a new starting point A;
step 6, adding the new starting point into the OpenList, and repeating the steps 3-5 until the target node is added into the CloseList;
and 7, according to the CloseList, starting from the target node, moving along the father node of each node until returning to the initial starting point, and drawing a global optimal path.
6. The intelligent logistics path planning method of claim 5, wherein the preset formula is:
F(n)＝G(n)+W(n)×H(n)
wherein, F (n) is an evaluation function of the node n, G (n) is an actual cost function of the node n and represents the moving cost of the node n from the starting point A to the node n, H (n) represents the estimated moving cost of the node n to the target node, W (n) is a weight coefficient, W (n) is larger than or equal to 1, the larger W (n) is, the closer to the BFS algorithm, and the smaller W (n) is, the closer to the Dijkstra algorithm.
7. The intelligent logistics path planning method of claim 6, wherein h (n) adopts Manhattan method estimation, calculates the number of squares passed by the current node moving horizontally or vertically to reach the target node, neglects diagonal movement, h (n) is:
H(n)＝|x1–x2|+|y1–y2|
wherein x1 and y1 represent the horizontal and vertical coordinates of the current node, and x2 and y2 represent the horizontal and vertical coordinates of the target node.
8. The intelligent logistics path planning method of claim 1, wherein if a dynamic barrier is encountered, re-planning the path comprises:
step 1, detecting the distance between the dynamic barrier and the robot at the current time t, if the distance between the dynamic barrier and the robot is less than a threat distance td, re-planning a path, executing step 2, and if not, keeping the original planned path;
step 2, detecting the distance between the dynamic barrier and the robot at the time t and the time t +0.2s, and if the distance between the barrier and the robot is increased, keeping the original motion state and the original planned path of the robot;
if the distance between the obstacle and the robot becomes smaller, the following two cases are distinguished:
(1) if the moving speed vrb of the robot is the same as the moving speed vdo of the dynamic obstacle, but the speed direction theta rb of the robot is different from the speed direction theta do of the dynamic obstacle, the moving acceleration arb of the robot is changed to be opposite to the moving speed vrb of the robot; when the distance between the dynamic barrier and the robot is larger than the safe distance sd and the distance detected by the sensor is increased, recovering the motion state before the time t, and executing the step 3;
(2) the moving speed vrb of the robot is opposite to the moving speed vdo of the dynamic obstacle, at this time, the moving speed vrb of the robot is changed to be opposite to the moving speed before the time t, at the same time, the speed at this time is half of the speed before the time t, namely vrb/2, when the distance between the dynamic obstacle and the robot is greater than the safe distance sd and the detection distance is increased, the moving state before the time t is recovered, and the step 3 is executed;
and 3, acquiring the position of the current node, taking the current node as a starting point and the end point as a target node, and then planning the shortest path by combining an A-x algorithm and a heuristic function.
9. An intelligent logistics path planning system, comprising:
the map building module is used for collecting environmental information and building an original map according to the collected environmental information;
the map preprocessing module is used for representing a map through a two-dimensional array and rasterizing the map;
the map initialization module is used for setting a starting point, an end point and a speed change rate of the robot and a starting point, an end point and coordinate axis speed change, a safety distance and a threat distance of the barrier;
the global path planning module is used for carrying out global path planning according to an A-x algorithm to obtain a global optimal path, and after the A-x algorithm is operated, each node is expanded to the node direction of the minimum total cost;
and the local dynamic obstacle avoidance module is used for judging dynamic obstacles in the moving process, replanning the path if the dynamic obstacles are met, and finishing path planning if the dynamic obstacles are not met.
10. The intelligent logistics path planning system of claim 9, further comprising:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the method comprises the steps of establishing a map module, a map preprocessing module, a map initialization module, a global path planning module and a local dynamic obstacle avoidance module.
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