CN115877853A - Intelligent storage flow path planning system and method - Google Patents
Intelligent storage flow path planning system and method Download PDFInfo
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Abstract
The invention provides an intelligent warehouse logistics path planning system and method. The system comprises: the system comprises an AGV, a controller, a laser radar and a communication module, wherein the controller is arranged on the AGV, and the laser radar and the communication module are connected with the controller; the controller plans a path according to a start point coordinate and a terminal point coordinate issued by the upper computer, controls the AGV to move according to the planned path, identifies obstacles according to the distance and direction data of the obstacles measured by the laser radar through transmitting and scanning laser beams, avoids the obstacles and plans the path again if dynamic obstacles are identified in the safe distance of the AGV, and controls the AGV to move according to the new path. The method and the system can not only carry out path planning, but also automatically carry out path planning again after identifying the dynamic barrier, so that the traveling path of the AGV trolley is always optimal.
Description
Technical Field
The invention belongs to the technical field of path planning, and particularly relates to an intelligent warehouse logistics path planning system and method.
Background
In recent years, the websites of the large electronic commerce are popular, the online consumption amount is refreshed frequently, and particularly the 'twenty-one shopping festival' of each year creates the wonderful trace of online consumption, which reflects that people tend to online consumption more and more, and logistics is an essential link, and the quality of logistics service can directly influence the online consumption experience of people, so that the efficiency and the convenience of logistics are a difficult problem to solve. However, the current logistics industry still has the defects of low mechanization degree, high manpower demand, low intelligent degree and the like, and a series of problems of high logistics cost, low operation efficiency and the like caused by the defects seriously restrict the development of the logistics industry and the progress of the economic society. In the modern logistics industry, the development of warehouse logistics, which is one of the main forms of logistics today, is undoubtedly a major factor influencing the above problems. As the operation of the warehouse logistics is not independent of related disciplines such as control, robot, etc., the warehouse logistics is becoming more and more modernized and intelligent along with the progress and development of these disciplines.
The warehouse logistics is intelligent and the warehouse robot can not be left. A storage robot is a special robot, and is essentially an Automated Guided Vehicle (AGV), which is commonly used for transporting goods in a warehouse. Storage robots were first introduced in the twentieth century and the fifties, with early AGVs guided by electromagnetic induction, and then by magnetic tape. Currently, the mainstream guidance modes include laser guidance, inertial navigation and visual navigation. The primary task of the warehousing robot is to reach a task area, i.e., a destination, from a starting point, and therefore it is very important to determine an optimal path of the robot, i.e., path planning. The path planning algorithms commonly used at present include genetic algorithms, neural networks, particle swarm algorithms, A-star algorithms and the like. Different algorithms have advantages and disadvantages, but the common characteristics are that the calculated amount is large, the solving speed is slow, and the optimal path is difficult to find.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides an intelligent warehouse flow path planning system and method.
In order to achieve the above object, the present invention adopts the following technical solutions.
In a first aspect, the present invention provides an intelligent warehouse flow path planning system, comprising: the system comprises an AGV, a controller, a laser radar and a communication module, wherein the controller is arranged on the AGV, and the laser radar and the communication module are connected with the controller; the controller plans a path according to a start point coordinate and a terminal point coordinate issued by the upper computer, controls the AGV to move according to the planned path, identifies obstacles according to the distance and direction data of the obstacles measured by the laser radar through transmitting and scanning laser beams, avoids the obstacles and plans the path again if dynamic obstacles are identified in the safe distance of the AGV, and controls the AGV to move according to the new path.
Further, the method for planning the path according to the starting point and the end point comprises the following steps:
s1, setting a path as a broken line formed by sequentially connecting the following nodes:
S(x s ,y s )，A 1 (x 1 ,y 1 )，A 2 (x 2 ,y 2 )，…，A n (x n ,y n )，T(x t ,y t )
wherein ,S(x s ,y s )、T(x t ,y t ) Respectively a start point coordinate and an end point coordinate,A i (x i ,y i ) Is the middle oneiThe coordinates of the individual nodes are then,i=1,2,…,n，nthe number of intermediate nodes;
s2, establishing a target optimization function;
s21, establishing barrier factorsf 1 ：
in the formula ,Bis a set of obstacle data points that is,A i A i+1 representing line segmentsA i A i+1 The set of all the points above is composed of,A 0 =S，A n+1 =T；
s22, establishing path lengthDegree factorf 2 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
s23, calculating an objective optimization functionf：
S3, solving the intermediate node coordinate which enables the target optimization function to be maximumA i (x i ,y i )，i=1,2,…,nAnd obtaining a planned path.
Furthermore, the method for planning a path according to a starting point and an end point further includes:
establishing a path smoothing factorf 3 ：
in the formula ,θ i is composed ofA i A i+1 And withA i+1 A i+2 Is/are as follows the included angle is formed by the angle of inclination,
，A 0 =S，A n+1 =T；
calculating an objective optimization function:
。
furthermore, the method for planning a path according to a starting point and an end point further includes:
establishing a directional consistency factorf 4 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
calculating an objective optimization function:
。
further, a genetic algorithm is used to solve the coordinates of the intermediate nodes that maximize the objective optimization function.
Further, if a dynamic obstacle is identified and the maximum distance deviating from the current planned path when obstacle avoidance is carried out is larger than a set threshold value, path planning is carried out again.
Furthermore, 2 laser radars are installed on the AGV, the maximum scanning range of each laser radar is-135 degrees to +135 degrees, and the AGV is used for realizing obstacle detection within the range of 360 degrees around the AGV.
Further, the method for identifying whether the AGV trolley has the dynamic obstacle within the safety distance comprises the following steps:
loading map data of a storage area, rasterizing the loaded map data, and marking the grid position of a fixed obstacle data point;
acquiring a barrier echo data point output by a laser radar in real time, and determining an echo data point with a distance to an AGV car smaller than a safe distance;
if the grid where the echo data point is located is occupied by the map fixed obstacle data point, the echo data point belongs to a fixed obstacle, and no dynamic obstacle exists in a safe distance; otherwise, the echo data point belongs to a dynamic obstacle, and the dynamic obstacle exists in the safe distance.
Furthermore, the map data is obtained by processing fixed obstacle echo data output by the laser radar when the AGV moves for one circle in the storage area.
In a second aspect, the present invention provides a method for planning a path by using the system, including the following steps:
a controller on the AGV receives a moving instruction sent by an upper computer, and analyzes a start point coordinate and an end point coordinate from the instruction;
the controller plans a path based on the starting point coordinate and the end point coordinate and controls the AGV to move according to the planned path;
the controller identifies the obstacles based on the distance and direction data of the obstacles measured by the laser radar through transmitting and scanning laser beams;
and if the fact that the dynamic obstacle exists in the safe distance of the AGV trolley is identified, avoiding the obstacle, replanning the path, and controlling the AGV trolley to move according to the new path.
Compared with the prior art, the invention has the following beneficial effects.
According to the system, the AGV trolley, the controller arranged on the AGV trolley, the laser radar and the communication module which are connected with the controller are arranged, data interaction is carried out between the communication module and the controller, the upper computer is arranged in a dispatching center, the controller carries out path planning according to the start point coordinate and the end point coordinate issued by the upper computer and controls the AGV trolley to move according to the planned path, obstacle identification is carried out on the basis of the distance and direction data of obstacles measured by the laser radar through transmitting and scanning laser beams, if dynamic obstacles are identified in the safety distance of the AGV trolley, the obstacles are avoided and the path is re-planned, the AGV trolley is controlled to move according to the new path, and automatic planning of the walking path of the AGV trolley is achieved. The method and the system can not only carry out path planning, but also automatically carry out path planning again after identifying the dynamic barrier, so that the traveling path of the AGV trolley is always optimal.
Drawings
Fig. 1 is a block diagram of an intelligent warehouse logistics path planning system according to an embodiment of the present invention. In the figure, 1-an AGV trolley, 2-a controller, 3-a laser radar, 4-a communication module and 5-an upper computer.
Fig. 2 is a schematic diagram of path smoothing factor calculation.
FIG. 3 is a schematic diagram of the calculation of the orientation consistency factor.
Fig. 4 is a schematic distribution diagram of map data points and real-time echo data points, where the small black dots are map data points and the larger gray dots are real-time echo data points.
Fig. 5 is a flowchart of a method for planning a path by using the system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and more obvious, the present invention is further described below with reference to the accompanying drawings and the detailed description. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
Fig. 1 is a block diagram of an intelligent warehouse flow path planning system according to an embodiment of the present invention, including: the system comprises an AGV trolley 1, a controller 2 arranged on the AGV trolley 1, a laser radar 3 and a communication module 4 which are connected with the controller 2, and an upper computer 5 which is arranged in a dispatching center and performs data interaction with the controller 2 through the communication module 4; the controller 2 plans a path according to a start point coordinate and a terminal point coordinate issued by the upper computer 5, controls the AGV trolley 1 to move according to the planned path, identifies obstacles based on distance and direction data of the obstacles measured by the laser radar 3 through transmitting and scanning laser beams, avoids the obstacles and replans the path if dynamic obstacles are identified in the safety distance of the AGV trolley 1, and controls the AGV trolley 1 to move according to a new path.
In this embodiment, the system mainly comprises AGV dolly 1, controller 2 installed on AGV dolly 1, laser radar 3 and communication module 4, host computer 5 that link to each other with controller 2 etc.. The connection relationship of the parts is shown in fig. 1, and the function of each part is described separately below.
The AGV trolley 1, namely the logistics robot, is a main goods transportation tool for warehousing and logistics, and achieves operations such as warehousing and ex-warehouse of goods. The AGV trolley 1 can move autonomously and can reach a target position according to the command of the upper computer 5. The moving part is controlled by more than two continuous rotating rudders. Generally, the AGV 1 is required to freely grab and place the material, so that a multi-degree-of-freedom manipulator device is generally required to be arranged on the AGV 1.
And the controller 2 is a control and data processing center of the AGV trolley 1 and is used for coordinating the work of other modules by outputting various control signals and completing main data processing tasks. For example, path planning is performed according to an instruction issued by the upper computer 5; autonomous navigation, obstacle avoidance and the like are realized based on position data of the obstacle detected by the laser radar 3. The controller 2 is generally mainly composed of a single chip microcomputer and peripheral circuits, for example, a main control chip STM3F103VCT6 with a high-performance ARM cotex-M3 microprocessor as an inner core can be adopted.
And the laser radar 3 is used for detecting the obstacle by transmitting a laser scanning signal to the front of the AGV trolley 1 and processing the received echo signal. The laser radar 3 can directly measure the distance of a target (obstacle), and the laser radar 3 is arranged on a rotating mechanism of the AGV trolley 1, rotates in a certain sector area along the horizontal direction during working, and has very narrow laser beams, so that the azimuth angle of the target can be measured while distance measurement is carried out, and polar coordinate position data of the target can be obtained.
And the communication module 4 is used for realizing data communication between the controller 2 and the upper computer 5. For example, the instruction sent by the upper computer 5 is received, and the data is uploaded to the upper computer 5. The communication module 4 generally employs a wireless communication mode, for example, an XBee wireless module may be employed. The XBee wireless communication module 4 has the advantages of long transmission distance, low power consumption and the like, and is produced by the united states DIGI company, and the working frequency can reach 2.4GHz.
And the upper computer 5 is also called a cloud server, is installed in the storage dispatching center and is used for realizing the dispatching of all AGV trolleys 1 in the whole storage area. For example, sending instructions to the AGV cart 1; and receiving the data uploaded by the AGV trolley 1. Because the data processing capacity of the controller 2 on the AGV cart 1 is limited, the upper computer 5 may also replace the AGV cart 1 to complete data processing tasks with relatively large calculation amount, such as path planning.
The controller 2 of the present embodiment performs path planning according to the start point and end point coordinates given by the upper computer 5. Since the positions of fixed facilities (fixed obstacles) and occupied areas in the warehouse area are generally unchanged, the environmental map data of the warehouse area can be measured offline and stored (periodically updated), the map data is read in when the route planning is performed, and the route planning is performed based on the position data of the fixed obstacles in the map data, namely, the route should avoid the fixed obstacles and keep the minimum safe distance. The path planning algorithms are many, and the algorithms commonly used include an a-star algorithm, an ant colony algorithm, a genetic algorithm, a fast random search tree algorithm, and the like, and the specific planning method is not limited in this embodiment. In the prior art, after path planning is finished, the AGV trolley 1 is controlled to walk according to a planned path all the time, and navigation is continued along the originally planned path after obstacle avoidance even if a dynamic obstacle is encountered. Since obstacle avoidance often requires a detour over a long distance to re-enter the original path, obstacle avoidance may increase the actual travel distance by a large amount. In fact, even if the detour is not needed, the path length from any point (such as an obstacle avoidance point) in the middle of the original path to the end point cannot be guaranteed, and the path length is the path with the shortest distance in all paths with the starting point and the end point of the point unchanged. For example, the route ABC is a route having the shortest distance from the start point C to the end point a, but the second half BC of the route is not necessarily a route having the shortest distance from the start point C to the end point B. Therefore, it is necessary to perform path planning again when obstacle avoidance is required. Only the starting point and the end point are unchanged when the path is re-planned.
As an alternative embodiment, the method for planning a path according to a start point and an end point includes:
s1, setting a path as a broken line formed by sequentially connecting the following nodes:
S(x s ,y s )，A 1 (x 1 ,y 1 )，A 2 (x 2 ,y 2 )，…，A n (x n ,y n )，T(x t ,y t )
wherein ,S(x s ,y s )、T(x t ,y t ) Respectively a start point coordinate and an end point coordinate,A i (x i ,y i ) Is the middle firstiThe coordinates of the individual nodes are then calculated,i=1,2,…,n，nis the number of intermediate nodes;
s2, establishing a target optimization function;
s21, establishing barrier factorsf 1 ：
in the formula ,Bis a set of obstacle data points that is,A i A i+1 representing line segmentsA i A i+1 The set of all the points above is composed of,A 0 =S，A n+1 =T；
s22, establishing a path length factorf 2 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
s23, calculating an objective optimization functionf：
S3, solving the intermediate node coordinate which enables the target optimization function to be maximumA i (x i ,y i )，i=1,2,…,nAnd obtaining a planned path.
This embodiment provides a technical solution for performing path planning according to a starting point and an end point. The path planning is mainly to obtain a planned path by solving an objective optimization function, so the quality of the objective optimization function directly influences the path planning result. The improvement of this embodiment (including the latter two embodiments) over the prior art is to improve the quality of the path planning by improving the objective optimization function. In this embodiment, a polygonal line path formed by connecting a start point, an intermediate node, and an end point is set; then, establishing an objective optimization function based on the starting point, the intermediate node and the end point; and finally, solving the objective optimization function to obtain the position coordinates of the intermediate node, thereby obtaining the required planning path. The objective optimization function of the present embodiment is obtained by multiplying a plurality of factors or sub-functions, wherein the first factor is the obstacle factorf 1 The obstacle factor has only two values, 0, 1, if the path can bypass the obstacle,f 1 =1; otherwisef 1 =0, path planning fails. According to the above formula, of the whole pathf 1 Equal to all broken line segmentsf 1 Product of factors wheniEach broken line segmentA i A i+1 When the intersection of the set formed by all the points and the obstacle data point set is empty, the broken line segment is representedA i A i+1 Does not intersect with the region of the obstacle,f 1 (i) =1; otherwise, representing a broken line segmentA i A i+1 And barrier regionThe cross-over is carried out,f 1 (i) =0. The second factor being a path length factorf 2 Is provided withf 2 In order to minimize the length of the planned path. Such as abovef 2 The numerator is the straight-line distance from the starting point to the end point, and the denominator is the distance of each polyline and the total length of the path. The greater the total length of the path is,f 2 the smaller, when the total path length is equal to the linear distance from the starting point to the end point,f 2 the maximum is 1, and the planned path is a straight line from the starting point to the end point.
As an optional embodiment, the method for planning a path according to a start point and an end point further includes:
establishing a path smoothing factorf 3 ：
in the formula ,θ i is composed ofA i A i+1 And withA i+1 A i+2 Is/are as follows the included angle is formed by the angle of inclination,
，A 0 =S，A n+1 =T；
calculating an objective optimization function:
。
this embodiment is a modification of the previous embodiment. Objective optimization function of the present embodimentfAdding a path smoothing factorf 3 At this timef=f 1 *f 2 *f 3 . The smoothness factor reflects the smoothness of the path based on the angle formed by every two adjacent polylines in a pathθThe sharpness of (c) was calculated. Two adjacent fold lines form an angleθThe smaller the sharpness, the smoother the smoothness and the smaller the smoothness factor. As shown in fig. 2, whenθIs equal to 180 0 When the two adjacent fold lines are collinear, it is equivalent to not havingChanging direction, the maximum smooth factor is 1; when in useθIs equal to 0 0 In the process, the route is equivalent to the return of the original route and belongs to the path with the least ideal, the minimum smooth factor is 0, and the situation is not allowed to occur in the path planning. Therefore, the smoothing factor corresponding to every two adjacent broken lines is availableθAnd 180 0 (i.e. the
Radian) as shown above. The smoothing factor of the whole path is equal to the product of the smoothing factors of all adjacent two polylines.
As an optional embodiment, the method for planning a path according to a start point and an end point further includes:
establishing a directional consistency factorf 4 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
calculating an objective optimization function:
。
this embodiment is again an improvement of the previous embodiment. Objective optimization function of the present embodimentfOn the basis of the above embodiment, a direction consistency factor is addedf 4 At this timef=f 1 *f 2 *f 3 *f 4 . The direction consistency factor reflects the totality of the planned pathThe degree to which the direction deviates from the direction of the straight line ST from the starting point to the end point. The direction consistency factor for the entire path is still equal to the product of the direction consistency factors for each polyline (exponential-sum equivalent of the above equation). The direction coincidence factor of each broken line in this embodiment is obtained based on the difference between the slope of the broken line and the slope of the straight line ST, and the smaller the difference between the two slopes is, the larger the direction coincidence factor is, and when the two slopes are equal, the broken line is parallel to the straight line ST, and the direction coincidence factor is 1. According to the above formula, the direction-consistent factor for a single polyline may be greater than 1, since the difference between the two slopes may be positive or negative, since the polyline direction changes may be either off (away) or off (close) to the line ST (with cancellation), as shown in fig. 3. Denominator is added in the above formula
In order to avoid that the direction consistency factor is too small to affect the value of the overall objective optimization function, the absolute value of the slope (difference) in the numerator can approach infinity, for example, the slope of a straight line perpendicular to the horizontal axis is infinity, and exp (∞) =0.
As an alternative embodiment, a genetic algorithm is used to solve the coordinates of the intermediate nodes that maximize the objective optimization function.
The embodiment provides a technical scheme for solving the objective optimization function. The present embodiment uses a genetic algorithm to solve the objective optimization function. The genetic algorithm is a mature existing optimization algorithm, and a detailed description thereof is not provided herein.
As an alternative embodiment, if a dynamic obstacle is identified and the maximum distance from the currently planned path when obstacle avoidance is performed is greater than a set threshold, path planning is performed again.
The present embodiment limits the timing of re-planning the path. In the foregoing embodiment, path planning is performed again as long as obstacle avoidance is required, in order to reduce the number of times of path planning and reduce data processing burden, the present embodiment determines whether to perform path planning again according to the maximum distance from the currently planned path when obstacle avoidance is performed, and if the maximum distance is greater than a set threshold, it indicates that the current planned path is deviated farther, and many unnecessary paths are left without re-planning; on the contrary, the increased distance is not much according to the current planned path, which is acceptable and does not need to be re-planned.
As an optional embodiment, 2 laser radars 3 are installed on the AGV 1, and the maximum scanning range of each laser radar 3 is-135 ° +135 °, so as to detect obstacles within a range of 360 ° around the AGV.
The present embodiment limits the number of laser radars 3. In this embodiment, in order to detect obstacles within a range of 360 degrees around the AGV cart 1, 1 lidar 3 is respectively installed in front and at the back of the AGV cart 1, and is respectively responsible for different azimuth areas. The maximum scanning range of each laser radar 3 is-135 degrees to +135 degrees, so that signals emitted by 2 laser radars 3 can completely cover the scanning range of 360 degrees.
As an alternative embodiment, the method for identifying whether there is a dynamic obstacle within the safety distance of the AGV 1 includes:
loading map data of a warehouse area, rasterizing the loaded map data, and marking the grid position of a fixed obstacle data point;
acquiring a barrier echo data point output by a laser radar 3 in real time, and determining an echo data point with a distance smaller than a safe distance from an AGV trolley 1;
if the grid where the echo data point is located is occupied by the map fixed obstacle data point, the echo data point belongs to a fixed obstacle, and no dynamic obstacle exists in the safe distance; otherwise, the echo data point belongs to a dynamic obstacle, and the dynamic obstacle exists in the safe distance.
The embodiment provides a technical scheme for identifying dynamic obstacles. The present embodiment utilizes lidar 3 mounted on the AGV cart 1 for obstacle (target) detection, including fixed obstacles and dynamic obstacles. If there is an obstacle around the AGV 1, the laser signal emitted by the laser radar 3 will be reflected back to an echo signal and received by the laser radar 3 after encountering the obstacle. Therefore, it is generally considered that an obstacle is detected as long as an echo signal having a certain amplitude is received. Because the laser radar 3 is close to the obstacle and the laser beam is narrow, the laser beam emitted by the laser radar 3 during the scanning process encounters the same obstacle many times, so that a plurality of echo signals are generated. If the echo signal is displayed on a display, many dense and rough data points are seen, and not one point represents an obstacle, but one obstacle corresponds to a large piece of data points, as shown in fig. 4. The embodiment not only needs to detect the obstacles, but also needs to identify the dynamic obstacles from the obstacles. The moving target detection method is various, and the embodiment provides a simpler dynamic obstacle identification method according to a specific application scene. Since the AGV 1 of this embodiment is applied to a relatively fixed environment, the size and position of the fixed obstacle in the environment generally change little; more frequently, the change is a dynamic obstruction, such as a worker. Since different obstacles have different position coordinates, the position coordinates of the dynamic obstacle and the fixed obstacle are also different. The embodiment distinguishes dynamic obstacles from fixed obstacles by using the characteristic, and the technical principle is as follows: the echo data point detected in real time belongs to a dynamic obstacle if the echo data point coordinates detected in real time are both different from the data point coordinates of a fixed obstacle in the map. The specific identification method comprises the following steps: firstly, rasterizing loaded map data; determining an echo data point which is less than a safe distance away from the AGV trolley 1, and if a square where the echo data point is located is occupied by a map fixed obstacle data point, determining that the echo data point belongs to a fixed obstacle; if not occupied, the echo data point is considered to belong to a dynamic obstacle, i.e. a dynamic obstacle exists within the safe distance. As shown in fig. 4.
As an optional embodiment, the map data is obtained by processing fixed obstacle echo data output by the laser radar 3 after the AGV trolley 1 moves for one circle in the storage area.
The embodiment provides a technical scheme for manufacturing the environment map. The map data is prepared and stored before work. The map data is obtained by processing fixed obstacle echo data output by the laser radar 3 after the AGV trolley 1 moves for one circle in a working scene through SLAM software, and data points in the map are two-dimensional discrete points in a world coordinate system. The map data are obtained based on a radar imaging principle, and the obtained two-dimensional discrete points are equivalent to pixel points in the barrier video image. The higher the resolution of the lidar 3, the more data points and the higher the imaging accuracy. According to the aforementioned dynamic obstacle identification principle, the map data of the present embodiment should only include echo data points of fixed obstacles, otherwise the identification of dynamic obstacles would be affected. Therefore, only fixed obstacles and no dynamic obstacles are allowed in the surrounding environment in the process of acquiring the map data; if the data points of the dynamic obstacles are not mixed in slightly, the elimination should be tried.
Fig. 5 is a flowchart of a method for planning a path by using the system according to an embodiment of the present invention, including the following steps:
101, a controller 2 on an AGV trolley 1 receives a moving instruction sent by an upper computer 5, and analyzes a starting point coordinate and a terminal point coordinate from the instruction;
102, the controller 2 performs path planning based on the start point coordinate and the end point coordinate, and controls the AGV trolley 1 to move according to the planned path;
103, the controller 2 identifies the obstacle based on the distance and direction data of the obstacle measured by the laser radar 3 by emitting and scanning the laser beam;
and step 104, if the fact that the dynamic obstacle exists in the safe distance of the AGV trolley 1 is identified, avoiding the obstacle, planning a path again, and controlling the AGV trolley 1 to move according to the new path.
Compared with the technical solution of the system embodiment shown in fig. 1, the method of this embodiment has similar implementation principle and technical effect, and is not described herein again.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent storage flow path planning system, comprising: the system comprises an AGV, a controller, a laser radar and a communication module, wherein the controller is arranged on the AGV, and the laser radar and the communication module are connected with the controller; the controller plans a path according to a start point coordinate and a terminal point coordinate issued by the upper computer, controls the AGV to move according to the planned path, identifies obstacles based on distance and direction data of the obstacles measured by the laser radar through transmitting and scanning laser beams, avoids the obstacles and replans the path if dynamic obstacles are identified in the safety distance of the AGV, and controls the AGV to move according to a new path.
2. The intelligent warehouse logistics path planning system of claim 1, wherein the method of path planning based on the starting point and the ending point comprises:
s1, setting a path as a broken line formed by sequentially connecting the following nodes:
S(x s ,y s )，A 1 (x 1 ,y 1 )，A 2 (x 2 ,y 2 )，…，A n (x n ,y n )，T(x t ,y t )
wherein ,S(x s ,y s )、T(x t ,y t ) Respectively a start point coordinate and an end point coordinate,A i (x i ,y i ) Is the middle oneiThe coordinates of the individual nodes are then,i=1,2,…,n，nis the number of intermediate nodes;
s2, establishing a target optimization function;
s21, establishing barrier factorsf 1 ：
in the formula ,Bis a set of obstacle data points that are,A i A i+1 representing line segmentsA i A i+1 The set of all the points above is composed of,A 0 =S，A n+1 =T；
s22, establishing a path length factorf 2 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
s23, calculating an objective optimization functionf：
S3, solving the intermediate node coordinate which enables the objective optimization function to be maximumA i (x i ,y i )，i=1,2,…,nAnd obtaining a planned path.
3. The intelligent warehouse workflow path planning system of claim 2 wherein the method for path planning based on start and end points further comprises:
establishing a path smoothing factorf 3 ：
in the formula ,θ i is composed ofA i A i+1 And withA i+1 A i+2 Is the included angle is formed by the angle of inclination,
，A 0 =S，A n+1 =T；/>
calculating an objective optimization function:
。
4. the intelligent warehouse workflow path planning system of claim 3 wherein the method for path planning based on start and end points further comprises:
establishing a directional agreement factorf 4 ：
in the formula ,x 0 =x s ，y 0 =y s ，x n+1 =x t ，y n+1 =y t ；
calculating an objective optimization function:
。
5. the intelligent warehouse workflow path planning system of claim 4 wherein genetic algorithms are employed to solve for intermediate node coordinates that maximize the objective optimization function.
6. The intelligent warehouse workflow path planning system of claim 1 wherein if dynamic obstacles are identified and the maximum distance from the currently planned path when obstacle avoidance is performed is greater than a set threshold, then path planning is resumed.
7. The intelligent warehouse logistics path planning system of claim 1, wherein 2 lidar are mounted on the AGV cart, each lidar having a maximum scanning range of-135 ° for enabling obstacle detection within 360 ° around the AGV.
8. The intelligent warehouse logistics path planning system of claim 1, wherein the method of identifying whether a dynamic obstruction exists within a safe distance of an AGV includes:
loading map data of a warehouse area, rasterizing the loaded map data, and marking the grid position of a fixed obstacle data point;
acquiring obstacle echo data points output by a laser radar in real time, and determining echo data points with the distance from the AGV to the obstacle echo data points being less than a safe distance;
if the grid where the echo data point is located is occupied by the map fixed obstacle data point, the echo data point belongs to a fixed obstacle, and no dynamic obstacle exists in the safe distance; otherwise, the echo data point belongs to a dynamic obstacle, and the dynamic obstacle exists in the safe distance.
9. The intelligent warehouse flow path planning system of claim 8 wherein the map data is obtained by processing laser radar-output fixed obstacle echo data for a single turn of AGV cart movement in the warehouse area.
10. A method for path planning using the intelligent warehouse flow path planning system of claim 1, comprising the steps of:
a controller on the AGV receives a moving instruction sent by an upper computer, and analyzes a starting point coordinate and a terminal point coordinate from the instruction;
the controller plans a path based on the starting point coordinate and the end point coordinate and controls the AGV to move according to the planned path;
the controller identifies the obstacles based on the distance and direction data of the obstacles measured by the laser radar through transmitting and scanning laser beams;
and if the fact that the dynamic obstacle exists in the safe distance of the AGV trolley is identified, the obstacle is avoided, the path is planned again, and the AGV trolley is controlled to move according to the new path.
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