CN115562276A - Path planning method, device, equipment and computer readable storage medium - Google Patents
Path planning method, device, equipment and computer readable storage medium Download PDFInfo
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- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
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Abstract
The application discloses a path planning method, a path planning device, a path planning equipment and a computer readable storage medium, and belongs to the technical field of wireless communication. According to the method, when the unmanned forklift reaches a preset position, whether deviation exists in the placing position of the goods to be taken is detected, and the preset position is a pause position when the unmanned forklift is ready to take the goods; if yes, re-determining the coordinates of the goods taking points according to the placing positions; planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by using a genetic algorithm; and controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken. The unmanned forklift is suitable for the goods taking scene with real-time change of goods positions.
Description
Technical Field
The application relates to the field of intelligent industrial robots, in particular to a path planning method, a path planning device, path planning equipment and a computer-readable storage medium.
Background
With the rapid spread of AGVs (Automated Guided vehicles), there is a scenario: the artificial forklift puts the goods at the connecting position, and the unmanned forklift goes to the connecting position to take the goods. Because artifical fork truck can't guarantee to get the precision of putting the goods, thereby there is the goods to put the too big risk that leads to unmanned fork truck to hit the goods of deviation.
One method for solving the problem is to draw a compact limiting frame on the ground, set the goods taking and placing positions in advance and draw a running route of the unmanned forklift, firstly ensure that the artificial forklift puts goods into the limiting frame during goods taking and placing operation, and then the unmanned forklift runs according to the preset route and takes and places the goods. The method can greatly increase the difficulty of manual operation and greatly reduce the operation efficiency.
Therefore, the technical problem that the unmanned forklift is difficult to adapt to real-time change of the goods position exists at present.
The above is only for the purpose of assisting understanding of the technical solutions of the present application, and does not represent an admission that the above is prior art.
Disclosure of Invention
The application mainly aims to provide a path planning method, a path planning device, path planning equipment and a computer readable storage medium, and aims to solve the technical problem that an unmanned forklift is difficult to adapt to real-time change of goods positions.
In order to achieve the above object, the present application provides a path planning method, including the following steps:
when an unmanned forklift reaches a preset position, detecting whether deviation exists in the placing position of an object to be picked, wherein the preset position is a pause position when the unmanned forklift is ready to pick the object;
if yes, re-determining the coordinates of the goods taking points according to the placing positions;
planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by using a genetic algorithm;
and controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
Optionally, the step of planning the pick-up route of the unmanned forklift from the preset position to the pick-up point coordinate by using a genetic algorithm comprises:
acquiring an initial population based on a Bezier curve;
continuously carrying out selection iteration on the initial population until the fitness of the optimal individual reaches a given threshold value to obtain a final generation population;
and decoding the individuals of the final generation total group to obtain target control points, wherein the curve fitted by the target control points is the goods taking route.
Optionally, the step of obtaining an initial population based on bezier curves comprises:
acquiring an initial individual based on the Bezier curve;
setting the initial population number as a first preset number,
and repeating the step of obtaining the initial individuals for the times of reaching the preset number to obtain the initial population with the first preset number.
Optionally, the step of obtaining initial individuals based on the bezier curve control points comprises:
selecting the coordinates of the preset position and the goods taking point as a first control point and a last control point of the Bezier curve;
inserting a second preset number of control points between the first control point and the last control point, wherein the coordinates of the second preset number of control points are randomly acquired;
and coding all control points of the Bezier curve in sequence to obtain an initial individual.
Optionally, the step of inserting a second preset number of control points between the first control point and the last control point, where the coordinates of the second preset number of control points are randomly obtained further includes:
and setting a penultimate control point, wherein the last control point is positioned on the same straight line, and the direction of the straight line is perpendicular to the front surface of the tray, so that when the unmanned forklift reaches the coordinate of the goods taking point, the posture of the body of the unmanned forklift is consistent with that of the tray of the goods to be taken.
Optionally, the step of inserting a second preset number of control points between the first control point and the last control point, where the coordinates of the second preset number of control points are randomly obtained further includes:
and setting the second preset number of control points to be positioned in a circle with the connecting line of the first control point and the last control point as the diameter so as to reduce the iteration number, wherein the second control point is the inner point of the first control point and the third control point.
Optionally, the step of controlling the unmanned forklift to reach the picking point coordinates according to the picking route to complete picking is followed by:
when the unmanned forklift reaches the goods taking point coordinates, controlling the unmanned forklift to fork goods to be taken;
and after the unmanned forklift finishes goods taking, controlling the unmanned forklift to return to the preset position according to the goods taking route.
In addition, to achieve the above object, the present application further provides a path planning apparatus, including:
the device comprises a detection module, a control module and a control module, wherein the detection module is used for detecting whether deviation exists in the placing position of an object to be picked when an unmanned forklift reaches a preset position, and the preset position is a pause position when the unmanned forklift is ready to pick the object;
the determining module is used for re-determining the coordinates of the goods taking points according to the placing positions if the goods taking points are located at the placing positions;
the planning module is used for planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by utilizing a genetic algorithm;
and the control module is used for controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish the goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
In addition, to achieve the above object, the present application further provides a path planning apparatus, including: a memory, a processor and a path planning program stored on the memory and executable on the processor, the path planning program being configured to implement the steps of the path planning method according to any of claims 1 to 7.
Further, to achieve the above object, the present application also provides a computer-readable storage medium having a path planning program stored thereon, which when executed by a processor implements the steps of the path planning method according to any one of claims 1 to 7.
In order to enable the unmanned forklift to be suitable for a scene with a real-time change of the goods position, after the unmanned forklift trolley reaches a preset position, whether deviation exists in the placing position of the goods to be taken or not is detected, and the coordinate coordinates of the goods taking point are further determined again. On the basis, the scheme further designs a genetic algorithm based on the Bezier curve, and is responsible for planning a goods taking route from a preset position to a goods taking point coordinate, so that the unmanned forklift can complete a goods taking task under the condition of the position deviation of goods to be taken, and the goods taking scene that the goods position of the unmanned forklift changes in real time is realized.
Drawings
Fig. 1 is a schematic structural diagram of a path planning apparatus of a hardware operating environment according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a first embodiment of the path planning method of the present application;
FIG. 3 is a flowchart of a genetic algorithm according to a first embodiment of the path planning method of the present application;
fig. 4 is a schematic diagram of a third embodiment of the path planning method according to the present application for picking up goods by an unmanned forklift;
fig. 5 is a functional module diagram of a first embodiment of a path planning apparatus according to the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a path planning device in a hardware operating environment according to an embodiment of the present application.
As shown in fig. 1, the path planning apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the path planning apparatus, and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and a path planning program.
In the path planning apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with other apparatuses; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the path planning apparatus of the present application may be disposed in the path planning apparatus, and the path planning apparatus calls the path planning program stored in the memory 1005 through the processor 1001 and executes the path planning method provided in the embodiment of the present application.
An embodiment of the present application provides a path planning method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the path planning method according to the present application.
In this embodiment, the path planning method includes:
step S10: when the unmanned forklift reaches a preset position, detecting whether deviation exists in the placing position of an object to be picked, wherein the preset position is a pause position when the unmanned forklift is ready to pick the object;
step S20: if yes, re-determining the coordinates of the goods taking points according to the placing positions;
specifically, in order to make unmanned fork truck be applicable to the scene that the goods position changes in real time, this application just detects whether there is the deviation in the locating position of waiting to get goods after unmanned fork truck reachs preset position, if wait to get goods because artifical fork truck's arbitrary putting, there is the deviation in the position, then further confirm again and get goods point coordinate. In order to detect the placement position of the goods to be picked up, and thus determine the coordinates of the goods picking point, a position detection device capable of sensing the goods to be picked up is required.
Optionally, when the position detecting device is an optical imaging device, the optical imaging device may be a depth camera, and the depth camera can detect a depth distance of the shooting space. The distance between each point in the image and the camera is obtained through the depth camera, and the two-dimensional coordinates of the point in the 2D image are added, so that the three-dimensional space coordinates of each point in the image can be obtained. The camera imaging device is used for shooting a photo of an object to be picked up after the unmanned forklift reaches a preset position, determining the relative position of the placing position of the object to be picked up and the unmanned forklift, and determining the coordinate of a pickup point in a world coordinate system by utilizing a camera imaging principle and coordinate system conversion.
Optionally, when the displacement detection device is a laser radar, the laser radar is composed of a laser transmitter, an optical receiver, a turntable, an information processing system and the like, an electric pulse is converted into an optical pulse through a laser to transmit a detection signal to a target, then a received signal (target echo) reflected from the target is compared with the transmitted signal, and after the processor performs appropriate processing, information related to the goods to be taken, such as parameters of distance, direction, height, speed, posture, even shape and the like of the goods to be taken from the unmanned forklift, can be obtained, and further comprehensive analysis is performed, so that coordinates of the goods taking point can be obtained.
Step S30: planning a goods taking route of the unmanned forklift from the preset position to the coordinates of the goods taking point by using a genetic algorithm;
specifically, genetic Algorithm (Genetic Algorithm) is a kind of randomized search method evolved by the evolution law (survival of the fittest, and high-and-low-rejection Genetic mechanism) in the biology world. The method is mainly characterized in that the method directly operates the structural object without the limitation of derivation and function continuity; the method has the advantages of inherent hidden parallelism and better global optimization capability; by adopting a probabilistic optimization method, the optimized search space can be automatically acquired and guided, the search direction can be adaptively adjusted, and a determined rule is not needed. The application uses a genetic algorithm for planning a control point of a route from a preset position to a pickup point of an unmanned forklift. Referring to fig. 3, fig. 3 is a flowchart of a genetic algorithm of the first embodiment of the path planning method of the present application.
Further, the step S30 includes:
step S31: acquiring an initial population based on a Bezier curve;
step S32: continuously carrying out selection iteration on the initial population until the fitness of the optimal individual reaches a given threshold value to obtain a final generation population;
step S33: and decoding the individuals of the final generation total group to obtain target control points, wherein a curve fitted by the target control points is the pickup route.
In particular, since the genetic algorithm is a search algorithm generated by evolutionary theory and genetic mechanism, much biogenetic knowledge is used in the algorithm, and the following terms used in the embodiment describe that chromosomes can also be called genotypic individuals (indeviduals), a certain number of individuals form a population (population), and the number of individuals in the population is called the population size. The degree of adaptation of each individual to the environment is called fitness (fitness). In order to embody the adaptability of the chromosome, a function capable of measuring each chromosome in the problem, namely an adaptability function, is introduced. This function is to calculate the probability that an individual is used in the population.
The process of obtaining the initial population is actually a process of parameterizing an actual problem, and after the initial population is subjected to genetic iteration for a plurality of times, the optimal result screened by the fitness function is decoded to obtain a solution of the actual problem.
Genetic iteration comprises selection, crossing and mutation operations. And (4) selecting operation, namely acting a selection operator on the group. The purpose of selection is to inherit optimized individuals directly to the next generation or to generate new individuals by pairwise crossing and then to inherit them to the next generation. The selection operation is based on fitness evaluation of individuals in the population. And (4) performing cross operation, namely applying a cross operator to the group. What plays a core role in genetic algorithms is the crossover operator. And (4) mutation operation, namely acting mutation operators on the population. I.e., to vary the gene values at certain loci of the individual strings in the population.
The fitness in the evolutionary theory represents the adaptability of an individual to the environment and also represents the ability of the individual to reproduce offspring. The fitness function of the genetic algorithm is also called an evaluation function, is an index for judging the degree of goodness of individuals in a population, and is evaluated according to an objective function of a problem to be solved.
The genetic algorithm does not generally need other external information in the search evolution process, and only uses an evaluation function to evaluate the quality of an individual or a solution and is used as a basis for subsequent genetic operation. In the genetic algorithm, the fitness function is compared and ranked and the selection probability is calculated on the basis of the ranking, so that the value of the fitness function takes a positive value. It follows that, in many cases, it is necessary to map the objective function to a fitness function that takes the form of a maximum and has a non-negative function value.
The fitness function is designed to mainly meet the following conditions:
(a) Single valued, continuous, non-negative, maximum
(b) Reasonable and consistent
(c) The calculated amount is small
(d) The universality is strong.
In a particular application, the fitness function is designed in accordance with the requirements of the problem itself. The fitness function design directly affects the performance of the genetic algorithm. The fitness function adopted in this embodiment is the sum of squares of curvatures of all fitting points on a bezier curve, where F represents the fitness function, and assuming that n fitting points are shared by bezier curves fitted with five control points obtained after decoding of the kth group of individuals in the T-th generation population, and the curvature of the curve at the ith fitting point is c (i), the fitness function of the individual is
Assuming T groups of individuals are shared by the T generation after selection, crossover and mutation, the probability that the k (0<k ≦ T) group of individuals is selected during inheritance is
And (T-20) groups with lower probability are removed from the T groups of individuals in the T generation population, and the rest 20 groups of individuals form a T +1 generation population.
The condition for genetic termination is set as
max(P(1),P(2),…,P(t))≥0.99。
And when the fitness of the optimal individual reaches a given threshold value, or the fitness of the optimal individual and the population fitness do not rise any more, or the iteration times reach a preset algebra, terminating the algorithm.
And the curve fitted by the individual decoded control points meeting the conditions is the pickup route.
Step S40: and controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish the goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
In this embodiment, in order to make the unmanned forklift suitable for a scene in which the position of the goods changes in real time, after the unmanned forklift truck arrives at the preset position, whether the placing position of the goods to be picked has a deviation is detected, and the coordinate of the goods pickup point is further determined again. On the basis, the embodiment further designs a genetic algorithm based on the Bezier curve, and is responsible for planning a route from a preset position to a goods taking point coordinate, so that the unmanned forklift can complete a goods taking task under the condition of the position deviation of goods to be taken, and the unmanned forklift is suitable for a goods taking scene with a real-time change of goods positions.
Further, based on the above embodiment, there is provided a second embodiment of the present application, where the step S31 includes:
step S34: acquiring an initial individual based on the Bezier curve;
step S35: setting the initial population quantity as a first preset quantity;
further, step S34 includes:
step S50: selecting the coordinates of the preset position and the goods taking point as a first control point and a last control point of the Bezier curve;
step S51: inserting a second preset number of control points between the first control point and the last control point, wherein the coordinates of the second preset number of control points are randomly acquired;
step S52: and coding all control points of the Bezier curve in sequence to obtain an initial individual.
Further, step S51 includes:
step S53: and setting a penultimate control point, wherein the last control point is positioned on the same straight line, and the direction of the straight line is perpendicular to the front surface of the tray, so that when the unmanned forklift reaches the coordinate of the goods taking point, the posture of the body of the unmanned forklift is consistent with that of the tray of the goods to be taken.
Step S54: and setting the second preset number of control points to be positioned in a circle with the connecting line of the first control point and the last control point as the diameter so as to reduce the iteration number, wherein the second control point is the inner point of the first control point and the third control point.
Specifically, unmanned fork truck is unmanned fork truck in this embodiment, and unmanned fork truck includes automobile body and tray, sets up five control points altogether, and the second is preset quantity and is 3.
After the unmanned forklift reaches a preset position, taking the current coordinate of the unmanned forklift and the picking point coordinate fed back by the position detection device as a first control point and a fifth control point of the Bezier curve;
and inserting second, third and fourth control points between the first control point and the fifth control point of the Bezier curve. The coordinates of the three control points inserted are randomly acquired, but follow the following principle: (1) in order to keep the posture of the vehicle body consistent with that of the tray when the unmanned forklift reaches the terminal, the fourth control point and the fifth control point are required to be positioned on the same straight line, the straight line passes through the parking point (namely the fifth control point), and the direction of the straight line is vertical to the front surface of the tray; (2) in order to reduce the number of iterations, the second control point is an inner point of the first and the third control points, and the three inserted control points are all positioned in a circle with the connecting line of the first and the fifth control points as the diameter.
Sequentially encoding five control points of the Bezier curve to obtain an initial individual;
suppose that the current coordinate of the unmanned forklift is S (S) x ,s y ) The coordinate of the goods-taking point fed back by the position detection device is E (E) x ,e y ) The two points are the first and the five control points, the direction of the parking point is marked as theta, and the coordinates of the three control points inserted in the middle are respectively marked as P2 (P2) x ,p2 y )、P3(p3 x ,p3 y )、P4(p4 x ,p4 y ) Taking out
Then pi x ∈[X 1 ,X 2 ]，pi y ∈[Y 1 ,Y 2 ]Respectively represent pi by 8-bit binary coded symbols x ，pi y Respectively obtain 2 8 =256 different encodings, and the corresponding relationship in parameter encoding is:
00000000＝X 1 ,
00000001＝X 1 +δ x ,
00000010＝X 1 +2δ x ，
……，
11111111＝X 2 。
wherein
In the same way, the method for preparing the composite material,
and (3) randomly selecting three control points which accord with the constraints of (1) and (2), and coding according to the corresponding relation to obtain a group of initial individuals.
Correspondingly, the decoding function is
Step S36: and repeating the step of obtaining the initial individuals for the preset number of times to obtain a first preset number of initial populations.
Repeating the steps for 20 times to obtain an initial population with the population number of 20.
In the embodiment, in order to enable the unmanned forklift to be suitable for a scene with real-time change of the goods position, the goods taking route is designed by adopting a genetic algorithm, and the method is simple and easy to realize.
Further, based on the above embodiments, referring to fig. 4, a third embodiment of the present application is provided, where the step S40 includes:
step S41: when the unmanned forklift reaches the goods taking point coordinates, controlling the unmanned forklift to fork goods to be taken;
step S42: and after the unmanned forklift finishes goods taking, controlling the unmanned forklift to return to the preset position according to the goods taking route.
Specifically, in this embodiment, the position detection device is a depth camera, the application scenario is to fork the goods, and the preset position is a visual detection point. Referring to fig. 4, fig. 4 is a schematic diagram of a third embodiment of the method for planning a path according to the present application. Numerals 1 to 6 respectively represent an unmanned forklift, a depth camera, a pickup route starting point, a pickup route ending point, and a cargo. The autonomous goods taking process of the unmanned forklift is as follows:
the unmanned forklift reaches the visual detection point along the route planned by the dispatching system; the visual module triggers the depth camera to take a picture, the position of the goods is obtained according to the point cloud image, and the coordinates of a goods taking parking point are calculated; the control module adopts a genetic calculation rule to draw a goods taking route line based on a Bezier curve according to the current coordinates of the vehicle and the coordinates of the goods taking points; the unmanned forklift reaches a goods taking point along an autonomously planned route to complete a goods taking task; returning the unmanned forklift to the visual detection point along the original path; and the unmanned forklift reaches the goods placing point along the route planned by the dispatching system.
In the embodiment, the coordinates of the goods taking points are determined by triggering the depth camera to reach the visual detection points, and the route of the unmanned forklift to reach the goods taking points is planned by utilizing the genetic algorithm, so that the risk that the unmanned forklift mistakenly takes the goods and collides the goods due to inaccurate goods putting positions is avoided.
In addition, a path planning device is further provided in the embodiments of the present application, and referring to fig. 5, fig. 5 is a schematic diagram of functional modules of the first embodiment of the path planning device of the present application. The path planning device comprises:
the device comprises a detection module 10, a control module and a control module, wherein the detection module is used for detecting whether deviation exists in the placing position of an object to be picked when an unmanned forklift reaches a preset position, and the preset position is a pause position when the unmanned forklift is ready to pick the object;
a determining module 20, configured to determine the coordinates of the pickup point again according to the placement position if the position is the pickup point;
the planning module 30 is used for planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by using a genetic algorithm;
and the control module 40 is used for controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish the goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
The specific embodiment executed by each module in the path planning apparatus of the present application is basically the same as each embodiment of the path planning method, and is not described herein again.
In addition, the embodiment of the application also provides a computer readable storage medium.
The present computer readable storage medium has stored thereon a path planning program which, when executed by a processor, implements the steps of the path planning method as described above.
The specific embodiment in which the path planning program stored in the computer-readable storage medium of the present application is executed by the processor is basically the same as the embodiments of the path planning method described above, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a path plan" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solution of the present application may be substantially or partially embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A path planning method, characterized in that the path planning method comprises the steps of:
when the unmanned forklift reaches a preset position, detecting whether deviation exists in the placing position of an object to be picked, wherein the preset position is a pause position when the unmanned forklift is ready to pick the object;
if yes, re-determining the coordinates of the goods taking points according to the placing positions;
planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by using a genetic algorithm;
and controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
2. The path planning method according to claim 1, wherein the step of using the genetic algorithm to plan the pickup route for the unmanned forklift from the preset position to the pickup point coordinates comprises:
acquiring an initial population based on a Bezier curve;
continuously carrying out selection iteration on the initial population until the fitness of the optimal individual reaches a given threshold value to obtain a final generation population;
and decoding the individuals of the final generation total group to obtain target control points, wherein the curve fitted by the target control points is the goods taking route.
3. The path planning method according to claim 2, wherein the step of obtaining an initial population based on bezier curves comprises:
acquiring an initial individual based on the Bezier curve;
setting the initial population number as a first preset number,
and repeating the step of obtaining the initial individuals for the times of reaching the preset number to obtain the initial population with the first preset number.
4. A path planning method according to claim 3, wherein the step of obtaining initial individuals based on the bezier curve control points comprises:
selecting the coordinates of the preset position and the goods taking point as a first control point and a last control point of the Bezier curve;
inserting a second preset number of control points between the first control point and the last control point, wherein the coordinates of the second preset number of control points are randomly acquired;
and coding all control points of the Bezier curve in sequence to obtain an initial individual.
5. The path planning method according to claim 4, wherein the step of inserting a second preset number of control points between the first control point and the last control point, and the step of randomly obtaining coordinates of the second preset number of control points further comprises:
and setting a penultimate control point, wherein the last control point is positioned on the same straight line, and the direction of the straight line is perpendicular to the front surface of the tray, so that when the unmanned forklift reaches the coordinate of the goods taking point, the posture of the body of the unmanned forklift is consistent with that of the tray of the goods to be taken.
6. The path planning method according to claim 4, wherein the step of inserting a second preset number of control points between the first control point and the last control point, and the step of randomly obtaining coordinates of the second preset number of control points further comprises:
and setting the second preset number of control points to be positioned in a circle with the connecting line of the first control point and the last control point as the diameter so as to reduce the iteration number, wherein the second control point is the inner point of the first control point and the third control point.
7. The path planning method according to claim 1, wherein the step of controlling the unmanned forklift to reach the pickup point coordinates according to the pickup route to complete pickup is followed by:
when the unmanned forklift reaches the goods taking point coordinates, controlling the unmanned forklift to fork goods to be taken;
and after the unmanned forklift finishes goods taking, controlling the unmanned forklift to return to the preset position according to the goods taking route.
8. A path planning apparatus, the apparatus comprising:
the device comprises a detection module, a control module and a control module, wherein the detection module is used for detecting whether deviation exists in the placing position of an object to be picked when an unmanned forklift reaches a preset position, and the preset position is a pause position when the unmanned forklift is ready to pick the object;
the determining module is used for re-determining the coordinates of the goods taking points according to the placing positions if the goods taking points are located at the placing positions;
the planning module is used for planning a goods taking route of the unmanned forklift from the preset position to the goods taking point coordinate by utilizing a genetic algorithm;
and the control module is used for controlling the unmanned forklift to reach the goods taking point coordinate according to the goods taking route so as to finish the goods taking, wherein when the unmanned forklift reaches the goods taking point coordinate according to the goods taking route, the body posture of the unmanned forklift is consistent with the tray of the goods to be taken.
9. A path planning apparatus, characterized in that the apparatus comprises: a memory, a processor and a path planning program stored on the memory and executable on the processor, the path planning program being configured to implement the steps of the path planning method according to any of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a path planning program which, when executed by a processor, implements the steps of the path planning method according to any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211242404.2A CN115562276A (en) | 2022-10-11 | 2022-10-11 | Path planning method, device, equipment and computer readable storage medium |
| PCT/CN2022/133336 WO2024077716A1 (en) | 2022-10-11 | 2022-11-21 | Local path planning method for amr |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211242404.2A CN115562276A (en) | 2022-10-11 | 2022-10-11 | Path planning method, device, equipment and computer readable storage medium |
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| CN115562276A true CN115562276A (en) | 2023-01-03 |
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| CN202211242404.2A Pending CN115562276A (en) | 2022-10-11 | 2022-10-11 | Path planning method, device, equipment and computer readable storage medium |
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| WO (1) | WO2024077716A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116358563A (en) * | 2023-06-01 | 2023-06-30 | 未来机器人（深圳）有限公司 | Motion planning method and device, unmanned forklift and storage medium |
| CN117576254A (en) * | 2024-01-15 | 2024-02-20 | 厦门民航凯亚有限公司 | Method for calculating movement track of luggage after arriving at extraction turntable |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6492024B2 (en) * | 2016-03-30 | 2019-03-27 | 株式会社豊田中央研究所 | Moving body |
| CN110347151B (en) * | 2019-05-31 | 2022-07-12 | 河南科技大学 | Robot path planning method fused with Bezier optimization genetic algorithm |
| JP7259662B2 (en) * | 2019-09-11 | 2023-04-18 | 株式会社豊田自動織機 | travel control device |
| CN112678724B (en) * | 2019-10-18 | 2022-06-10 | 北京极智嘉科技股份有限公司 | Intelligent forklift and control method thereof |
| CN110865642A (en) * | 2019-11-06 | 2020-03-06 | 天津大学 | Path planning method based on mobile robot |
| CN111115511B (en) * | 2019-12-31 | 2022-02-15 | 广东电网有限责任公司 | Unloading and loading method based on intelligent navigation forklift |
| CN114358145A (en) * | 2021-12-16 | 2022-04-15 | 劢微机器人科技(深圳)有限公司 | Forklift unloading method, forklift and computer-readable storage medium |
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2022
- 2022-10-11 CN CN202211242404.2A patent/CN115562276A/en active Pending
- 2022-11-21 WO PCT/CN2022/133336 patent/WO2024077716A1/en unknown
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116358563A (en) * | 2023-06-01 | 2023-06-30 | 未来机器人（深圳）有限公司 | Motion planning method and device, unmanned forklift and storage medium |
| CN117576254A (en) * | 2024-01-15 | 2024-02-20 | 厦门民航凯亚有限公司 | Method for calculating movement track of luggage after arriving at extraction turntable |
| CN117576254B (en) * | 2024-01-15 | 2024-04-30 | 厦门民航凯亚有限公司 | Method for calculating movement track of luggage after arriving at extraction turntable |
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| WO2024077716A1 (en) | 2024-04-18 |
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