CN113190006B - Robot path planning method, device and storage medium - Google Patents
Robot path planning method, device and storage medium Download PDFInfo
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- CN113190006B CN113190006B CN202110500673.3A CN202110500673A CN113190006B CN 113190006 B CN113190006 B CN 113190006B CN 202110500673 A CN202110500673 A CN 202110500673A CN 113190006 B CN113190006 B CN 113190006B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The application discloses a robot path planning method, a robot path planning device and a storage medium, which are used for improving the instantaneity and the effectiveness of path planning. The robot path planning method disclosed by the application comprises the following steps: acquiring own state information; sensing environmental information; planning a moving path according to the self state information and the environment information; moving to a target position according to the path; wherein the status information includes a start position, a target position, and a moving speed. According to the application, environmental information is fully considered, path planning parameters are adjusted, the motion trail of the robot is optimized, speed planning is efficiently performed, and real-time calculation is ensured. The application also provides a robot path planning device and a storage medium.
Description
Technical Field
The present application relates to the field of robots, and in particular, to a method, an apparatus, and a storage medium for planning a path of a robot.
Background
Mobile robots are applied in more and more industry fields, and research on path planning methods is an important problem in research on the field of mobile robots. The path planning refers to the robot planning a moving path from a starting position to a target position. The path planning algorithm in the prior art has the problems of poor real-time performance and poor optimization performance for the environment of processing multiple obstacles.
Disclosure of Invention
Aiming at the technical problems, the embodiment of the application provides a robot path planning method, a device and a storage medium, which are used for improving the instantaneity and the effectiveness of path planning.
In a first aspect, a method for planning a path of a robot provided by an embodiment of the present application includes:
acquiring own state information;
sensing environmental information;
planning a moving path according to the self state information and the environment information;
moving to a target position according to the path;
wherein the status information includes a start position, a target position, and a moving speed.
Preferably, the sensing environment information includes:
the robot perceives surrounding environment information at the current position;
generating an environment map, wherein the environment map comprises an obstacle area and a passable area;
and setting a path node position in the passable area.
Preferably, the planning a moving path according to the self state information and the environment information includes:
the robot sets a decision area range at the current position;
setting a path point as a decision path point if the straight line connecting the current position to each path node position in the current position decision area does not pass through the obstacle area;
calculating a cost evaluation value from the current position to each decision path point;
setting a decision path point with the minimum cost evaluation value as a global path point;
and judging whether the global path point is a target position, if so, determining that the path from the current position to the global path point is a planned robot moving path, and if not, continuing searching for the next global path point after the robot moves to the global path point.
Preferably, the decision region range is one of the following:
a circular region;
square areas;
diamond-shaped areas.
Preferably, the calculation formula of the cost evaluation value is:
wherein f (a) represents a cost evaluation value of the a-th decision path point;
ω 1 is a safety weight coefficient omega 2 Is a distance weight coefficient omega 3 Is a steering angle weight coefficient;
σ a representing the distance of the a-th decision path point from the surroundingsThe nearest distance of the obstacle;
s a representing the linear distance of the a decision path point from the target point;
v represents the moving speed of the robot;
θ a and the current direction of the robot and the included angle between the a decision path point and the connecting line of the current position of the robot are represented.
Preferably, the path from the current position to the global path point is a planned robot moving path, and then the method further comprises:
performing smoothing processing on the planned robot moving path;
and taking the path after the smoothing processing as a final planned robot moving path.
Preferably, the smoothing the planned movement path of the robot includes:
setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, wherein X is an abscissa set of each global path point, and X = { X } 1 ,x 2 ,...,x n Y is the ordinate set of each global path point, y= { Y 1 ,y 2 ,...,y n -the number of global path points is n;
setting an arithmetic sequence set T from 0 to 1, wherein the number of elements in the set T is preset;
setting the coordinates of the smooth path points as (xx, yy), initializing xx to 0, and initializing yy to 0;
and carrying out smoothing processing according to the arithmetic sequence set T for each point in the global path points { X, Y }.
Preferably, said smoothing according to said set of arithmetic sequences T for each of said respective global path points { X, Y } comprises:
b4: setting the maximum circulation times as n-1 and setting the current times as k as 0;
b5: judging whether the current frequency exceeds the maximum cycle frequency, outputting (xx, yy) if the current frequency exceeds the maximum cycle frequency, and executing the step B6 if the current frequency does not exceed the maximum cycle frequency:
b6: the first correction value and the second correction value are calculated using:
wherein ρ is in 1 For a first correction value ρ 2 For the second correction value, n is the number of global path points, T is an arithmetic sequence set from 0 to 1, and k is the current times;
b7: updating the smoothed path point coordinates (xx, yy) using:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and x k+1 Abscissa, y representing the (k+1) th global path point k+1 An ordinate representing the (k+1) th global path point;
b8: updating the coordinates of the smooth path points, setting the current times k as k+1, and returning to the step B5.
By using the robot path planning method provided by the application, surrounding environment information is perceived in real time, global path points are searched according to the cost evaluation function, and the planned path is smoothed, so that the instantaneity and the effectiveness of path planning are improved.
In a second aspect, an embodiment of the present application further provides a robot path planning apparatus, including:
the state acquisition module is configured to acquire own state information;
an environment sensing module configured to sense environment information;
the path planning module is configured to plan a moving path according to the state information of the path planning module and the environment information;
wherein the status information includes a start position, a target position, and a moving speed.
In a third aspect, an embodiment of the present application further provides a robot path planning apparatus, including: a memory, a processor, and a user interface;
the memory is used for storing a computer program;
the user interface is used for realizing interaction with a user;
the processor is used for reading the computer program in the memory, and when the processor executes the computer program, the robot path planning method provided by the application is realized.
In a fourth aspect, an embodiment of the present application further provides a processor readable storage medium, where a computer program is stored in the processor readable storage medium, and when the processor executes the computer program, the robot path planning method provided by the present application is implemented.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a robot acquiring a starting position and a target position according to an embodiment of the present application;
fig. 2 is a schematic diagram of a robot path planning method according to an embodiment of the present application;
fig. 3 is a schematic diagram of a robot searching paths in decision path points according to an embodiment of the present application;
fig. 4 is a schematic diagram of a planned path of a robot according to an embodiment of the present application;
fig. 5 is a schematic diagram of a robot smoothing a planned path according to an embodiment of the present application;
fig. 6 is a schematic diagram of a robot path planning apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of another robot path planning apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Some words appearing hereinafter are explained:
1. in the embodiment of the application, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
2. The term "plurality" in embodiments of the present application means two or more, and other adjectives are similar.
3. In the embodiment of the application, the "robot" and the "mobile robot" are the same meaning, and refer to the robot performing path planning.
As shown in fig. 1, the mobile robot acquires a schematic diagram of a start position and a target position in the environment, and a black area is an obstacle area. The mobile robot needs to move from the initial position of the lower left corner to the target position of the upper right corner five-pointed star, and an optimal path needs to be planned according to a path planning method.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the display sequence of the embodiments of the present application only represents the sequence of the embodiments, and does not represent the advantages or disadvantages of the technical solutions provided by the embodiments.
Example 1
Referring to fig. 1, a schematic diagram of a robot path planning method according to an embodiment of the present application is shown in fig. 1, and the method includes steps S201 to S204:
s201, acquiring self state information;
s202, sensing environment information;
s203, planning a moving path according to the self state information and the environment information;
s204, moving to a target position according to the path;
wherein the status information includes a start position, a target position, and a moving speed.
As a preferred example, in the embodiment of the present application, step S201 obtains its own status information, which may include information such as the current direction of the robot, the current electric quantity of the robot, and the like, in addition to the start position, the target position, and the moving speed.
As a preferred example, the start position and the target position may be acquired first, and a coordinate system in which coordinate values of the start position and the target position are acquired may be set on the environment map. The coordinate values include an abscissa and an ordinate.
As a preferred example, in step S202 of the present embodiment, the sensing environment information may include:
the robot perceives surrounding environment information at the current position;
generating an environment map, wherein the environment map comprises an obstacle area and a passable area;
and setting a path node position in the passable area.
That is, the robot senses surrounding environment information in real time at the current position and generates an environment map according to the environment information. The area in the environment map is divided into an obstacle area and a passable area, wherein the obstacle area comprises static obstacles such as walls and the like and also comprises an area occupied by dynamic obstacles around the robot at the current time.
As shown in fig. 1, a robot senses surrounding environment information at a start position, generates an environment map, and divides the environment map into a black obstacle area and a white passable area.
Further, path node positions are set in the passable area. The arrangement mode comprises that a group of nodes are uniformly arranged in the passable area, and the positions of the path nodes arranged in the passable area which is farther from the obstacle can be denser.
It should be noted that, in the embodiment of the present application, the path node refers to a safe location in the passable area.
As a preferred example, in step S203 of the embodiment of the present application, planning a moving path according to the own state information and the environment information may include:
the robot sets a decision area range at the current position;
setting a path point as a decision path point if the straight line connecting the current position to each path node position in the current position decision area does not pass through the obstacle area;
calculating a cost evaluation value from the current position to each decision path point;
setting a decision path point with the minimum cost evaluation value as a global path point;
and judging whether the global path point is a target position, if so, determining that the path from the current position to the global path point is a planned robot moving path, and if not, searching the global path point again after the robot moves to the global path point.
In the embodiment of the present application, the global path point refers to a position where the robot needs to sequentially pass from the initial current position to the target position;
specifically, according to the own state information and the environment information, planning a movement path includes the following steps A1 to A4:
step A1: and setting a decision area range at the current position of the robot. Preferably, the decision area range can be set as different ranges such as a circular area, a square area, a diamond area and the like, and can be specifically preset according to the needs, and the application is not limited.
And in the decision area range, connecting the current position of the robot to the straight line of each path node in the current position decision area range, and setting the path point as a decision path point if the connecting straight line of the current position of the robot and the path node does not pass through the obstacle area. As shown in fig. 3, the robot is provided with a plurality of decision path points on which paths are searched for in the following.
Step A2: and calculating the cost evaluation value from the current position of the robot to each decision path point. In the embodiment of the application, the cost evaluation value is used for evaluating the moving cost and the safety from the current position of the robot to each decision path point. As a preferable example, the cost evaluation value is determined according to the safety, the distance, and the steering angle. Preferably, the calculation formula of the cost evaluation value is:
wherein f (a) represents a cost evaluation value of the a-th decision path point;
ω 1 is a safety weight coefficient omega 2 Is a distance weight coefficient omega 3 Is a steering angle weight coefficient;
σ a representing the nearest distance of the a-th decision path point from surrounding obstacles;
s a representing the linear distance of the a decision path point from the target point;
v represents the moving speed of the robot;
θ a and the current direction of the robot and the included angle between the a decision path point and the connecting line of the current position of the robot are represented.
Note that, the safety weight coefficient ω 1 Distance weight coefficient omega 2 Steering angle weight coefficient omega 3 Is budget set.
A3: determining a decision path point with the minimum cost evaluation value, and setting the decision path point as a global path point;
a4: judging whether the global path point is a target point, if so, sequentially passing through paths of the global path points from the initial position to be a planned mobile robot path, and if not, moving the mobile robot to the global path point and then searching the global path point again, namely executing step S202 to continuously search the next global path point.
The global waypoint search process of the above steps A1 to A4 is, for example:
the current position is a path point 1, three {2,3,4} path points are arranged in a decision area around the path point 1, and the path point 3 with the minimum cost evaluation value is calculated according to the environmental information sent by devices in the decision area around the path point 1, so that the path point 3 is a global path point;
judging whether the path point 3 is a target position, if so, the path is from the path point 1 to the path point 3, if not, the target position is assumed to be 8, then the path point 3 is taken as the current position, the path points in the decision area around the path point 3 are searched, the path point 5 with the minimum cost evaluation value is calculated according to the environment information sent by devices in the decision area around the path point 3, and the path point 5 is the next global path point;
and judging whether the path 5 is a target position or not, if so, taking the path point 5 as the current position, searching for path points in a decision area around the path 5, and if the path point 8 is searched, finally, taking the global path points from the path points 1 to 8 as 1,3,5 and 8, wherein the path is from 1 to 3 to 5 to 8.
Through the steps A1 to A4, the current position is updated in each iteration, the decision area is divided by the current path point, the path point of the decision area is searched, the environment information in the decision area is obtained, the environment information transmitted by the utilization device in each area is dynamically obtained, the real-time and high-efficiency performance can be achieved, and the effectiveness of path planning is improved. Fig. 4 is a schematic diagram of the path of the mobile robot after the search is completed.
As a preferred example, in the embodiment of the present application, in the step A4, after the path sequentially passing through the global path points from the start position is the planned mobile robot path, the method further includes:
performing smoothing processing on the planned robot moving path;
and taking the path after the smoothing processing as a final planned robot moving path.
Preferably, the smoothing process includes:
setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, wherein X is an abscissa set of each global path point, and X = { X } 1 ,x 2 ,...,x n Y is the ordinate set of each global path point, y= { Y 1 ,y 2 ,...,y n -the number of global path points is n;
setting an arithmetic sequence set T from 0 to 1, wherein the number of elements in the set T is preset;
setting the coordinates of the smooth path points as (xx, yy), initializing xx to 0, and initializing yy to 0;
for each point in the global path points { X, Y }, smoothing is performed according to the set of arithmetic sequences T.
A specific example of the above smoothing processing procedure is given below:
b1, setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, wherein X is the abscissa set of each global path point, and X= { X } 1 ,x 2 ,...,x n Y is the ordinate set of each global path point, y= { Y 1 ,y 2 ,...,y n -the number of global path points is n;
b2, setting an arithmetic sequence set T from 0 to 1, wherein the number of elements in the set T is preset. As a preferred example, for example, set the number of elements in the set T to be 100, then T is t= {0,0.01,0.02,0.03,.. 0.99,1}. The smoother the number of elements in the set T, the smoother the movement of the robot.
B3: the smooth path point coordinates are set to (xx, yy), xx is initialized to 0, yy is initialized to 0.
B4: the maximum number of cycles is set to n-1 and the current number k is set to 0.
B5: and (3) judging whether the current frequency exceeds the maximum cycle frequency n-1, outputting (xx, yy) if the current frequency exceeds the maximum cycle frequency n-1, and executing the step B6 if the current frequency does not exceed the maximum cycle frequency.
B6: the first correction value and the second correction value are calculated using:
rho in 1 For a first correction value ρ 2 For the second correction value, n is the number of global path points, T is the set of arithmetic sequences from 0 to 1, and k is the current number of times.
B7: updating the smoothed path point coordinates (xx, yy) using:
where xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and x k+1 Abscissa, y representing the (k+1) th global path point k+1 The ordinate representing the k+1th global path point.
B8: updating the coordinates of the smooth path points, setting the current times k as k+1, and returning to the step B5.
The smoothing process of steps B1 to B8 described above is, for example:
the coordinates of each global path point are { x= [1,2,3, 4], y= [1,2, 1] }; setting the number of elements in the set T to be 10;
then xx= [1,1.44,1.89,2.32,2.74,3.13,3.47,3.75,3.93,4] after the smoothing process
yy＝[1,1.38,1.63,1.79,1.87,1.87,1.79,1.63,1.38,1]。
And after the target position point is found, carrying out smoothing on the initial current position, the global path points and the target position point which are sequentially found, and expanding the global path points, and circularly utilizing the coordinates of each global path point each time, thereby improving the rationality and the instantaneity of the path. As shown in fig. 5, after the smoothing process, the movement path of the robot is obtained, and after the smoothing process, the movement path is smoother at the corner of the path, so that the movement of the robot at the turning position is facilitated.
Example two
Based on the same inventive concept, the embodiment of the application also provides a robot path planning device, as shown in fig. 6, which comprises:
a state acquisition module 601 configured to acquire state information of itself;
an environment awareness module 602 configured to perceive environmental information;
a path planning module 603 configured to plan a moving path according to the own state information and the environment information;
wherein the status information includes a start position, a target position, and a moving speed.
As a preferred example, the context awareness module 602 is further configured to:
sensing surrounding environmental information at the current location;
generating an environment map, wherein the environment map comprises an obstacle area and a passable area;
and setting a path node position in the passable area.
As a preferred example, the path planning module 603 is also configured to:
setting a decision area range at the current position;
setting a path point as a decision path point if the straight line connecting the current position to each path node position in the current position decision area does not pass through the obstacle area;
calculating a cost evaluation value from the current position to each decision path point;
setting a decision path point with the minimum cost evaluation value as a global path point;
and judging whether the global path point is a target position, if so, determining that the path from the current position to the global path point is a planned robot moving path, and if not, continuing searching for the next global path point after the robot moves to the global path point.
As a preferred example, the decision region range is one of:
a circular region;
square areas;
diamond-shaped areas.
As a preferable example, the calculation formula of the cost evaluation value is:
wherein f (a) represents a cost evaluation value of the a-th decision path point;
ω 1 is a safety weight coefficient omega 2 Is a distance weight coefficient omega 3 Is a steering angle weight coefficient;
σ a representing the nearest distance of the a-th decision path point from surrounding obstacles;
s a representing the linear distance of the a decision path point from the target point;
v represents the moving speed of the robot;
θ a and the current direction of the robot and the included angle between the a decision path point and the connecting line of the current position of the robot are represented.
As a preferred example, the path planning module 603 is further configured to, after the path from the current position to the global path point is the planned robot movement path:
performing smoothing processing on the planned robot moving path;
and taking the path after the smoothing processing as a final planned robot moving path.
The path planning module 603 is further configured to perform smoothing according to the following steps:
setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, wherein X is an abscissa set of each global path point, and X = { X } 1 ,x 2 ,...,x n Y is the ordinate set of each global path point, y= { Y 1 ,y 2 ,...,y n -the number of global path points is n;
setting an arithmetic sequence set T from 0 to 1, wherein the number of elements in the set T is preset;
setting the coordinates of the smooth path points as (xx, yy), initializing xx to 0, and initializing yy to 0;
for each point in the global path points { X, Y }, smoothing is performed according to the set of arithmetic sequences T.
Said smoothing according to said set of arithmetic sequences T for each of the respective global path points { X, Y }, comprises:
b4: setting the maximum circulation times as n-1 and setting the current times as k as 0;
b5: judging whether the current frequency exceeds the maximum cycle frequency, outputting (xx, yy) if the current frequency exceeds the maximum cycle frequency, and executing the step B6 if the current frequency does not exceed the maximum cycle frequency:
b6: the first correction value and the second correction value are calculated using:
wherein ρ is in 1 For a first correction value ρ 2 For the second correction value, n is the number of global path points, T is an arithmetic sequence set from 0 to 1, and k is the current times;
b7: updating the smoothed path point coordinates (xx, yy) using:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and x k+1 Abscissa, y representing the (k+1) th global path point k+1 An ordinate representing the (k+1) th global path point;
b8: updating the coordinates of the smooth path points, setting the current times k as k+1, and returning to the step B5.
As a preferred example, the mobile robot path planning apparatus provided in this embodiment further includes:
a movement control module 604 for moving to a target location according to the path.
It should be noted that, the state obtaining module 601 provided in the present embodiment can implement all the functions included in step S201 in the first embodiment, solve the same technical problem, achieve the same technical effect, and are not described herein again;
it should be noted that, the environmental awareness module 602 provided in the present embodiment can implement all the functions included in the step S202 in the first embodiment, solve the same technical problem, achieve the same technical effect, and are not described herein again;
it should be noted that, the path planning module 603 provided in the present embodiment can implement all the functions included in step S203 in the first embodiment, solve the same technical problem, achieve the same technical effect, and are not described herein again;
it should be noted that, the device provided in the second embodiment and the method provided in the first embodiment belong to the same inventive concept, solve the same technical problem, achieve the same technical effect, and the device provided in the second embodiment can implement all the methods in the first embodiment, and the same points are not repeated.
Example III
Based on the same inventive concept, the embodiment of the application also provides a robot path planning device, as shown in fig. 7, which comprises:
including a memory 702, a processor 701, and a user interface 703;
the memory 702 is used for storing a computer program;
the user interface 703 is configured to interact with a user;
the processor 701 is configured to read a computer program in the memory 702, where the processor 701 implements:
acquiring own state information;
sensing environmental information;
planning a moving path according to the self state information and the environment information;
moving to a target position according to the path;
wherein the status information includes a start position, a target position, and a moving speed.
Where in FIG. 7, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by the processor 701 and various circuits of the memory represented by the memory 702, are linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The processor 701 is responsible for managing the bus architecture and general processing, and the memory 702 may store data used by the processor 701 in performing operations.
The processor 701 may be CPU, ASIC, FPGA or a CPLD, and the processor 701 may also employ a multi-core architecture.
When the processor 701 executes the computer program stored in the memory 702, any one of the robot path planning methods in the first embodiment is implemented.
It should be noted that, the device provided in the third embodiment and the method provided in the first embodiment belong to the same inventive concept, solve the same technical problem, achieve the same technical effect, and the device provided in the third embodiment can implement all the methods in the first embodiment, and the same points are not repeated.
The application also proposes a processor readable storage medium. The processor-readable storage medium stores a computer program, and when the processor executes the computer program, the processor implements any one of the robot path planning methods in the first embodiment.
It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A robot path planning method applied to a mobile robot, comprising:
acquiring own state information;
sensing environmental information;
planning a moving path according to the self state information and the environment information;
moving to a target position according to the path;
wherein the state information includes a start position, a target position, and a moving speed;
the perceived environmental information includes:
the robot perceives surrounding environment information at the current position;
generating an environment map, wherein the environment map comprises an obstacle area and a passable area;
setting a path node position in the passable area;
wherein, the planning a moving path according to the self state information and the environment information includes:
the robot sets a decision area range at the current position;
a straight line connecting the current position to each path node position in the current position decision area range is set as a decision path point if the straight line between the current position and the path node does not pass through the obstacle area;
calculating a cost evaluation value from the current position to each decision path point; the calculation formula of the cost evaluation value is as follows:
wherein f (a) represents a cost evaluation value of the a-th decision path point;
ω 1 is a safety weight coefficient omega 2 Is a distance weight coefficient omega 3 Is a steering angle weight coefficient;
σ a representing the nearest distance of the a-th decision path point from surrounding obstacles;
s a representing the linear distance of the a decision path point from the target point;
v represents the moving speed of the robot;
θ a and the current direction of the robot and the included angle between the a decision path point and the connecting line of the current position of the robot are represented.
2. The method of claim 1, wherein planning a movement path based on the own state information and the environment information, further comprises:
setting a decision path point with the minimum cost evaluation value as a global path point;
and judging whether the global path point is a target position, if so, determining that the path from the current position to the global path point is a planned robot moving path, and if not, continuing searching for the next global path point after the robot moves to the global path point.
3. The method of claim 2, wherein the decision region range is one of:
a circular region;
square areas;
diamond-shaped areas.
4. The method of claim 2, wherein the path from the current location to the global waypoint is a planned robot travel path further comprising:
performing smoothing processing on the planned robot moving path;
and taking the path after the smoothing processing as a final planned robot moving path.
5. The method of claim 4, wherein smoothing the planned robot travel path comprises:
setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, wherein X is an abscissa set of each global path point, and X = { X } 1 ,x 2 ,...,x n Y is the ordinate set of each global path point, y= { Y 1 ,y 2 ,...,y n -the number of global path points is n;
setting an arithmetic sequence set T from 0 to 1, wherein the number of elements in the set T is preset;
setting the coordinates of the smooth path points as (xx, yy), initializing xx to 0, and initializing yy to 0;
and carrying out smoothing processing according to the arithmetic sequence set T for each point in the global path points { X, Y }.
6. The method of claim 5, wherein said smoothing from said set of arithmetic sequences T for each of said respective global path points { X, Y }, comprises:
b4: setting the maximum circulation times as n-1 and setting the current times as k as 0;
b5: judging whether the current frequency exceeds the maximum cycle frequency, outputting (xx, yy) if the current frequency exceeds the maximum cycle frequency, and executing the step B6 if the current frequency does not exceed the maximum cycle frequency:
b6: the first correction value and the second correction value are calculated using:
wherein ρ is in 1 For a first correction value ρ 2 For the second correction value, n is the number of global path points, T is an arithmetic sequence set from 0 to 1, and k is the current times;
b7: updating the smoothed path point coordinates (xx, yy) using:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and x k+1 Abscissa, y representing the (k+1) th global path point k+1 An ordinate representing the (k+1) th global path point;
b8: updating the coordinates of the smooth path points, setting the current times k as k+1, and returning to the step B5.
7. A robot path planning apparatus, comprising:
the state acquisition module is configured to acquire own state information;
an environment sensing module configured to sense environment information;
the path planning module is configured to plan a moving path according to the state information of the path planning module and the environment information;
wherein the state information includes a start position, a target position, and a moving speed;
the perceived environmental information includes:
the robot perceives surrounding environment information at the current position;
generating an environment map, wherein the environment map comprises an obstacle area and a passable area;
and setting a path node position in the passable area.
8. A robot path planning device, comprising a memory, a processor and a user interface;
the memory is used for storing a computer program;
the user interface is used for realizing interaction with a user;
the processor is configured to read a computer program in the memory, and when the processor executes the computer program, implement the robot path planning method according to one of claims 1 to 6.
9. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program, which when executed by the processor implements the robot path planning method according to one of claims 1 to 6.
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| CN114384907A (en) * | 2021-12-15 | 2022-04-22 | 佛山市顺德区美的电子科技有限公司 | Path tracking method and device, electronic equipment, mobile air conditioner and storage medium |
| CN116188480B (en) * | 2023-04-23 | 2023-07-18 | 安徽同湃特机器人科技有限公司 | Calculation method of AGV traveling path point during ceiling operation of spraying robot |
| CN117553804B (en) * | 2024-01-11 | 2024-04-09 | 深圳市普渡科技有限公司 | Path planning method, path planning device, computer equipment and storage medium |
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