CN113190006A - Robot path planning method and device and storage medium - Google Patents
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Abstract
The application discloses a robot path planning method, a device and a storage medium, which are used for improving the instantaneity and effectiveness of path planning. The robot path planning method disclosed by the application comprises the following steps: acquiring self 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 invention fully considers the environmental information, adjusts the path planning parameters, optimizes the motion track of the robot, efficiently plans the speed and ensures the real-time calculation. The application also provides a robot path planning device and a storage medium.
Description
Technical Field
The present disclosure relates to the field of robots, and in particular, to a method, an apparatus, and a storage medium for planning a robot path.
Background
The mobile robot is applied in more and more industry fields, and the research of the path planning method is a key problem of the research in the field of the mobile robot. Path planning refers to planning a moving path from a starting position to a target position by a robot. The path planning algorithm in the prior art has the problems of poor real-time performance and poor optimization performance in the process of processing the environment with multiple obstacles.
Disclosure of Invention
In view of the above technical problems, embodiments of the present application provide a method, an apparatus, and a storage medium for robot path planning, so as to improve the real-time performance and effectiveness of path planning.
In a first aspect, a robot path planning method provided in an embodiment of the present application includes:
acquiring self 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.
Preferably, the perceptual environment information includes:
the robot senses surrounding environment information at the current position;
generating an environment map, the environment map including an obstacle area and a passable area;
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;
connecting the current position to a straight line of each path node position in the current position decision area range, and if the straight line of the current position and the path node cannot penetrate through the barrier area, setting the path point as a decision path point;
calculating the cost evaluation value from the current position to each decision path point;
setting the 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, moving the robot to the global path point and then continuously searching the next global path point.
Preferably, the decision area range is one of the following:
a circular region;
a square region;
a diamond shaped area.
Preferably, the calculation formula of the cost evaluation value is:
wherein, f (a) represents the cost evaluation value of the a-th decision path point;
ω1is the safety weight coefficient, ω2Is a distance weight coefficient, ω3Is the steering angle weight coefficient;
σarepresenting the closest distance of the a-th decision path point from the surrounding obstacles;
sarepresenting a linear distance of the a-th decision path point from the target point;
v represents the moving speed of the robot;
θaand the included angle between the current orientation of the robot and the connecting line between the a-th decision path point and the current position of the robot is represented.
Preferably, the following is further included after the path from the current position to the global path point is a planned robot movement path:
smoothing the planned robot moving path;
and taking the smoothed path as a finally planned robot moving path.
Preferably, the smoothing the planned robot movement path includes:
setting a coordinate system on the environment map, setting the coordinate of each global path point as { X, Y }, wherein X is the abscissa set of each global path point, and X is { X ═1,x2,...,xnY is the set of ordinates of the respective global path point, Y ═ Y1,y2,...,ynThe number of the 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 performing smoothing processing on each point in the global path points { X, Y } according to the equal difference sequence set T.
Preferably, the smoothing, for each of the global path points { X, Y }, according to the set of arithmetic sequence T includes:
b4: setting the maximum cycle number to be n-1 and setting the current number k to be 0;
b5: judging whether the current times exceed the maximum circulation times, if the current times exceed the maximum circulation times, outputting (xx, yy), and if the current times do not exceed the maximum circulation times, executing the step B6:
b6: calculating a first corrective value and a second corrective value using:
wherein, in the formula, rho1Is the first corrective value, p2The correction value is a second correction value, n is the number of the global path points, T is an arithmetic sequence set from 0 to 1, and k is the current time;
b7: the smoothed path point coordinates (xx, yy) are updated using the following equation:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and xk+1Abscissa, y, representing the k +1 global path pointk+1Represents the ordinate of the (k + 1) th global path point;
b8: and D, updating the coordinates of the smooth path points, setting the current time k as k +1, and returning to the step B5.
By using the robot path planning method provided by the invention, the surrounding environment information is sensed in real time, the global path point is searched according to the cost evaluation function, and the planned path is smoothed, so that the real-time performance 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 state information of the state acquisition module;
a context awareness module configured to perceive context information;
the path planning module is configured to plan a moving path according to the self state information and the environment information;
wherein the state 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 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 invention is realized.
In a fourth aspect, an embodiment of the present invention further provides a processor-readable storage medium, where a computer program is stored, and when the processor executes the computer program, the robot path planning method provided in the present invention is implemented.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a robot acquiring an initial position and a target position according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a robot path planning method provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a robot searching path in each decision path point according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a path planned by a robot according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a robot smoothing a planned path according to an embodiment of the present disclosure;
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 invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Some of the words that appear in the text are explained below:
1. the term "and/or" in the embodiments of the present invention describes an association relationship of associated objects, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
2. In the embodiments of the present application, the term "plurality" means two or more, and other terms are similar thereto.
3. In the embodiment of the present invention, "robot" and "mobile robot" are the same meaning, and both refer to robots that perform path planning.
As shown in fig. 1, the mobile robot obtains a schematic diagram of a start position and a target position in an environment, and a black area is an obstacle area. The mobile robot needs to move from the starting position of the lower left corner to the target position of the five-pointed star on the upper right corner, and an optimal path needs to be planned according to a path planning method.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the display sequence of the embodiment of the present application only represents the sequence of the embodiment, and does not represent the merits of the technical solutions provided by the embodiments.
Example one
Referring to fig. 1, a schematic diagram of a robot path planning method provided in an embodiment of the present application is shown in fig. 1, where the method includes steps S201 to S204:
s201, acquiring self state information;
s202, sensing environmental 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 state information includes a start position, a target position, and a moving speed.
As a preferable example, in the embodiment of the present invention, the step S201 acquires the state information of the robot, which may include information such as a current orientation of the robot, a current power 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 obtained by first obtaining an environment map, setting a coordinate system on the environment map, and obtaining coordinate values of the start position and the target position in the coordinate system. The coordinate values include an abscissa and an ordinate.
As a preferable example, in step S202 of this embodiment, the sensing environment information may include:
the robot senses surrounding environment information at the current position;
generating an environment map, the environment map including an obstacle area and a passable area;
setting a path node position in the passable area.
That is, the robot senses the surrounding environment information in real time at the current position, and generates an environment map based on 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, the robot senses surrounding environment information at a home 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 is uniformly arranged in the passable area, and the positions of the path nodes are more densely arranged according to the passable area which is farther away from the barrier.
It should be noted that, in the embodiment of the present invention, the path node refers to a safe location in the passable area.
As a preferable example, in step S203 according to the embodiment of the present invention, planning the moving path according to the self state information and the environment information may include:
the robot sets a decision area range at the current position;
connecting the current position to a straight line of each path node position in the current position decision area range, and if the straight line of the current position and the path node cannot penetrate through the barrier area, setting the path point as a decision path point;
calculating the cost evaluation value from the current position to each decision path point;
setting the 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.
It should be noted that, in the embodiment of the present invention, the global path point refers to a position that the robot needs to sequentially pass from the initial current position to the target position;
specifically, according to the self state information and the environment information, planning a moving path includes the following steps a1 to a 4:
step A1: and setting a decision area range at the current position of the robot. Preferably, the decision area range may be set to different ranges such as a circular area, a square area, a diamond area, and the like, and may be preset specifically according to the need, which is not limited in the present invention.
And in the decision area range, connecting the current position of the robot to the straight line of each path node in the decision area range of the current position, and if the connecting straight line of the current position of the robot and the path node cannot pass through the barrier area, setting the path point as a decision path point. As shown in fig. 3, the robot sets a plurality of decision path points, and the path searched at each decision path point will be described below.
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 invention, 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 in accordance with safety, distance, and steering angle. Preferably, the cost evaluation value is calculated by the following formula:
wherein, f (a) represents the cost evaluation value of the a-th decision path point;
ω1is the safety weight coefficient, ω2Is a distance weight coefficient, ω3Is the steering angle weight coefficient;
σarepresenting the closest distance of the a-th decision path point from the surrounding obstacles;
sarepresenting a linear distance of the a-th decision path point from the target point;
v represents the moving speed of the robot;
θaand the included angle between the current orientation of the robot and the connecting line between the a-th decision path point and the current position of the robot is represented.
Note that, the safety weight coefficient ω1Distance weight coefficient ω2Steering angle weight coefficient ω3Is 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: and judging whether the global path point is a target point, if so, taking the path passing through all the global path points from the initial position as 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 the step S202 and continuing to search the next global path point.
The global path point search process of the above steps a1 to a4 is, for example:
the current position is a path point 1, three path points {2, 3 and 4} are arranged in a decision area around the path point 1, and according to environment information sent by devices in the decision area around the path point 1, the path point 3 with the minimum calculation cost evaluation value is a global path point 3;
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 taking the path point 3 as the current position, searching path points in a decision area around the path point 3, and according to the environment information sent by devices in the decision area around the path point 3, calculating the path point 5 with the minimum cost evaluation value, and then 5 is the next global path point;
and judging whether the path 5 is the target position, if so, the path is from the path point 1 to the path point 3 to the path point 5, if not, the path point 5 is taken as the current position, searching path points in a decision area around the path point 5, if the path point 8 is searched, finally, the global path points from the path points 1 to 8 are 1, 3, 5 and 8, and the path is from 1 to 3 to 5 to 8.
Through the steps from 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, and therefore the path planning effectiveness can be improved in a real-time and high-efficiency mode. Fig. 4 is a schematic diagram of the path of the mobile robot obtained after the search is finished.
As a preferable example, in the embodiment of the present invention, in step a4, after the step of sequentially passing the path of each global path point from the start position through the global path points is the planned path of the mobile robot, the method further includes:
smoothing the planned robot moving path;
and taking the smoothed path as a finally planned robot moving path.
Preferably, the smoothing process includes:
setting a coordinate system on the environment map, setting the coordinate of each global path point as { X, Y }, wherein X is the abscissa set of each global path point, and X is { X ═1,x2,...,xnY is the set of ordinates of the respective global path point, Y ═ Y1,y2,...,ynThe number of the 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 performing smoothing processing on each point in each global path point { X, Y } according to the equal difference sequence set T.
A specific example is given below for the smoothing process described above:
b1, setting a coordinate system on the environment map, setting the coordinates of each global path point as { X, Y }, setting X as the abscissa set of each global path point, and setting X as { X ═ X { (X })1,x2,...,xnY is the set of ordinates of the respective global path point, Y ═ Y1,y2,...,ynThe number of the global path points is n;
b2, setting an equal difference sequence set T from 0 to 1, wherein the number of elements in the set T is preset. As a preferred example, for example, if the number of elements in the set T is set to 100, then T is {0,0.01,0.02, 0.03.., 0.99,1 }. It should be noted that the more the number of elements in the set T, the smoother the path, and the more favorable the movement of the robot.
B3: the coordinates of the smooth path point are set to (xx, yy), xx is initialized to 0, and yy is initialized to 0.
B4: the maximum cycle number is set to n-1, and the current number k is set to 0.
B5: and judging whether the current time exceeds the maximum circulation time n-1, outputting (xx, yy) if the current time exceeds the maximum circulation time n-1, and executing the step B6 if the current time does not exceed the maximum circulation time.
B6: calculating a first corrective value and a second corrective value using:
where rho1Is the first corrective value, p2And n is the number of the global path points, T is an arithmetic sequence set from 0 to 1, and k is the current time.
B7: the smoothed path point coordinates (xx, yy) are updated using the following equation:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and xk+1Abscissa, y, representing the k +1 global path pointk+1Represents the ordinate of the (k + 1) th global path point.
B8: and D, updating the coordinates of the smooth path points, setting the current time k as k +1, and returning to the step B5.
The smoothing processing procedures of the above steps B1 to B8 are as follows:
the coordinates of each global path point are { X ═ 1,2,3,4,4], Y ═ 1,2,2,2,1] }; setting the number of elements in the set T to be 10;
then xx after smoothing is ═ 1,1.44,1.89,2.32,2.74,3.13,3.47,3.75,3.93,4]
yy＝[1,1.38,1.63,1.79,1.87,1.87,1.79,1.63,1.38,1]。
After the target position point is found, the initial current position, the sequentially found global path points and the target position point are smoothed, the global path points are expanded, and the coordinates of each global path point are cyclically utilized each time, so that the reasonability and the real-time performance of the path are improved. As shown in fig. 5, after the smoothing process, the movement path of the robot finally obtained is smoother at the corners of the path, so that the path is more favorable for the movement of the robot at the turning position.
Example two
Based on the same inventive concept, an embodiment of the present invention further provides a robot path planning apparatus, as shown in fig. 6, the apparatus includes:
a state obtaining module 601 configured to obtain state information of itself;
a context awareness module 602 configured to perceive context information;
a path planning module 603 configured to plan a moving path according to the state information of the mobile terminal and the environment information;
wherein the state 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 a current location;
generating an environment map, the environment map including an obstacle area and a passable area;
setting a path node position in the passable area.
As a preferred example, the path planning module 603 is further configured to:
setting a decision area range at the current position;
connecting the current position to a straight line of each path node position in the current position decision area range, and if the straight line of the current position and the path node cannot penetrate through the barrier area, setting the path point as a decision path point;
calculating the cost evaluation value from the current position to each decision path point;
setting the 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, moving the robot to the global path point and then continuously searching the next global path point.
As a preferred example, the decision region range is one of:
a circular region;
a square region;
a diamond shaped area.
As a preferable example, the calculation formula of the cost evaluation value is:
wherein, f (a) represents the cost evaluation value of the a-th decision path point;
ω1is the safety weight coefficient, ω2Is a distance weight coefficient, ω3Is the steering angle weight coefficient;
σarepresenting the closest distance of the a-th decision path point from the surrounding obstacles;
sarepresenting a linear distance of the a-th decision path point from the target point;
v represents the moving speed of the robot;
θaand the included angle between the current orientation of the robot and the connecting line between the a-th decision path point and the current position of the robot is 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 a planned robot movement path:
smoothing the planned robot moving path;
and taking the smoothed path as a finally planned robot moving path.
The path planning module 603 is further configured to perform a smoothing process according to the following steps:
setting a coordinate system on the environment map, setting the coordinate of each global path point as { X, Y }, wherein X is the abscissa set of each global path point, and X is { X ═1,x2,...,xnY is the set of ordinates of the respective global path point, Y ═ Y1,y2,...,ynThe number of the 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 performing smoothing processing on each point in each global path point { X, Y } according to the equal difference sequence set T.
The smoothing processing according to the arithmetic difference sequence set T for each point in the global path points { X, Y } includes:
b4: setting the maximum cycle number to be n-1 and setting the current number k to be 0;
b5: judging whether the current times exceed the maximum circulation times, if the current times exceed the maximum circulation times, outputting (xx, yy), and if the current times do not exceed the maximum circulation times, executing the step B6:
b6: calculating a first corrective value and a second corrective value using:
wherein, in the formula, rho1Is the first corrective value, p2The correction value is a second correction value, n is the number of the global path points, T is an arithmetic sequence set from 0 to 1, and k is the current time;
b7: the smoothed path point coordinates (xx, yy) are updated using the following equation:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and xk+1Abscissa, y, representing the k +1 global path pointk+1Represents the ordinate of the (k + 1) th global path point;
b8: and D, updating the coordinates of the smooth path points, setting the current time k as k +1, and returning to the step B5.
As a preferable example, the mobile robot path planning apparatus provided in this embodiment further includes:
and a movement control module 604, configured to move to the target position according to the path.
It should be noted that, the state obtaining module 601 provided in this embodiment can implement all the functions included in step S201 in the first embodiment, solve the same technical problem, and achieve the same technical effect, which is not described herein again;
it should be noted that, the environmental awareness module 602 provided in this embodiment can implement all the functions included in step S202 in the first embodiment, solve the same technical problem, and achieve the same technical effect, which is not described herein again;
it should be noted that the path planning module 603 provided in this 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 apparatus provided in the second embodiment and the method provided in the first embodiment belong to the same inventive concept, solve the same technical problem, and achieve the same technical effect, and the apparatus provided in the second embodiment can implement all the methods of the first embodiment, and the same parts are not described again.
EXAMPLE III
Based on the same inventive concept, an embodiment of the present invention further provides a robot path planning apparatus, as shown in fig. 7, the apparatus includes:
including a memory 702, a processor 701, and a user interface 703;
the memory 702 for storing a computer program;
the user interface 703 is used for interacting with a user;
the processor 701 is configured to read the computer program in the memory 702, and when the processor 701 executes the computer program, the processor implements:
acquiring self 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.
Wherein in fig. 7, the bus architecture may include any number of interconnected buses and bridges, with one or more processors, represented by processor 701, and various circuits, represented by memory 702, being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any 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 a CPU, an ASIC, an FPGA, or a CPLD, and the processor 701 may also adopt a multi-core architecture.
The processor 701 implements the robot path planning method according to any one of the first embodiment when executing the computer program stored in the memory 702.
It should be noted that the apparatus provided in the third embodiment and the method provided in the first embodiment belong to the same inventive concept, solve the same technical problem, and achieve the same technical effect, and the apparatus provided in the third embodiment can implement all the methods of the first embodiment, and the same parts are not described again.
The present application also proposes a processor-readable storage medium. The processor-readable storage medium stores a computer program, and the processor implements any one of the robot path planning methods of the first embodiment when executing the computer program.
It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
As will be appreciated by one skilled in the art, 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, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (11)
1. A robot path planning method is applied to a mobile robot and is characterized by comprising the following steps:
acquiring self 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.
2. The method of claim 1, wherein the perceptual context information comprises:
the robot senses surrounding environment information at the current position;
generating an environment map, the environment map including an obstacle area and a passable area;
setting a path node position in the passable area.
3. The method according to claim 2, wherein planning a moving path according to the self status information and the environment information comprises:
the robot sets a decision area range at the current position;
connecting the current position to a straight line of each path node position in the current position decision area range, and if the straight line of the current position and the path node cannot penetrate through the barrier area, setting the path point as a decision path point;
calculating the cost evaluation value from the current position to each decision path point;
setting the 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, moving the robot to the global path point and then continuously searching the next global path point.
4. The method of claim 3, wherein the decision region range is one of:
a circular region;
a square region;
a diamond shaped area.
5. The method according to claim 3, wherein the cost evaluation value is calculated by the formula:
wherein, f (a) represents the cost evaluation value of the a-th decision path point;
ω1is the safety weight coefficient, ω2Is a distance weight coefficient, ω3Is the steering angle weight coefficient;
σarepresenting the closest distance of the a-th decision path point from the surrounding obstacles;
sarepresenting a linear distance of the a-th decision path point from the target point;
v represents the moving speed of the robot;
θaand the included angle between the current orientation of the robot and the connecting line between the a-th decision path point and the current position of the robot is represented.
6. The method of claim 3, wherein the path from the current location to the global waypoint is further followed by a planned robot movement path comprising:
smoothing the planned robot moving path;
and taking the smoothed path as a finally planned robot moving path.
7. The method of claim 6, wherein smoothing the planned robot movement path comprises:
setting a coordinate system on the environment map, setting the coordinate of each global path point as { X, Y }, wherein X is the abscissa set of each global path point, and X is { X ═1,x2,...,xnY is the set of ordinates of the respective global path point, Y ═ Y1,y2,...,ynThe number of the 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 performing smoothing processing on each point in the global path points { X, Y } according to the equal difference sequence set T.
8. The method according to claim 7, wherein the smoothing according to the set of arithmetic sequence T for each of the global path points { X, Y } comprises:
b4: setting the maximum cycle number to be n-1 and setting the current number k to be 0;
b5: judging whether the current times exceed the maximum circulation times, if the current times exceed the maximum circulation times, outputting (xx, yy), and if the current times do not exceed the maximum circulation times, executing the step B6:
b6: calculating a first corrective value and a second corrective value using:
wherein, in the formula, rho1Is the first corrective value, p2The correction value is a second correction value, n is the number of the global path points, T is an arithmetic sequence set from 0 to 1, and k is the current time;
b7: the smoothed path point coordinates (xx, yy) are updated using the following equation:
wherein xx represents the abscissa of the smooth path point, yy represents the ordinate of the smooth path point, and xk+1Abscissa, y, representing the k +1 global path pointk+1Represents the ordinate of the (k + 1) th global path point;
b8: and D, updating the coordinates of the smooth path points, setting the current time k as k +1, and returning to the step B5.
9. A robot path planning apparatus, comprising:
the state acquisition module is configured to acquire state information of the state acquisition module;
a context awareness module configured to perceive context information;
the path planning module is configured to plan a moving path according to the self state information and the environment information;
wherein the state information includes a start position, a target position, and a moving speed.
10. A robot path planning apparatus, comprising a memory, a processor and a user interface;
the memory for storing a computer program;
the user interface is used for realizing interaction with a user;
the processor, configured to read the 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 8.
11. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program which, when executed by a processor, implements a robot path planning method according to one of claims 1 to 8.
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