CN111487967A - Robot path planning method, device, medium and equipment - Google Patents
Robot path planning method, device, medium and equipment Download PDFInfo
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- CN111487967A CN111487967A CN202010307705.3A CN202010307705A CN111487967A CN 111487967 A CN111487967 A CN 111487967A CN 202010307705 A CN202010307705 A CN 202010307705A CN 111487967 A CN111487967 A CN 111487967A
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- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
Abstract
The disclosure relates to a robot path planning method, a device, a medium and equipment, which belong to the field of robots and can quickly search an optimal path. A robot path planning method, comprising: generating a sampling point from the initial position to the target position by using a sampling method; finding a hypersphere from the starting position to the target position based on the sampling point, wherein the hypersphere is a hypersphere with the sampling point as a sphere center and a distance between the sampling point and a nearest obstacle as a radius; finding an optimized path from the starting position to the target position within a connected domain formed by the hypersphere from the starting position to the target position.
Description
Technical Field
The present disclosure relates to the field of robots, and in particular, to a method, an apparatus, a medium, and a device for planning a robot path.
Background
The current robot path planning method can quickly search a feasible path, but the optimization of the path is difficult to guarantee.
Disclosure of Invention
The invention aims to provide a robot path planning method, a device, a medium and equipment, which can quickly search an optimal path.
According to a first embodiment of the present disclosure, there is provided a robot path planning method including: generating a sampling point from the initial position to the target position by using a sampling method; finding a hypersphere from the starting position to the target position based on the sampling point, wherein the hypersphere is a hypersphere with the sampling point as a sphere center and a distance between the sampling point and a nearest obstacle as a radius; finding an optimized path from the starting position to the target position within a connected domain formed by the hypersphere from the starting position to the target position.
According to a second embodiment of the present disclosure, there is provided a robot path planning apparatus including: the sampling point generating module is used for generating sampling points from the initial position to the target position by using a sampling method; a hypersphere finding module for finding a hypersphere from the start position to the target position based on the sampling point, the hypersphere being a hypersphere with the sampling point as a center of sphere and a distance between the sampling point and its nearest obstacle as a radius; and the optimized path searching module is used for searching an optimized path from the starting position to the target position in a connected domain formed by the hypersphere from the starting position to the target position.
According to a third embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to the first embodiment of the present disclosure.
According to a fourth embodiment of the present disclosure, there is provided an electronic apparatus including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to carry out the steps of the method according to the first embodiment of the disclosure.
By adopting the technical scheme, firstly, the sampling method is utilized to generate the sampling points from the initial position to the target position, then the hypersphere from the initial position to the target position is searched based on the sampling points, namely, the hypersphere structure space near the sampling points is searched until the obstacle is met, thus each non-collision sampling point can be expanded into a non-collision hypersphere, the hypersphere can be continuously connected along with the increase of the number of the sampling points, finally, a connected domain from the initial position to the target position is formed, and then a path meeting the optimal condition can be searched in the connected domain. The technical scheme can simultaneously meet the path planning of the mobile robot and the high-freedom-degree mechanical arm, and can find the optimal path meeting the optimal condition while ensuring that the path is quickly searched.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flow chart of a robot path planning method according to an embodiment of the present disclosure.
Fig. 2 is yet another flow chart of a robot path planning method according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of path planning in two-dimensional space.
Fig. 4 is a schematic block diagram of a robot path planning apparatus according to an embodiment of the present disclosure.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flow chart of a robot path planning method according to an embodiment of the present disclosure. The robot path planning method is suitable for planning paths for mobile robots and also suitable for planning paths for mechanical arms (such as humanoid mechanical arms) of the robots. As shown in fig. 1, the robot path planning method includes the following steps S11 to S13.
In step S11, a sampling point from the start position to the target position is generated by a sampling method.
The sampling Method may be a sampling Method such as a rapid-expansion Random Tree (RRT), a Probabilistic Roadmap (PRM), or the like.
In step S12, a hypersphere from the start position to the target position is found based on the sampling point, and the hypersphere is a hypersphere with the sampling point as the center of sphere and the distance between the sampling point and its nearest obstacle as the radius.
The hypersphere is a sphere in an n-dimensional space, the hypersphere is a common circle if the space is two-dimensional, the hypersphere is a sphere if the space is three-dimensional, and the hypersphere is a hypersphere in a high-dimensional space if the space is high-dimensional. The hypersphere has two parameters of radius r and sphere center C. The general equation for a hypersphere is as follows:
in the formula, r is the radius of the super sphere, n represents an n-dimensional space, x is any point on the spherical surface of the super sphere, and C is the spherical center of the super sphere.
The dimension space of the hyper-sphere involved in the present disclosure is the same as the path dimension of the object for which a path needs to be planned. For example, if a path is planned for a mobile robot, since the mobile robot usually performs a path search in a two-dimensional or three-dimensional space, the dimension space of the hyper-sphere is a two-dimensional or three-dimensional space in this case. For another example, if a path is planned for a robot arm of a robot, since the robot arm, for example, a humanoid robot arm, is usually six-or seven-degree-of-freedom, it is necessary to search for a path for the robot arm in a six-or seven-dimensional space, and coordinate axis units and ranges of each dimension are angles of each joint and limit ranges, in which case, the dimension space of the hyper-sphere is a six-or seven-dimensional space.
In step S13, an optimized path from the start position to the target position is found within a connected domain from the start position to the target position formed by the hyper-sphere.
By adopting the technical scheme, firstly, the sampling method is utilized to generate the sampling points from the initial position to the target position, then the hypersphere from the initial position to the target position is searched based on the sampling points, namely, the hypersphere structure space near the sampling points is searched until the obstacle is met, thus each non-collision sampling point can be expanded into a non-collision hypersphere, the hypersphere can be continuously connected along with the increase of the number of the sampling points, finally, a connected domain from the initial position to the target position is formed, and then a path meeting the optimal condition can be searched in the connected domain. The technical scheme can simultaneously meet the path planning of the mobile robot and the high-freedom-degree mechanical arm, and can find the optimal path meeting the optimal condition while ensuring that the path is quickly searched.
Fig. 2 is yet another flow chart of a robot path planning method according to an embodiment of the present disclosure. As shown in fig. 2, the method includes the following steps S21 to S29.
In step S21, initialization is performed. That is, a start position x of an object (e.g., a mobile robot or a robot arm of a robot) whose path needs to be planned is setinitAnd a target position xgoal(ii) a And setting termination conditions of the robot path planning method, such as iteration time limit or iteration number limit.
In step S22, a start position x is generated by a sampling methodinitTo the target position xgoalSample point x ofsamp。
In step S23, it is determined whether or not the sample point collides with an obstacle. The step can be realized by adopting a plurality of open-source logistics simulation engines or logistics simulation platforms, and the sampling point x generated in the step S22 is usedsampAs an input, it is determined whether a collision has occurred. If a collision occurs, go to step S28; if no collision occurs, the sampling point x is indicatedsampIs an available point in the path planning, the process goes to step S24.
In step S24, the sample point x is determinedsampWhether it is located within its nearest hypersphere. If the super-sphere is within the recent super-sphere, the process goes to step S27. If not, the process goes to step S25.
In step S25, a new hyper-sphere is generated. That is, x is generated with the current sample pointsampAs the center of the sphere, with the current sample point xsampObstacle o nearest theretosampThe distance between them is a new hyper-sphere of radius.
Wherein the radius can be calculated by:
rsamp＝||osamp-xsamp|| (2)
wherein r issampRepresenting the current sample point xsampObstacle o nearest theretosampThe distance between them.
In step S26, the shortest path is updated. If a new hypersphere is generated in step S25, it is determined in step S26 whether a plurality of hypersphere links can be found and connected from the start position xinitTo the current sample point xsampCan be connected. If the connection is available, several connection modes may exist, but a unique path with the shortest distance can be found from the connection modes and recorded. The shortest path needs to be updated each time a new hyper-sphere is generated, until the target position x is contained within the new hyper-sphere generated at a certain timegoalUntil then, from the starting position xinitTo the target position xgoalThe path of (1) is the shortest path connecting all the generated sampling points at present.
In step S27, if the sampling point collides with an obstacle, the unobstructed space is updated, i.e., the radius of the nearest hyper-sphere is updated based on the distance between the center of the obstacle and the nearest hyper-sphere. That is, the center C of the obstacle and its nearest hyper-sphere is calculatednearDistance L between:
L＝||xsamp-Cnear|| (3)
if L<rnearThen let rnearL, wherein rnearIs and sample point xsampI.e. the radius of the hyper-sphere closest to the current obstacle.
In step S28, it is determined whether or not the termination condition is satisfied. If so, go to step S29, and if not, return to step S22.
In step S29, the path is optimized. That is, if the start position x is found in step S26initTo the target position xgoalIf the shortest path is communicated, the target position can be reached, and if the shortest path is not found, the path finding is failed. When a path is connected, the path is a sampling point phase in a hyper-sphere connected domain from an initial position to a target position generated by a plurality of hyper-spheresEven generated, the path may not be smooth. Moreover, although the shortest path found in step S26 is the optimal path among all paths obtained by connecting sampling points, the shortest path is not necessarily the optimal path in the connected domain formed by the hypersphere; the optimal path from the starting position to the target position is obviously an n-dimensional straight line, but the target position cannot be reached from the starting position straight line due to obstacles. Therefore, in step S29, the shortest path found in step S26 may be first smoothed, and then the smoothed shortest path and an n-dimensional straight line are weighted and averaged to obtain an optimized path, where n is a dimension of the robot path space.
The shortest path after the smoothing process and the n-dimensional straight line are weighted and averaged to obtain an optimized path, which can be implemented by using the following formula:
L’＝k*Lpath+(1-k)*Lline(4)
wherein L' is the optimized path after weighted average, LpathIs the shortest path after smoothing, LlineIs an n-dimensional straight line and k is a coefficient. k has a value range of [0,1 ]]And the value of k should be L' as small as possible without collision, and the smaller the value of k, the closer the final optimized path is to a straight line.
Fig. 3 is a schematic diagram of path planning in two-dimensional space. As shown in FIG. 3, the starting position is xinitTarget position is xgoalThe hypersphere is a plane circle in two-dimensional space, the circle center is C, the radius is r, and each circle center is also a sampling point x generated by each iterationsampOn the obstacle 31 and the sampling point xsampThe nearest point is osamp. From a starting position xinitTo the target position xgoalThe space of circular connection is the starting position xinitTo the target position xgoalA communication domain capable of communicating. The black solid line 32 is an optimal sampling point connecting line (i.e., the shortest path obtained in step S26) generated after the iteration is completed, the black dotted line 33 is a path curve after the B-spline smoothing processing, and the long dotted line 34 is a line drawn from the starting position xinitTo the target position xgoalBy the straight line of (3), the black dot-dash line 35 being the weightThe calculated path curve with the minimum k value is the optimized path obtained by the path planning method according to the embodiment of the disclosure.
Fig. 4 is a schematic block diagram of a robot path planning apparatus according to an embodiment of the present disclosure. As shown in fig. 4, the apparatus includes: a sampling point generating module 41, configured to generate a sampling point from the start position to the target position by using a sampling method; a hypersphere finding module 42, configured to find a hypersphere from the starting position to the target position based on the sampling point, where the hypersphere is a hypersphere with the sampling point as a center of sphere and a radius of a distance between the sampling point and a nearest obstacle; and an optimized path finding module 43, configured to find an optimized path from the start position to the target position within a connected domain from the start position to the target position formed by the hyper-sphere.
By adopting the technical scheme, firstly, the sampling method is utilized to generate the sampling points from the initial position to the target position, then the hypersphere from the initial position to the target position is searched based on the sampling points, namely, the hypersphere structure space near the sampling points is searched until the obstacle is met, thus each non-collision sampling point can be expanded into a non-collision hypersphere, the hypersphere can be continuously connected along with the increase of the number of the sampling points, finally, a connected domain from the initial position to the target position is formed, and then a path meeting the optimal condition can be searched in the connected domain. The technical scheme can simultaneously meet the path planning of the mobile robot and the high-freedom-degree mechanical arm, and can find the optimal path meeting the optimal condition while ensuring that the path is quickly searched.
Optionally, the hypersphere finding module 42 is configured to: and under the condition that the sampling point does not collide with the obstacle and is not positioned in the nearest hypersphere, generating a new hypersphere taking the sampling point as the center of a sphere and the distance between the sampling point and the nearest obstacle as a radius.
Optionally, the hypersphere finding module 42 is configured to: in the event that a sampling point collides with an obstacle, the radius of its nearest hyper-sphere is updated based on the distance between the obstacle and its sphere center.
Optionally, the optimized path finding module 43 is configured to: searching a shortest path from the starting position to the target position formed by connecting sampling points in a connected domain from the starting position to the target position formed by the hypersphere; carrying out smoothing treatment on the shortest path; and carrying out weighted average on the shortest path after the smoothing processing and an n-dimensional straight line to obtain an optimized path, wherein n is the dimension of a path space.
Optionally, the optimized path finding module 43 performs weighted average on the smoothed shortest path and the n-dimensional straight line to obtain an optimized path, and the optimized path is implemented by using the following formula:
L’＝k*Lpath+(1-k)*Lline
wherein L' is the optimized path after weighted average, LpathIs the shortest path after smoothing, LlineIs an n-dimensional straight line and k is a coefficient.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 5 is a block diagram illustrating an electronic device 700 according to an example embodiment. As shown in fig. 5, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the robot path planning method. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 705 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable logic devices (Programmable L ic devices, P L D), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for performing the robot path planning method described above.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the robot path planning method described above is also provided. For example, the computer readable storage medium may be the above-mentioned memory 702 comprising program instructions executable by the processor 701 of the electronic device 700 to perform the above-mentioned robot path planning method.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A robot path planning method is characterized by comprising the following steps:
generating a sampling point from the initial position to the target position by using a sampling method;
finding a hypersphere from the starting position to the target position based on the sampling point, wherein the hypersphere is a hypersphere with the sampling point as a sphere center and a distance between the sampling point and a nearest obstacle as a radius;
finding an optimized path from the starting position to the target position within a connected domain formed by the hypersphere from the starting position to the target position.
2. The method of claim 1, wherein finding the hyper-sphere from the start position to the target position based on the sample points comprises:
and under the condition that the sampling point does not collide with an obstacle and is not positioned in the nearest hypersphere, generating a new hypersphere taking the sampling point as the center of a sphere and the distance between the sampling point and the nearest obstacle as a radius.
3. The method of claim 1, wherein finding the hyper-sphere from the start position to the target position based on the sample points comprises:
in the event that the sampling point collides with an obstacle, the radius of its nearest hyper-sphere is updated based on the distance between the obstacle and the center of its nearest hyper-sphere.
4. The method of claim 1, wherein said finding an optimized path from the starting location to the target location within a connected domain formed by the hypersphere from the starting location to the target location comprises:
searching a shortest path from the starting position to the target position, which is formed by connecting the sampling points, in a connected domain formed by the hypersphere from the starting position to the target position;
performing smoothing processing on the shortest path;
and carrying out weighted average on the shortest path after the smoothing processing and an n-dimensional straight line to obtain the optimized path, wherein n is the dimension of a path space.
5. The method according to claim 4, wherein the weighted average of the smoothed shortest path and the n-dimensional straight line is performed to obtain the optimized path, and the method is implemented by using the following formula:
L’＝k*Lpath+(1-k)*Lline
wherein L' is the optimized path after weighted average, LpathIs the shortest path after smoothing, LlineIs an n-dimensional straight line and k is a coefficient.
6. A robot path planning apparatus, comprising:
the sampling point generating module is used for generating sampling points from the initial position to the target position by using a sampling method;
a hypersphere finding module for finding a hypersphere from the start position to the target position based on the sampling point, the hypersphere being a hypersphere with the sampling point as a center of sphere and a distance between the sampling point and its nearest obstacle as a radius;
and the optimized path searching module is used for searching an optimized path from the starting position to the target position in a connected domain formed by the hypersphere from the starting position to the target position.
7. The apparatus of claim 6, wherein the hypersphere finding module is to:
and under the condition that the sampling point does not collide with an obstacle and is not positioned in the nearest hypersphere, generating a new hypersphere taking the sampling point as the center of a sphere and the distance between the sampling point and the nearest obstacle as a radius.
8. The apparatus of claim 6, wherein the optimized path finding module is configured to:
searching a shortest path from the starting position to the target position, which is formed by connecting the sampling points, in a connected domain formed by the hypersphere from the starting position to the target position;
performing smoothing processing on the shortest path;
and carrying out weighted average on the shortest path after the smoothing processing and an n-dimensional straight line to obtain the optimized path, wherein n is the dimension of a path space.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 5.
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