CN116804556B - A probability maximization path planning method for arbitrary convex polygon robots - Google Patents
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Abstract
The invention discloses a probability maximization path planning method of an arbitrary convex polygon robot. Maximizing the probability of successfully reaching the target point at the time of online path planning, allows the robot to tend to pass through a broader area, thereby providing more options for successfully bypassing obstacles during navigation. The invention maximizes the probability of reaching the target point in each planning, rather than simply searching the shortest path, so that the path planning speed is high and the planning result is excellent.
Description
Technical Field
The invention relates to the field of mobile robots, in particular to a probability maximization path planning method for an arbitrary convex polygon robot.
Background
Mobile robots have been a hotspot in research fields for decades, one of the rapidly developing technologies in the world today, attracting intensive research by scholars of various countries. Technological advances over the years have brought robotics from laboratories to almost all aspects of science, engineering, industry, manufacturing, transportation and life. The mobile robot executes tasks in a complex environment and has the functions of positioning, sensing, planning, controlling and the like, namely, the mobile robot performs sensing modeling on the surrounding environment in the motion process to obtain the pose of the mobile robot, plans the global path and the local track, and controls the robot to move along the expected track. The path planning of the mobile robot means that under a specific environment, an optimal or suboptimal collision-free path from an initial position to a target position is found according to a specific performance index (usually, the shortest path). Path planning techniques can generally be divided into two broad categories: global path planning and local path planning. Based on the determination of the known surrounding environment, the robot needs to perform global path planning, such as Dijkstra algorithm, a-algorithm, visual method, artificial potential field method, etc., before starting navigation. In local path planning, the surrounding environment is usually unknown or almost unknown, and only the surrounding environment information can be obtained from the sensor (such as a laser radar), and some popular algorithms are for example fast exploring random trees (RRT), artificial neural networks, particle Swarm Optimization (PSO), ant colony Algorithm (ACO), etc.
The conventional path planning mainly has the following three problems: 1) The shape of the robot is simply regarded as a circle, and the specific shape is not considered; 2) Only the shortest path can be found, the robot can possibly be guided to pass through a narrow area, and when more static or dynamic obstacles are found due to environmental changes, the possibility of bypassing the obstacles is low; 3) The map needs to be updated continuously according to the laser radar data, and then the route searching is performed based on the updated map in real time, so that the problem of large calculation amount exists.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a probability maximization path planning method of an arbitrary convex polygon robot, which belongs to local path planning, is not only applicable to round robots, but also applicable to arbitrary convex polygon robots, generates a basic path library offline, and calculates the collision relation between the convex polygon robot and an obstacle offline. Maximizing the probability of successfully reaching the target point at the time of online path planning, allows the robot to tend to pass through a broader area, thereby providing more options for successfully bypassing obstacles during navigation. As a large amount of calculation is finished off-line, the on-line calculation is reduced as much as possible, and therefore, the path planning method provided by the invention has very high solving speed.
The aim of the invention is achieved by the following technical scheme:
A probability maximization path planning method for an arbitrary convex polygon robot, the method comprising the steps of:
Step one: generating a basic path library covering a 360-degree range around the robot offline, and calculating the collision relation between each basic path in the basic path library and each grid point in the range surrounding the basic path library offline;
step two: acquiring necessary information including a static map, robot motion capability, robot positioning and real-time laser data, searching based on the static map and the laser data to obtain a global path from a starting point to the target point and bypassing an obstacle after obtaining the position of the target point, and smoothing the global path to obtain a global smooth path;
Step three: on the global smooth path, a point with the distance S from the current actual position of the robot is selected as a sub-target point between the current position of the robot and the position of the target point, and a local path with the maximum probability of reaching the sub-target point is planned based on the collision relationship between the basic path obtained in the step one and the grid point; as the robot moves, sub-target points are also dynamically selected on the global path.
Further, the first step includes the following sub-steps:
s101: starting from the starting point, rotating according to a preset length and a preset angle, and generating an end point when rotating once; then generating a spline curve between the starting point and each end point; these multiple spline curves scattered from the starting point form a first scattered sub-path;
S102: performing the same operation as S101 with the end point of each sub-path in the first-time scattered paths as a start point, to obtain second-time scattered sub-paths scattered again from the end point of each first-time scattered sub-path;
S103: and the sub-paths are further scattered by taking the end point of the previous sub-path as a starting point every time, so as to obtain a plurality of scattered sub-paths; the method comprises the steps that any one of the final end points from the initial start point to the corresponding final end point is a basic path, and the basic paths form a basic path library;
S104: calculating the minimum range surrounding all basic paths, uniformly discretizing a continuous curve forming each basic path into a plurality of points, and detecting whether grid points in the minimum range enter the interior of the robot contour when the robot moves along the basic path; if the collision occurs, the collision occurs; otherwise, there is no collision; and storing the established collision relation between the grid points and the basic paths into a relation list.
Further, the preset angles of rotation are equal when the sub-paths are scattered each time, and the number of sub-paths scattered from each starting point is equal; the distances from the start point to the end point are also equal in each of the scattered sub-paths.
Further, the third step comprises the following sub-steps:
S301: expanding peripheral contour points of the obstacle obtained by the laser radar, wherein the expansion size is i;
s302: marking whether each basic path collides with the peripheral outline point of each obstacle according to the collision relation between each grid point and the basic path generated in the off-line in the step one;
S303: taking the basic paths with the first scattered sub-paths overlapped as a group, calculating the probability that each group of collision-free paths reach the current sub-target point, and sequencing the collision-free path groups according to the probability from large to small;
S304: performing in-situ rotation collision detection on the robot;
S305: judging whether a collision-free path group with the highest probability of passing the collision detection is found, if not, reducing the value of the expansion size i, repeating S302-S304 until i is reduced to 0, and indicating that a safety path does not exist; if yes, iteration is terminated, and the sub-path scattered for the first time by the collision-free path group is output as the optimal local path.
Further, in S301, expanding the peripheral contour points of the obstacle obtained by the laser radar specifically includes:
For each data point obtained by the laser radar, taking the data point as a center, respectively adding a plurality of map grid points along the positive and negative directions of x and y parallel to the horizontal plane, and finally forming a plurality of map grid points surrounding the data point; the expansion dimension i is the multiple of the data point after expansion relative to the data point before expansion.
Further, in S303, the probability that each group of collision-free paths reaches the current sub-target point is the sum of the probabilities that all the basic paths in the group reach the sub-target point; the probability of each basic path is calculated by the included angle between the direction of the end of the path and the direction of the sub-target point.
Further, in S304, when performing the robot in-situ rotation collision check, it is checked whether it is safe to rotate from the current direction of the robot to the path direction.
Further, in order to cover a 360 ° range around the robot, the generation operation of the basic path of S101 to S103 is performed once every rotation of a fixed angle around the robot.
An electronic device, comprising:
One or more processors;
and the storage device is used for storing one or more programs, and when the one or more programs are executed by the electronic equipment, the electronic equipment can realize the probability maximization path planning method of the random convex polygon robot.
A computer readable storage medium having stored thereon a program which, when executed by a processor, implements a method for probability maximization path planning for said arbitrary convex polygon robot.
The beneficial effects of the invention are as follows:
1. The path planning method provided by the invention is not only suitable for the circular robot, but also can be applied to any convex polygon robot.
2. The path planning method provided by the invention maximizes the probability of reaching the target point at each planning time instead of simply searching the shortest path.
3. The path planning method provided by the invention reduces the online calculation amount as much as possible, and a large amount of calculation is finished offline. In order to avoid online searching and reduce the computational complexity, obstacles in a certain range near the robot are considered to be known, a basic path library is generated offline, and a collision-free path with the highest probability is selected from the candidate path library only according to obstacle information obtained by a laser radar during online planning.
Drawings
Fig. 1 is a schematic diagram of 7 path groups in an embodiment.
Fig. 2 is a schematic view of the expansion of an obstacle, from left to right, with expansion sizes of 0, 1, 2, 3, respectively.
Fig. 3 is a schematic view of a rectangular robot in-situ rotation security inspection.
Fig. 4 is a test scenario of embodiment 1.
Fig. 5 is a schematic diagram of the actual running path of the rectangular robot in embodiment 1.
Fig. 6 is a schematic diagram of a mobile robot used for the test in example 2.
Fig. 7 is a practical scene of embodiment 2.
Fig. 8 is a schematic diagram of a global smooth path and dynamically selecting sub-target points in embodiment 2.
Fig. 9 is a schematic diagram of a current feasible path and an optimal path of the mobile robot in embodiment 2.
Fig. 10 is a schematic diagram of an actual running path of the mobile robot in embodiment 2.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" depending on the context.
The probability maximization path planning method of any convex polygon robot is used for avoiding online searching, reducing calculation complexity, regarding an obstacle in a certain range near the robot as known, generating a basic path library offline, and calculating the collision relationship between the convex polygon robot and the obstacle offline. Maximizing the probability of successfully reaching the target point at the time of online path planning, allows the robot to tend to pass through a broader area, thereby providing more options for successfully bypassing obstacles during navigation. In order to enable the robot to keep a certain safety distance from an obstacle in the moving process, an obstacle expansion method is provided, collision detection on a path is not needed on line, and the calculated amount is greatly reduced. As a large amount of calculation is finished off-line, the on-line calculation is reduced as much as possible, and therefore, the path planning method provided by the invention has very high solving speed.
The probability maximization path planning method of any convex polygon robot provided by the embodiment of the invention comprises the following steps:
Step one: generating a basic path library covering a 360-degree range around the robot offline, and calculating the collision relation between each basic path in the basic path library and each grid point in the range surrounding the basic path library offline.
S101: starting from the starting point, rotating according to a preset length and a preset angle, and generating an end point when rotating once; then generating a spline curve between the starting point and each end point; these multiple spline curves scattered from the starting point form a first scattered sub-path;
S102: performing the same operation as S101 with the end point of each sub-path in the first-time scattered paths as a start point, to obtain second-time scattered sub-paths scattered again from the end point of each first-time scattered sub-path;
S103: and the sub-paths are further scattered by taking the end point of the previous sub-path as a starting point every time, so as to obtain a plurality of scattered sub-paths; the basic paths form a basic path library from the initial starting point to any final end point.
Preferably, the preset angle of rotation is equal each time the sub-paths are scattered, and the number of sub-paths scattered from each starting point is equal; the distances from the start point to the end point are also equal in each of the scattered sub-paths.
To cover a 360 ° range around the robot, step S101 is performed every 10 ° range. Fig. 1 shows an example of a basic path library, within 10 degrees, from a starting point, scattered in 7 horizontal directions according to a sub path length of 1 meter, and the scattered angles are-27 degrees, -18 degrees, -9 degrees, 0 degrees, 9 degrees, 18 degrees and 27 degrees, respectively, and each sub path is generated by using a cubic spline curve. These 7 paths are denoted as r 1-r 7 as 7 path groups. Then, the end points of the 7 sub-paths are used as starting points and are dispersed in 7 horizontal directions in the same way to obtain secondary unfolded sub-paths; s101 is performed again, 7 horizontal direction dispersion is performed again. Therefore, each set of paths contains 49 paths, and 7 paths together have 343 paths. These 343 paths are merely the number of paths within 10 degrees around the overlay robot, which contains 252 path groups in total within 360 degrees around the overlay robot.
S104: calculating the minimum range surrounding all basic paths, uniformly discretizing a continuous curve forming each basic path into a plurality of points, and detecting whether grid points in the minimum range enter the interior of the robot contour when the robot moves along the basic path; if the collision occurs, the collision occurs; otherwise, there is no collision; and storing the established collision relation between the grid points and the basic paths into a relation list.
When the robot approximates a circle, it is only necessary to calculate whether the distance between the grid point and each point on the path is smaller than the radius of the robot in order to determine whether the grid point enters the interior of the robot contour. Whereas for any convex polygon robot it has to be checked if a grid point enters the inside of the convex polygon when the polygon robot moves along the path, the calculation of the correspondence will take more time than for a circular robot. Fortunately, it is calculated off-line and does not affect the computational efficiency at on-line run-time. And when the system is started, loading a pre-generated path library and collision relations between the paths and each grid point into a memory of a robot computer.
Step two: necessary information including a static map, robot motion capability, robot positioning and real-time laser data is acquired, after the position of a target point is obtained, a global path from a starting point to the target point and bypassing an obstacle is obtained by searching based on the static map and the laser data, and the global path is smoothed to obtain the global smooth path.
The static map comprises a grid map, map resolution (e.g. 5 cm), map origin coordinates.
Step three: on the global smooth path, a point with the distance S from the current actual position of the robot is selected as a sub-target point between the current position of the robot and the position of the target point, and a local path with the maximum probability of reaching the sub-target point is planned based on the collision relationship between the basic path obtained in the step one and the grid point; as the robot moves, sub-target points are also dynamically selected on the global path.
The third step comprises the following sub-steps:
S301: and expanding the peripheral contour points of the obstacle obtained by the laser radar, wherein the expansion size is i.
As an example, as shown in fig. 2, for each data point obtained by the laser radar, 8 laser points are added to expand, in such a way that i map grid points are respectively expanded in the forward and backward directions parallel to x and y with the laser point as the center, and finally a plurality of map grid points surrounding the data point are formed. When the expansion size is i, it is expressed as i times the size occupied by the data points after expansion relative to the data points before expansion. Fig. 2 gives a schematic illustration of the increase of 8 laser spots with expansion dimensions i=0, 1,2, 3.
S302: and marking whether each basic path collides with the peripheral outline point of each obstacle according to the collision relation between each grid point and the basic path generated in the off-line in the step one.
S303: and taking the basic paths with the first scattered sub-paths overlapped as a group, calculating the probability that each group of collision-free paths reaches the current sub-target point, and sequencing the collision-free path groups according to the probability from large to small.
Preferably, the probability that each group of collision-free paths reaches the current sub-target point is the sum of the probabilities that all basic paths in the group reach the sub-target points; the probability of each basic path is calculated by the included angle between the direction of the end of the path and the direction of the sub-target point.
As one example, all collision-free paths are found from the lidar data. If collision-free paths exist, the probability that all path groups where the collision-free paths are located reach the sub-target points is calculated. Taking path group r1 as an example, the probability of reaching the sub-target point is the sum of 49 paths consisting of r11 to r17, r111 to r 117. The probability magnitude of each path is calculated from the direction of the end of the path and the direction of the target point. And finally, sorting the path groups according to the probability from large to small.
S304: and performing in-situ rotation collision detection on the robot.
If the current orientation of the robot does not coincide with the tangential direction at the start of the selected path, the robot will first rotate in place. As shown in fig. 3, for a circular robot, the robot in-situ rotation must be safe, whereas for a convex polygon robot, it is necessary to check whether it is safe to rotate from the current orientation to the path orientation in-situ. Without loss of generality, the method of in-situ rotational collision detection is exemplified by a rectangular robot, if the obstacle distance d 1 is greater than the radius of the circumscribed circle of the rectangle, the rectangle will not collide, and the maximum allowable angles alpha 1、α2 for clockwise and counterclockwise rotation are 180 degrees respectively. If d 1 is smaller than the inscribed circle radius of the rectangle, then a collision has occurred. If d 1 is in between, then α 1、α2 is calculated. In the example shown in fig. 3, the maximum allowable rotation angle of the robot clockwise is α 1, and the maximum allowable rotation angle of the robot counterclockwise is α 2 =180 degrees ifOr alternativelyThe in-situ rotation is safe.
S305: judging whether a collision-free path group with the highest probability of passing the collision detection is found, if not, reducing the value of the expansion size i, repeating S302-S304 until i is reduced to 0, and indicating that a safety path does not exist; if yes, iteration is terminated, and the sub-path scattered for the first time by the collision-free path group is output as the optimal local path.
The pseudocode for finding the optimal security path is as follows:
the process according to the invention is further illustrated in the following in two examples.
Example 1
Taking autonomous movement of a rectangular robot in a test scene as an example, the embodiment comprises the following specific steps:
step one: offline generating a basic path library and calculating collision relation
A basic path library is generated in advance, and collision relation between paths in the path library and each grid point is calculated offline.
Step two: inputting necessary information and dynamically selecting sub-target points.
And inputting information such as a static map, robot motion capability, robot positioning, target point position, real-time laser data and the like. The test scenario is shown in fig. 4, with dimensions of 10m long and 5m wide. The resolution of the grid map is 5cm, the left lower corner of the map is the origin, the coordinates of the origin are [ -2.0, -2.0], the black part in the grid map indicates that the grid is occupied, the white part indicates that the grid is idle, and no obstacle exists. The laser radar is installed on the robot, so that laser data can be acquired in real time. As shown in fig. 4, the initial position of the robot is directed to the right and is in a stationary state. The motion capabilities of the robot are as follows: linear velocity v e 0,1 m/s, linear acceleration av e-1, 1 m/s 2, angular velocity ω e-1, 1 rad/s, angular acceleration aw e-1, 1 rad/s 2. In this embodiment, the sub-target points are not dynamically selected from the global smooth path, but are fixed, and small circles in fig. 4 represent the sub-target points. The two sides of the rectangular robot at the initial moment are provided with barriers, and the rectangular robot cannot rotate to the target position in situ.
Step three: probability maximization online path planning for arbitrary convex polygon robots
Firstly, expanding laser points (data points obtained by a laser radar are peripheral contour points of an obstacle), wherein the expansion size i is n (n is the maximum expansion size and is a parameter); then, according to the collision relation between the grid points generated offline and the paths, marking whether each path collides with the laser point or not; then calculating the probability that each group of collision-free paths reach the current sub-target point; and finally, performing in-situ rotation collision detection, and finding a path group with the highest probability from the safety paths as an optimal local path. If a safe path with the highest probability is found, the iteration is terminated and the optimal path is output, otherwise, the value of i is reduced, and the steps are repeated. If i is reduced to 0, there is still no safe path, indicating a path planning failure. The online local path planning mainly comprises the following steps:
1) Expanding the laser point;
2) Calculating the probability of the path group reaching the sub-target point;
3) Rotating in situ security inspection;
4) Finding the optimal safe path.
The robot moves from the initial position to the target position autonomously, and on the basis of local path planning, the robot is controlled to move along the planned path by utilizing a path tracking module. Fig. 5 shows an actual path diagram of autonomous movement of a rectangle from an initial state to a target point, indicating that the path planning method proposed by the present invention can be applied to a rectangular robot.
Example 2
The embodiment further describes taking the autonomous movement of the actual mobile robot in the actual scene as an example, and the specific steps are as follows:
step one: offline generating a basic path library and calculating collision relation
A basic path library is generated in advance, and collision relation between paths in the path library and each grid point is calculated offline.
Step two: inputting necessary information, dynamically selecting sub-target point
And inputting information such as a static map, robot motion capability, robot positioning, target point position, real-time laser data and the like. As shown in fig. 6, the robot in actual use is mounted with a 3D laser radar, and distances from the rotation center of the robot to the front, rear, left and right sides are 0.2m, 0.6m, 0.35m and 0.35m, respectively. The motion capabilities of the robot are as follows: linear velocity v e 0,1 m/s, linear acceleration av e-1, 1 m/s 2, angular velocity ω e-1, 1 rad/s, angular acceleration aw e-1, 1 rad/s 2. The actual test scenario is shown in fig. 7, where the start and target points of the navigation task are marked, as well as the added obstacles. The static map built in the scene is shown in fig. 8, the robot is located at the starting point at the initial moment, after receiving the target point, the robot searches the global collision-free path first and smoothes the global collision-free path, and the obtained global smooth path is shown in fig. 8. Points on the global smooth path that are at a distance from the robot position are dynamically selected as sub-target points of the local path planning algorithm, as indicated by small circles in fig. 8.
Step three: probability maximization online path planning for arbitrary convex polygon robots
Mainly comprises the following steps:
1) Expanding the laser point;
2) Calculating the probability of the path group reaching the sub-target point;
3) Rotating in situ security inspection;
4) Finding the optimal safe path.
According to real-time obstacle information obtained by laser, calculating the probability that each path in a basic path library reaches a sub-target point, wherein the probability of a collision-free path is greater than 0, the probability of the collision path is 0, and fig. 9 shows all collision-free paths and optimal safety paths of the robot at the current moment in the motion process, wherein a dotted line is the collision-free path, and a solid line with an arrow is the optimal safety path. On the basis of local path planning, the robot is controlled to move along the planned path by utilizing a path tracking module. The actual running route of the test robot from the start point to the target point is shown in fig. 10.
The electronic device provided by the embodiment of the invention can be applied to any device with data processing capability, and the any device with data processing capability can be a device or a device such as a computer. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. Taking software implementation as an example, the device in a logic sense is formed by reading corresponding computer program instructions in a nonvolatile memory into a memory by a processor of any device with data processing capability. In terms of hardware, in addition to the processor, the memory, the network interface, and the nonvolatile memory, any device with data processing capability in the embodiments of the present invention generally may further include other hardware according to the actual function of the any device with data processing capability, which will not be described herein.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The embodiment of the invention also provides a computer readable storage medium, on which a program is stored, which when executed by a processor, implements the probability maximization path planning method of any convex polygon robot in the above embodiment.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any of the data processing enabled devices described in any of the previous embodiments. The computer readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (SMARTMEDIA CARD, SMC), an SD card, a flash memory card (FLASH CARD), etc. provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any data processing device. The computer readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing apparatus, and may also be used for temporarily storing data that has been output or is to be output.
It will be appreciated by persons skilled in the art that the foregoing description is a preferred embodiment of the invention, and is not intended to limit the invention, but rather to limit the invention to the specific embodiments described, and that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for elements thereof, for the purposes of those skilled in the art. Modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
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