CN117991758A - SLAM navigation obstacle avoidance method based on line laser, chip and robot - Google Patents
SLAM navigation obstacle avoidance method based on line laser, chip and robot Download PDFInfo
- Publication number
- CN117991758A CN117991758A CN202211270151.XA CN202211270151A CN117991758A CN 117991758 A CN117991758 A CN 117991758A CN 202211270151 A CN202211270151 A CN 202211270151A CN 117991758 A CN117991758 A CN 117991758A
- Authority
- CN
- China
- Prior art keywords
- robot
- obstacle avoidance
- obstacle
- point
- grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000008569 process Effects 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 22
- 238000001514 detection method Methods 0.000 claims description 7
- 230000004888 barrier function Effects 0.000 claims description 6
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a SLAM navigation obstacle avoidance method, a chip and a robot based on line laser, which comprise the following steps: s1: when the robot encounters an obstacle in the operation process according to the planned route, the robot performs obstacle avoidance path planning to acquire an obstacle avoidance target point; s2: the robot acquires point cloud data of an obstacle through a line laser sensor, then selects an obstacle avoidance point through an obstacle avoidance target point, and moves to a first obstacle avoidance target point based on the selected obstacle avoidance point; s3: the robot acquires point cloud data of the obstacle through the line laser sensor again at the obstacle avoidance target point, then selects an obstacle avoidance point according to the next obstacle avoidance target point, and moves to the next obstacle avoidance target point according to the new obstacle avoidance point; s4: and so on, the robot keeps away the obstacle in the mode until the robot stops keeping away the obstacle and continues to operate after judging that the robot bypasses the obstacle. So that the robot can accurately bypass the obstacle.
Description
Technical Field
The invention relates to the technical field of intelligent robots, in particular to a SLAM navigation obstacle avoidance method based on line laser, a chip and a robot.
Background
At present, with the rapid development of technology, mobile robots are often used in fields of cleaning, service, inspection and the like to replace manual work, and it is very critical for the mobile robots to be able to accurately, efficiently and intelligently avoid obstacles in the actual operation working scene. When the existing robot encounters an obstacle, the obstacle is sensed mainly through laser SLAM navigation or collision sensors, so that obstacle avoidance is performed. However, laser SLAM navigation has poor perception of dynamic and low obstacles, more collisions and poor obstacle avoidance effect; the collision sensor cannot sense the accurate position of the obstacle, cannot realize accurate obstacle avoidance, and has poor obstacle avoidance effect.
Disclosure of Invention
In order to solve the problems, the invention provides a SLAM navigation obstacle avoidance method based on line laser, a chip and a robot, so that the robot can accurately bypass an obstacle. The specific technical scheme of the invention is as follows:
A SLAM navigation obstacle avoidance method based on line laser comprises the following steps: s1: when the robot encounters an obstacle in the operation process according to the planned route, the robot performs obstacle avoidance path planning to acquire an obstacle avoidance target point; s2: the robot acquires point cloud data of an obstacle through a line laser sensor, acquires an obstacle avoidance point based on the point cloud data of the obstacle, selects the obstacle avoidance point through an obstacle avoidance target point, and moves to a first obstacle avoidance target point based on the selected obstacle avoidance point; s3: the robot acquires point cloud data of the obstacle through the line laser sensor again at the obstacle avoidance target point, acquires a new obstacle avoidance point based on the acquired point cloud data of the obstacle again, selects the obstacle avoidance point according to the next obstacle avoidance target point, and moves to the next obstacle avoidance target point according to the new obstacle avoidance point; s4: the robot acquires point cloud data of the obstacle through the line laser sensor again, and so on, the robot keeps away the obstacle through the mode, and the robot stops keeping away the obstacle and continues to operate until the robot judges that the robot bypasses the obstacle.
Further, in step S1, the robot performs obstacle avoidance path planning to obtain an obstacle avoidance target point, including the following steps: the robot converts the walking map into a grid map, and sets a movement amount and a movement calculation amount for each grid; the robot takes the current grid as a starting point, searches for a grid adjacent to the starting point, sets the starting point as a father node of the adjacent grid, puts the father node into a closed list, and puts the grid which is not occupied by the barrier in the grid adjacent to the starting point into an open list; the robot takes each grid in the open list as a target grid, calculates the movement amount and the movement calculated amount of the target grid, selects the target grid with the minimum sum value of the movement amount and the movement calculated amount, searches for grids adjacent to the selected target grid, sets the selected target grid as a father node of the adjacent grid, moves the father node from the open list to the closed list, and places the grids which are not occupied by the barrier and are not added into the open list in the grids adjacent to the selected target grid; the robot takes each grid in the open list as a target grid, calculates the movement amount and the movement calculated amount of the target grid, selects the target grid with the minimum sum of the movement amount and the movement calculated amount, and so on, and places the adjacent grids of the selected target grid in the open list in the mode until the grid where the obstacle avoidance terminal is positioned is placed in the open list, and takes a father node connected with the obstacle avoidance terminal and the starting point as an obstacle avoidance target point; the moving amount is the moving cost of the robot from the starting point to the target grid, and the moving calculated amount is the calculating cost of the robot from the target grid to the obstacle avoidance end point.
Further, the robot calculating the movement amount includes the steps of: the robot acquires a moving track based on the parent node moving from the starting point to the target grid; the robot obtains the distance between adjacent grids on the moving track; the robot acquires a sum of distances between adjacent grids as a movement amount.
Further, the robot calculating the movement calculation amount includes the steps of: the robot obtains the absolute value of the difference value between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point; the robot obtains the absolute value of the difference value between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point; the robot uses the sum of the absolute value of the difference between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point and the absolute value of the difference between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point as the movement calculation amount.
Further, in step S4, if the robot reaches the obstacle avoidance end point, it is determined that the robot bypasses the obstacle, and the robot continues to operate according to the planned route.
Further, in step S2, the robot acquires the obstacle detouring point based on the point cloud data of the obstacle, including the steps of: the robot converts point cloud data of the obstacle obtained through the line laser sensor into an obstacle coordinate; the robot fits the obstacle coordinates obtained by the autorotation into a point cloud line segment through a least square method according to the obstacle coordinates; the robot sets two end points of the point cloud line segment as obstacle detouring points.
Further, in step S2, the robot selects an obstacle avoidance point through the obstacle avoidance target point, and moves to the first obstacle avoidance target point based on the selected obstacle avoidance point, including the following steps: the robot selects an obstacle avoidance point closest to the obstacle avoidance target point, and then the robot reaches the first obstacle avoidance target point by moving around the selected obstacle avoidance point.
Further, after the robot reaches the first obstacle avoidance target point, the robot adjusts the angle through autorotation to enable the robot to be opposite to the next obstacle avoidance target point, and then the point cloud data of the obstacle is obtained through the line laser sensor.
Further, in step S4, if the robot cannot acquire the point cloud data of the obstacle through the line laser sensor at the obstacle avoidance target point, the robot determines that the robot bypasses the obstacle and continues to operate according to the planned route.
A chip for storing a program configured to perform the line laser based SLAM navigation obstacle avoidance method described above.
The robot comprises a main control chip and a line laser sensor, wherein the main control chip is the chip.
Further, the line laser sensor is arranged at the front end of the robot, the detection distance of the line laser sensor is 10cm, and the detection range is 170 degrees.
Compared with the prior art, the application has the beneficial effects that: the robot enters an obstacle avoidance mode after encountering an obstacle, acquires point cloud data of the obstacle for many times through line laser to calculate outline points of the obstacle, accurately bypasses the obstacle according to the calculated outline points of the obstacle, improves navigation efficiency, and has no collision with dynamic and low obstacles in the navigation process.
Drawings
FIG. 1 is a flow chart of a SLAM navigation obstacle avoidance method based on line laser in an embodiment of the present invention;
FIG. 2 is a schematic view of a robot obstacle detouring in accordance with one embodiment of the present invention;
FIG. 3 is a schematic view illustrating selection of obstacle avoidance target points of a robot according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a detection range of a line laser sensor of a robot according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout.
In the description of the present invention, it should be noted that, for the azimuth words such as "center", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., the azimuth and positional relationships are based on the azimuth or positional relationships shown in the drawings, it is merely for convenience of describing the present invention and simplifying the description, and it is not to be construed as limiting the specific scope of protection of the present invention that the device or element referred to must have a specific azimuth configuration and operation.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features. Thus, the definition of "a first", "a second" feature may explicitly or implicitly include one or more of such features, and in the description of the invention, "at least" means one or more, unless clearly specifically defined otherwise.
In the present invention, unless explicitly stated and limited otherwise, the terms "assembled," "connected," and "connected" are to be construed broadly, e.g., as being either fixedly connected, detachably connected, or integrally connected; or may be a mechanical connection; can be directly connected or connected through an intermediate medium, and can be communicated with the inside of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless specified and limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "below," and "above" a second feature includes the first feature being directly above and obliquely above the second feature, or simply representing the first feature as having a higher level than the second feature. The first feature being "above," "below," and "beneath" the second feature includes the first feature being directly below or obliquely below the second feature, or simply indicating that the first feature is level below the second feature.
The technical scheme and the beneficial effects of the invention are more clear and definite by further describing the specific embodiments of the invention with reference to the drawings in the specification. The embodiments described below are exemplary by referring to the drawings for the purpose of illustrating the invention and are not to be construed as limiting the invention.
As shown in fig. 1 and 2, a line laser-based SLAM navigation obstacle avoidance method includes the following steps:
step S1: when the robot encounters an obstacle in the operation process according to the planned route, the robot performs obstacle avoidance path planning to acquire an obstacle avoidance target point. SLAM (simultaneous localization AND MAPPING), i.e., synchronous positioning and mapping, means that in an unknown environment, a robot positions itself through an internal sensor (encoder, IMU, etc.) and an external sensor (laser sensor or vision sensor) carried by itself, and builds an environment map in an incremental manner by using environment information acquired by the external sensor on the basis of positioning. The SLAM navigation robot acquires a grid map of a work area before performing work, and then plans a walking route based on the grid map, such as moving to a set place, covering walking in the work area, and so on. In the robot operation walking process, if the robot discovers an obstacle through a ranging sensor, a collision sensor or a visual sensor and the like, the robot enters an obstacle avoidance mode, a plurality of obstacle avoidance target points are constructed, then obstacle avoidance is performed through the obstacle avoidance target points and a line laser sensor, and the accuracy of obstacle avoidance is provided.
As one embodiment, in step S1, the robot performs obstacle avoidance path planning to obtain an obstacle avoidance target point, including the following steps: the robot converts the walking map into a grid map, and sets a moving amount and a moving calculation amount for each grid, namely, each grid of the grid map is used as a node; instead of using the grids as nodes, the walking map of the robot can be directly divided into grids with equal sizes, the center point of each grid is used as a node, and the shape of each grid can be rectangular, hexagonal or triangular. The robot takes the current grid as the starting point, at this time, the starting point can be put into the open list, then the grids adjacent to the starting point, namely, eight grids surrounding the starting point are acquired, the starting point is set as the father node of the adjacent eight grids, the father node is put into the closed list from the open list, and the grids which are not occupied by the barrier in the grids adjacent to the starting point are put into the open list. In order to continue searching a route, the robot takes each grid in the open list as a target grid, calculates the movement amount and the movement calculation amount of the target grid, and selects a target grid with the minimum sum of the movement amount and the movement calculation amount as a grid to be moved next by the robot when the robot expects to go to the obstacle avoidance end point, then the robot searches for a grid adjacent to the selected target grid, sets the target grid as a father node of the adjacent grid, then moves the father node from the open list to the closed list, and places a grid which is not occupied by an obstacle and is not added to the open list in the adjacent grid to the selected target grid in the open list. The robot takes each grid in the open list as a target grid again, calculates the movement amount and the movement calculated amount of the target grid, selects the target grid with the minimum sum value of the movement amount and the movement calculated amount, and continues to search for adjacent grids of the selected target grid to be added into the open list. As shown in fig. 3, at the start point, the robot searches for adjacent eight grids, that is, grid 1, grid 2, grid 3, grid 4, grid 6, grid 7, grid 8, and grid 9, and places the eight grids into the open list, and then calculates the movement amount and movement calculation amount of the eight grids. If the sum of the movement amount of the grid 8 and the movement calculation amount is smallest among the eight adjacent grids, the robot selects the grid 8 as the grid to be moved next while placing the starting point as the parent node of the eight grids into the closed list. Then the robot takes the grid 8 as the selected target grid, at this time, the robot takes the grid 8 as the current position of the robot, only as the current position of the robot, and does not need to move to the grid 8, and then the robot searches for the adjacent grids of the grid 8 to obtain the grids 4, 5, 6, 7, 9, 10, 11 and 12. Of the grids obtained in this search, grid 4, grid 6, grid 7 and grid 9 are already in the open list, grid 5 is already in the closed list as a parent node, and grid 10 is occupied by an obstacle, so that only grid 11 and grid 12 need be put in the open list, then the robot takes grid 8 as the current position of the robot, each grid in the open list as a target grid, and the movement amount and movement calculation amount of each grid are calculated one by one. If grid 11 is the smallest sum of the amount of movement and the amount of movement calculation among all grids of the open list, the robot selects grid 11 as the grid to be moved next while placing grid 8 as the parent node of grids 4, 6, 7, 9, 11 and 12 into the closed list, i.e., parent node 1. The robot then searches for neighboring grids of the grid 11 in the same way with the grid 11 as the selected target grid, resulting in grids 7, 8, 9, 10, 12, 13, 14 and 15, and places the grids 13, 14 and 15 in the open list for the same reason, the grid 11 moving from the open list into the closed list, i.e. the parent node 2. The robot sequentially selects the grid 14, the grid 18 and the grid 21 with the smallest sum of the movement amount and the movement calculation amount from the open list, and when the grid 21 is used as the selected target grid, the obstacle avoidance end point is put into the open list, and the robot completes the search. After the search is completed, the robot takes a father node connecting the obstacle avoidance end point and the starting point as an obstacle avoidance target point, namely, the starting point, the father node 1, the father node 2, the father node 3, the grid 18 and the grid 21 as the obstacle avoidance target points, or takes the starting point, the father node 2, the grid 18 and the grid 21 as the obstacle avoidance target points in order to prevent the obstacle avoidance target points from being too close; the moving amount is the moving cost of the robot from the starting point to the target grid, and the moving calculated amount is the calculating cost of the robot from the target grid to the obstacle avoidance end point. The robot keeps away the barrier through setting up a plurality of obstacle avoidance target points, promotes the obstacle avoidance efficiency of robot.
As one of the embodiments, as shown in fig. 3, the robot calculates the movement amount including the steps of: the robot acquires a movement trajectory based on the parent node moving from the starting point to the target grid. When calculating the movement amount of the target grid, the movement track of the robot from the starting point to the target grid is determined, if the target grid is the grid 18, the robot sequentially passes through the father node 1, the father node 2 and the father node 3 to reach the target grid from the starting point, and the starting point, the father node 1, the father node 2, the father node 3 and the grid 18 are sequentially connected to obtain the movement track. The robot obtains the distance between adjacent grids on the moving track, namely each point occupies one grid, calculates the distance between two adjacent points, and calculates the distance between the starting point and the father node 1, the distance between the father node 1 and the father node 2, the distance between the father node 2 and the father node 3 and the distance between the father node 3 and the target grid. The robot may set the movement amount between the adjacent grids in advance, and if the straight line between the two grids is in the horizontal direction or the vertical direction, the distance between the two grids is a, that is, the distance between the parent node 2 and the parent node 3 is a, and if the straight line between the two grids is in the diagonal direction, the distance between the two grids is 1.4A, that is, the distance between the parent node 3 and the target grid is 1.4A. The robot acquires a sum of distances between adjacent grids as a movement amount. The calculated distances are added to form movement amounts, and the movement amounts of the target grids correspondingly change according to different movement tracks. When the robot calculates the distance between adjacent grids, the distance between two coordinates is calculated by a distance formula. Calculating the distance between the starting point and the father node 1, wherein the robot obtains the square value of the absolute value of the difference value between the x coordinate of the starting point and the x coordinate of the grid where the father node 1 is positioned; the robot obtains the square value of the absolute value of the difference between the y coordinate of the starting point and the y coordinate of the grid where the father node 1 is located; the robot obtains the sum of the square value of the absolute value of the difference between the x coordinate of the starting point and the x coordinate of the grid where the parent node 1 is positioned and the square value of the absolute value of the difference between the y coordinate of the starting point and the y coordinate of the grid where the parent node 1 is positioned; the robot square the sum to obtain the distance between the starting point and the parent node 1, and adds the obtained distances to obtain the movement amount of the target grid.
As one of the embodiments, the robot calculating the movement calculation amount includes the steps of: the robot obtains the absolute value of the difference value between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point; the robot obtains the absolute value of the difference value between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point; the robot uses the sum of the absolute value of the difference between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point and the absolute value of the difference between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point as the movement calculation amount. As shown in fig. 3, the movement calculation amount is the sum of absolute wheelbases of the target grid and the obstacle avoidance end point on a standard coordinate system, and the method is a manhattan distance calculation method.
In step S2, the robot acquires point cloud data of the obstacle through the line laser sensor, acquires the obstacle avoidance point based on the point cloud data of the obstacle, selects the obstacle avoidance point through the obstacle avoidance target point, and moves to the first obstacle avoidance target point (i.e., the position ① in fig. 2) based on the selected obstacle avoidance point. When the robot acquires the point cloud data of the obstacle through the line laser sensor, the obstacle coordinates of each point on the obstacle on the robot coordinates are determined according to the emission time, the return time and the arrangement sequence of each laser point. Then the robot processes the point cloud data, a plurality of obstacle avoidance points are selected from the point cloud data, one obstacle avoidance point is selected as an obstacle avoidance direction according to the position relation between the obstacle avoidance points and the obstacle avoidance target point, and then the robot moves to the nearest obstacle avoidance target point.
As one of the embodiments, in step S2, the robot acquires the obstacle detouring point based on the point cloud data of the obstacle, including the steps of: the robot converts the point cloud data of the obstacle obtained by the line laser sensor into the obstacle coordinate, and the robot can do so by a laser conversion formula, which is an existing method and is not described herein. The robot fits the obstacle coordinates obtained by the autorotation into a point cloud line segment through a least square method according to the obstacle coordinates; the robot sets two end points of the point cloud line segment as obstacle detouring points, and the midpoint of the voltage line segment can be used as an obstacle detouring point. The least squares method (also known as least squares) is a mathematical optimization technique. It finds the best functional match for the data by minimizing the sum of squares of the errors. The unknown data can be easily obtained by the least square method, and the sum of squares of errors between the obtained data and the actual data is minimized. The main calculation step is that the average value of the abscissa and the ordinate of the obtained obstacle coordinate is calculated respectively; then, carrying out summation calculation on the abscissa of the acquired obstacle coordinate and summation calculation on the ordinate of the acquired obstacle coordinate; calculating the square of the abscissa of each obstacle coordinate, then summing, and calculating the product of the abscissa of each obstacle coordinate multiplied by the ordinate, then summing; according to the obtained data, solving two parameters of intercept a and slope b in a linear equation to obtain the linear equation: y=ax+b. The robot determines which points are used for fitting straight line calculation according to the position relation of the coordinate points.
As one embodiment, in step S2, the robot selects an obstacle avoidance point through the obstacle avoidance target point, and moves to the first obstacle avoidance target point (i.e. the position ① in fig. 2) based on the selected obstacle avoidance point, including the following steps: the robot selects an obstacle avoidance point closest to the obstacle avoidance target point, and then the robot reaches the first obstacle avoidance target point by moving around the selected obstacle avoidance point. After the robot selects the obstacle detouring point, the robot moves along the obstacle detouring point to reach the first obstacle avoidance target point (i.e. the position ① in fig. 2), and the point cloud data located on the same side of the selected obstacle detouring point can also be used as a reference for the movement along the edge.
As one example, after reaching the first obstacle avoidance target point, the robot adjusts the angle by autorotation, so that the robot faces the next obstacle avoidance target point (i.e. the position ② in fig. 2), and then obtains the point cloud data of the obstacle through the line laser sensor. The robot moves around the obstacle-surrounding point to go to the first obstacle-avoiding target point, and the direction of the robot and the obstacle are adopted, so that the robot does not need to adjust the angle of the robot, and the point cloud data of the obstacle can be directly acquired. The robot can acquire the point cloud data of the obstacle through adjusting the angle of the robot, so that the accuracy of the data can be improved.
Step S3: the robot acquires point cloud data of the obstacle through the line laser sensor again at the obstacle avoidance target point ①, acquires a new obstacle detouring point based on the point cloud data of the obstacle acquired again, namely, performs point cloud data fitting through a least square method according to the point cloud data of the obstacle acquired newly to obtain a new point cloud line segment, and takes two end points of the point cloud line segment as new obstacle detouring points again. And selecting an obstacle detouring point according to the next obstacle detouring target point ②, and selecting the obstacle detouring point closest to the obstacle detouring target point ② from the two obstacle detouring points as the direction of the obstacle detouring. The robot then moves from the obstacle avoidance target ① to the obstacle avoidance target ② based on the new obstacle avoidance point to the next obstacle avoidance target ②, i.e., based on the selected obstacle avoidance point.
Step S4: the robot acquires the point cloud data of the obstacle again through the line laser sensor at the position of the obstacle avoidance target point ②, and moves to the position of the obstacle avoidance target point ③ through the method. And so on, the robot keeps away the obstacle through the mode, and the robot sequentially passes through the obstacle avoidance target points ④、⑤、⑥、⑦、⑧ and ⑨ until the robot stops keeping away the obstacle and continues to operate after judging that the robot bypasses the obstacle.
In step S4, if the robot reaches the obstacle avoidance end point, i.e. the position ⑩ in the figure, it is determined that the robot bypasses the obstacle, and the robot continues to operate according to the planned route.
In step S4, if the robot cannot acquire the point cloud data of the obstacle through the line laser sensor at the obstacle avoidance target point ⑦, the robot determines that the robot bypasses the obstacle and continues to operate according to the planned route.
A chip for storing a program configured to perform the line laser based SLAM navigation obstacle avoidance method described above.
The robot comprises a main control chip and a line laser sensor, wherein the main control chip is the chip.
As one example, as shown in fig. 4, the line laser sensor is disposed at the front end of the robot, and the detection distance of the line laser sensor is 10cm, and the detection range is 170 °.
Compared with the prior art, the application has the beneficial effects that: the robot enters an obstacle avoidance mode after encountering an obstacle, acquires point cloud data of the obstacle for many times through line laser to calculate outline points of the obstacle, accurately bypasses the obstacle according to the calculated outline points of the obstacle, improves navigation efficiency, and has no collision with dynamic and low obstacles in the navigation process.
In the description of the present invention, a description of the terms "one embodiment," "preferred," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention, and a schematic representation of the terms described above in the present specification does not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The connection modes in the description of the specification have obvious effects and practical effectiveness.
From the above description of the structure and principles, it should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, but rather that modifications and substitutions using known techniques in the art on the basis of the present invention fall within the scope of the present invention, which is defined by the appended claims.
Claims (12)
1. A SLAM navigation obstacle avoidance method based on line laser is characterized by comprising the following steps:
S1: when the robot encounters an obstacle in the operation process according to the planned route, the robot performs obstacle avoidance path planning to acquire an obstacle avoidance target point;
S2: the robot acquires point cloud data of an obstacle through a line laser sensor, acquires an obstacle avoidance point based on the point cloud data of the obstacle, selects the obstacle avoidance point through an obstacle avoidance target point, and moves to a first obstacle avoidance target point based on the selected obstacle avoidance point;
s3: the robot acquires point cloud data of the obstacle through the line laser sensor again at the obstacle avoidance target point, acquires a new obstacle avoidance point based on the acquired point cloud data of the obstacle again, selects the obstacle avoidance point according to the next obstacle avoidance target point, and moves to the next obstacle avoidance target point according to the new obstacle avoidance point;
s4: the robot acquires point cloud data of the obstacle through the line laser sensor again, and so on, the robot keeps away the obstacle through the mode, and the robot stops keeping away the obstacle and continues to operate until the robot judges that the robot bypasses the obstacle.
2. The line laser-based SLAM navigation obstacle avoidance method of claim 1, wherein in step S1, the robot performs obstacle avoidance path planning to obtain an obstacle avoidance target point, comprising the steps of:
the robot converts the walking map into a grid map, and sets a movement amount and a movement calculation amount for each grid;
The robot takes the current grid as a starting point, searches for a grid adjacent to the starting point, sets the starting point as a father node of the adjacent grid, puts the father node into a closed list, and puts the grid which is not occupied by the barrier in the grid adjacent to the starting point into an open list;
The robot takes each grid in the open list as a target grid, calculates the movement amount and the movement calculated amount of the target grid, selects the target grid with the minimum sum value of the movement amount and the movement calculated amount, searches for grids adjacent to the selected target grid, sets the selected target grid as a father node of the adjacent grid, moves the father node from the open list to the closed list, and places the grids which are not occupied by the barrier and are not added into the open list in the grids adjacent to the selected target grid;
The robot takes each grid in the open list as a target grid, calculates the movement amount and the movement calculated amount of the target grid, selects the target grid with the minimum sum of the movement amount and the movement calculated amount, and so on, and places the adjacent grids of the selected target grid in the open list in the mode until the grid where the obstacle avoidance terminal is positioned is placed in the open list, and takes a father node connected with the obstacle avoidance terminal and the starting point as an obstacle avoidance target point;
The moving amount is the moving cost of the robot from the starting point to the target grid, and the moving calculated amount is the calculating cost of the robot from the target grid to the obstacle avoidance end point.
3. The line laser based SLAM navigation obstacle avoidance method of claim 2 wherein the robot calculating the amount of movement comprises the steps of:
the robot acquires a moving track based on the parent node moving from the starting point to the target grid;
The robot obtains the distance between adjacent grids on the moving track;
the robot acquires a sum of distances between adjacent grids as a movement amount.
4. The line laser based SLAM navigation obstacle avoidance method of claim 2 wherein the robot calculating the movement calculation amount comprises the steps of:
the robot obtains the absolute value of the difference value between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point;
The robot obtains the absolute value of the difference value between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point;
The robot uses the sum of the absolute value of the difference between the x coordinate of the target grid and the x coordinate of the obstacle avoidance end point and the absolute value of the difference between the y coordinate of the target grid and the y coordinate of the obstacle avoidance end point as the movement calculation amount.
5. The line laser based SLAM navigation obstacle avoidance method of claim 4 wherein in step S4, if the robot reaches the obstacle avoidance endpoint, it is determined to bypass the obstacle and continue the operation along the planned route.
6. The line laser-based SLAM navigation obstacle avoidance method of claim 1, wherein in step S2, the robot acquires obstacle avoidance points based on point cloud data of the obstacle, comprising the steps of:
the robot converts point cloud data of the obstacle obtained through the line laser sensor into an obstacle coordinate;
the robot fits the obstacle coordinates obtained by the autorotation into a point cloud line segment through a least square method according to the obstacle coordinates;
The robot sets two end points of the point cloud line segment as obstacle detouring points.
7. The SLAM navigation obstacle avoidance method of claim 6, wherein in step S2, the robot selects an obstacle avoidance point by the obstacle avoidance target point and moves to the first obstacle avoidance target point based on the selected obstacle avoidance point, comprising the steps of:
the robot selects an obstacle avoidance point closest to the obstacle avoidance target point, and then the robot reaches the first obstacle avoidance target point by moving around the selected obstacle avoidance point.
8. The line laser based SLAM navigation obstacle avoidance method of claim 7 wherein the robot adjusts the angle by autorotation after reaching a first obstacle avoidance target point to bring itself into direct opposition to the next obstacle avoidance target point, and then obtains point cloud data of the obstacle via the line laser sensor.
9. The line laser-based SLAM navigation obstacle avoidance method of claim 1, wherein in step S4, if the robot cannot obtain the point cloud data of the obstacle through the line laser sensor at the obstacle avoidance target point, the robot determines that the robot bypasses the obstacle and continues to operate according to the planned route.
10. A chip for storing a program, characterized in that the program is configured to execute the line laser-based SLAM navigation obstacle avoidance method of any one of claims 1 to 9.
11. A robot comprising a master control chip and a line laser sensor, wherein the master control chip is the chip of claim 10.
12. The robot of claim 11, wherein the line laser sensor is disposed at a front end of the robot, and the detection distance of the line laser sensor is 10cm and the detection range is 170 °.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211270151.XA CN117991758A (en) | 2022-10-18 | 2022-10-18 | SLAM navigation obstacle avoidance method based on line laser, chip and robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211270151.XA CN117991758A (en) | 2022-10-18 | 2022-10-18 | SLAM navigation obstacle avoidance method based on line laser, chip and robot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117991758A true CN117991758A (en) | 2024-05-07 |
Family
ID=90892993
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211270151.XA Pending CN117991758A (en) | 2022-10-18 | 2022-10-18 | SLAM navigation obstacle avoidance method based on line laser, chip and robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117991758A (en) |
-
2022
- 2022-10-18 CN CN202211270151.XA patent/CN117991758A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3764186A1 (en) | Method for controlling autonomous mobile robot to travel along edge | |
| CN113110457B (en) | Autonomous coverage inspection method for intelligent robot in indoor complex dynamic environment | |
| US20200284596A1 (en) | Route planning method for mobile vehicle | |
| Vasiljević et al. | High-accuracy vehicle localization for autonomous warehousing | |
| Castellanos et al. | Building a global map of the environment of a mobile robot: The importance of correlations | |
| KR101372482B1 (en) | Method and apparatus of path planning for a mobile robot | |
| KR101179075B1 (en) | Path planning method for autonomous robot and path planning system thereof | |
| CN108415432B (en) | Straight edge-based positioning method for robot | |
| CN113189977B (en) | Intelligent navigation path planning system and method for robot | |
| CN103926925A (en) | Improved VFH algorithm-based positioning and obstacle avoidance method and robot | |
| Arul et al. | LSwarm: Efficient collision avoidance for large swarms with coverage constraints in complex urban scenes | |
| CN104714551A (en) | Indoor area covering method suitable for vehicle type mobile robot | |
| CN109798901B (en) | Robot for files and navigation positioning system and navigation positioning method thereof | |
| Karkowski et al. | Real-time footstep planning using a geometric approach | |
| KR20140145033A (en) | Device for searching area and mapping for path of intelligent robot in unknown environments | |
| CN113359769A (en) | Indoor autonomous mobile robot composite navigation method and device | |
| CN112857370A (en) | Robot map-free navigation method based on time sequence information modeling | |
| Bayer et al. | Decentralized topological mapping for multi-robot autonomous exploration under low-bandwidth communication | |
| CN112828883B (en) | Robot environment exploration method and system in unknown environment | |
| CN117991758A (en) | SLAM navigation obstacle avoidance method based on line laser, chip and robot | |
| Song et al. | SLAM based shape adaptive coverage control using autonomous vehicles | |
| CN116520819A (en) | Indoor path planning method and system based on direct connection search | |
| KR101297608B1 (en) | Method and system for robot coverage of unknown environment | |
| CN113048978A (en) | Mobile robot repositioning method and mobile robot | |
| CN111923050A (en) | Mobile robot mapping method in large scene |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |