CN110332943B - Method for planning full-coverage path of robot with rapid traversal - Google Patents
Method for planning full-coverage path of robot with rapid traversal Download PDFInfo
- Publication number
- CN110332943B CN110332943B CN201910596968.8A CN201910596968A CN110332943B CN 110332943 B CN110332943 B CN 110332943B CN 201910596968 A CN201910596968 A CN 201910596968A CN 110332943 B CN110332943 B CN 110332943B
- Authority
- CN
- China
- Prior art keywords
- grid map
- grid
- robot
- area
- longest side
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a method for planning a full-coverage path of a robot with rapid traversal, which comprises the steps of firstly estimating the current pose of the robot through a Monte Carlo positioning algorithm and constructing a grid map by utilizing an inversion measurement model; dividing the area of the grid map based on the direction of the longest side of the grid map and each vertex of the grid map; calculating cost values from each grid of the grid map to the starting point to determine a cleaning area; and determining a cleaning direction according to the longest side of the grid map so as to plan a full coverage path. The invention can divide the concave polygon into the convex polygon so as to facilitate planning of the arched full coverage path, simultaneously reduces the rotation times of the cleaning robot, greatly reduces the traversing time and improves the cleaning efficiency.
Description
Technical Field
The invention relates to the technical field of mobile robots, in particular to a cleaning robot full-coverage path planning method.
Background
Robots were born in the 60 s of the 20 th century due to industrial development, and with the development of society and the progress of technology, the functions of robots became diversified, and robots are becoming popular with people at present. The indoor cleaning robot is a household service robot, can replace manual work to reduce labor burden, and has become a hot spot for intelligent robot research, wherein a path planning technology is one of core problems in the field of intelligent robots.
The main research content of the robot path planning technology comprises point-to-point path planning and full-coverage path planning. The full coverage path planning refers to searching a continuous path passing through all reachable points in a set area on the premise of meeting the optimal or quasi-optimal performance index.
At present, the method for generating the full-coverage path of the sweeping robot mainly comprises random sweeping, combination of an arched path and random sweeping and planning of the full-coverage path based on region segmentation. The random sweeping and the combination of the arched path and the random sweeping have strong randomness, the coverage rate is related to the sweeping time, the efficiency is low, and the time is long; the prior area segmentation technology applied to the indoor cleaning robot mainly comprises a template matching method and a pit segmentation method, wherein the area generated by the template matching method cannot be completely matched with an actual environment map, so that the cleaning efficiency is low, and the obtained cleaning map is not attractive; the pit segmentation method does not consider the segmentation direction of the region, the segmented region can cause the increase of the rotation times of the robot, the longer total cleaning path, and the cleaning efficiency of the cleaning robot is reduced.
Disclosure of Invention
The invention provides a rapid traversal robot full-coverage path planning method, and aims to solve the problem of low full-coverage cleaning efficiency of the existing sweeping robot.
The technical scheme adopted for solving the technical problems is as follows:
the wall-following sensor is used for controlling the cleaning robot to walk along the wall, and meanwhile, the ranging sensor and encoder information are fused to construct a room grid map;
traversing the grid map to obtain all the coordinates of the sides and the vertexes of the grid map, and solving the direction of the longest side;
according to the direction of the longest side of the grid map and the vertex coordinates, carrying out region segmentation on the grid map, and calculating the cost value from each grid to the initial position of the cleaning robot, thereby determining a final cleaning region;
and (3) carrying out arc path planning in the partitioned areas, solving the shortest path from the previous area to the next area after the partitioning of one area is completed, and continuing to carry out arc path planning until all the reachable areas are traversed.
The beneficial effects of the invention are as follows: the cleaning robot is based on the construction of the grid map, performs region segmentation on the irregular polygon grid map in the direction of the longest side to obtain the convex polygon, greatly reduces the rotation times and the total path length of the cleaning robot, accelerates the full traversing speed, improves the coverage rate of the cleaning robot through the region segmentation technology, avoids the conditions of re-sweeping and missed-sweeping, and improves the cleaning efficiency.
Drawings
FIG. 1 is a schematic overall flow diagram of a full coverage path planning method of the present invention;
FIG. 2 is a schematic diagram of an inversion measurement model construction grid map of the present invention;
FIG. 3 is a schematic diagram of an edge binary image obtained by processing a grid map according to the present invention;
FIG. 4 is a schematic view of the present invention in area segmentation;
FIG. 5 is a schematic diagram of an arcuate full coverage path planning process;
Detailed Description
In order to better understand the technical scheme of the present invention, the following description of the embodiments of the present invention is further provided with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flow chart of a fast traversal robot full coverage path planning method according to the present invention, and the specific process is as follows:
s1, firstly, initializing a grid map by taking a cleaning robot charging pile as a starting point, and dividing a space into a plurality of limited grid units by using the grid map:
m＝m i
wherein m is i Represents the ith grid element, each m i Corresponding to a three-valued occupancy variable indicating whether the unit is occupied. If the grid is occupied with a "1", unoccupied with a "0", the unknown region (or unscanned region) is "-1". p (m) i =1) or p (m i ) Representing the likelihood that the grid is occupied, initializing each p (m i ) The initial value of (2) is 0.5.
The essence of building an indoor grid map is to use odometer information u 1:t Distance sensor information z 1:t To calculate the pose x of each moment of the robot 0:t And an environment map m, i.e. p (x) 0:t ,m|z 1:t ,u 1:t ) And (3) carrying out solving:
p(x 0:t ,m|z 1:t ,u 1:t )＝p(x 0:t |z 1:t ,u 1:t )p(m|x 1:t ,z 1:t )
decomposing the constructed grid map into pose x by using the formula 0:t I.e. estimation of robot trajectory p (x 0:t |z 1:t ,u 1:t ) Multiplying an estimate p (m|x) of a map based on pose 1:t ,z 1:t ) Two parts. And estimating the motion trail of the robot by using a Monte Carlo algorithm, and updating the initialized grid map by combining a binary Bayesian filtering algorithm with an inversion measurement model. The inversion measurement model is shown in fig. 2: when a certain grid unit is out of the measuring range of the sensor beam or the grid unit is positioned at the measuring distance
When the back exceeds a certain threshold value alpha/2, the unknown occupancy probability l in the logarithmic form is returned t-1,i The method comprises the steps of carrying out a first treatment on the surface of the If the grid cell is measuring distance +>
Within + -alpha/2 of the range, the occupancy probability l in logarithmic form is returned occ The method comprises the steps of carrying out a first treatment on the surface of the If the distance of the cell is greater than the measured distance>
Short alpha/2 more, return to the log form of the idle probability l free . The log occupancy probability expression is formulated as follows:
for each measurement update, the occupancy probability update formula for the grid is:
l t,i ＝l t-1,i +inverse_sersor_model(m i ,x t ,z t )-l 0
wherein, the inversion measurement model index_sensor_model(m i ,x t ,z t ) The calculation formula is as follows:
thus, the above algorithm is employed to implement the construction of a room grid map by walking around the indoor circumference along wall sensors or otherwise.
S2, obtaining a longest side direction angle theta from the grid map generated by the steps, wherein the reference method comprises the following steps of:
(1) Graying the obtained room grid map by adopting an opencv function cvtdcolor (Image, grayImage, cv_bgr2gray);
(2) Invoking threshold (gray image, dst,0,255, cv_thresh_otsu) to binarize the gray image, and obtaining an edge binary image, as shown in fig. 3;
(3) Carrying out standard Hough transformation on the edge binary image to obtain all line segments in the grid map;
(4) Traversing the obtained line segment to obtain a longest side direction angle theta;
s3, carrying out region segmentation on the grid map by the direction angle theta obtained by solving in the step S2, wherein the reference method comprises the following steps of:
(1) As shown in FIG. 4, after the step S2, the longest side is l 1 ；
(2) Traversing each vertex of the grid map according to the longest side direction angle theta, and taking the longest side l as each vertex 1 Is a parallel line of (2);
(3) Intersecting parallel lines made by each vertex with peripheral contour lines of the grid map and barrier contour lines, removing straight line parts passing through the barrier, and determining a segmentation area, as shown in fig. 4;
s4, calculating cost values cost from each grid to the starting point position so as to determine a cleanable area, wherein the reference method comprises the following steps:
(1) Initializing each grid cost value to be-1, which means an unreachable area;
(2) The starting point cost value is 0, and the starting point coordinates are pressed into a queue;
(3) Judging the state of the four adjacent domains of the first grid of the queue, and if the state is an idle area and the queue is not pressed in, adding one to the first grid cost value of the queue: cost i ＝cost i-1 +1, and pushing the grid coordinates into the queue;
(4) The first queue is dequeued;
(5) Circularly executing the steps (3) - (4) until the queue is empty;
(6) All grids with cost values of non-1 are the areas to be cleaned;
s5, traversing the area to be cleaned in the segmented area in an arc-shaped path in sequence, wherein the reference method comprises the following steps of:
(1) Starting from a starting point, starting to clean by taking the longest side direction as a cleaning direction, rotating by 90 degrees when encountering an obstacle, walking a distance of the width of the robot in parallel, and then rotating by 90 degrees to clean in the opposite direction;
(2) Repeating the cleaning process in the previous area until the previous area is cleaned;
(3) A shortest path from the end point of the previous area to the start point of the next area is obtained by utilizing a shortest path algorithm, and the cleaning robot moves to the next area along the shortest path;
(4) The above processes (1) - (2) - (3) are repeated until all areas have been traversed, and the planning process is shown in fig. 5 below.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. A method for planning a full-coverage path of a robot with quick traversal is characterized by comprising the following steps:
estimating the current gesture by using a Monte Carlo algorithm, and constructing a grid map by using an inversion measurement model to fuse distance sensor information;
detecting all vertexes in the grid map and traversing all sides of the grid map according to the constructed grid map to obtain the longest side and the direction of the longest side of the grid map;
step three, carrying out region segmentation on the grid map according to the obtained longest side direction, and calculating the cost value from each grid to the starting point to obtain a region to be cleaned;
step four, planning an arched full-coverage path in each area according to the obtained areas to be cleaned until all the areas are cleaned;
performing binarization processing on the grid map to obtain a binary image, obtaining all sides of the grid map through standard Hough transformation, traversing all sides, and obtaining the direction of the longest side;
step three, according to the direction of the longest side, each vertex in the grid map is crossed with a longest side parallel line, the longest side parallel line is intersected with a peripheral contour line of the grid map and an obstacle contour line, a straight line part passing through the obstacle is removed, and the grid map is subjected to region segmentation;
in the fourth step, traversing the area to be cleaned in the segmentation area sequentially in an arc-shaped path, wherein the method concretely comprises the following steps:
(1) Starting from a starting point, starting to clean by taking the longest side direction as a cleaning direction, rotating by 90 degrees when encountering an obstacle, walking a distance of the width of the robot in parallel, and then rotating by 90 degrees to clean in the opposite direction;
(2) Repeating the cleaning process in the previous area until the previous area is cleaned;
(3) A shortest path from the end point of the previous area to the start point of the next area is obtained by utilizing a shortest path algorithm, and the cleaning robot moves to the next area along the shortest path;
repeating the processes (1) - (2) - (3) until all the areas are traversed.
2. The method for planning a fully covered path of a robot according to claim 1, wherein in the third step, all grid cost values are initialized to be-1, the starting point cost value is assigned to be 0, and the cost value from each grid to the starting point position is calculated according to the four-neighborhood grid state of each grid point, so that the final cleaning area is determined.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910596968.8A CN110332943B (en) | 2019-07-03 | 2019-07-03 | Method for planning full-coverage path of robot with rapid traversal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910596968.8A CN110332943B (en) | 2019-07-03 | 2019-07-03 | Method for planning full-coverage path of robot with rapid traversal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110332943A CN110332943A (en) | 2019-10-15 |
| CN110332943B true CN110332943B (en) | 2023-05-09 |
Family
ID=68144120
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910596968.8A Active CN110332943B (en) | 2019-07-03 | 2019-07-03 | Method for planning full-coverage path of robot with rapid traversal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110332943B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112817269A (en) * | 2019-11-15 | 2021-05-18 | 深圳拓邦股份有限公司 | Traversal operation method and system and robot equipment |
| CN111307156B (en) * | 2020-03-09 | 2023-05-16 | 中振同辂(江苏)机器人有限公司 | Coverage path planning method suitable for vehicle type robot |
| CN111329398A (en) * | 2020-03-27 | 2020-06-26 | 上海高仙自动化科技发展有限公司 | Robot control method, robot, electronic device, and readable storage medium |
| CN113552866B (en) * | 2020-04-17 | 2023-05-05 | 苏州科瓴精密机械科技有限公司 | Method, system, robot and readable storage medium for improving traversal equilibrium performance |
| CN111652462B (en) * | 2020-04-17 | 2023-08-15 | 湘潭大学 | Agricultural land heavy metal pollution and potential ecological risk evaluation method |
| CN111681250B (en) * | 2020-05-16 | 2023-04-07 | 珠海一微半导体股份有限公司 | Segmentation method based on laser grid map |
| CN112161629B (en) * | 2020-09-30 | 2022-11-29 | 深圳银星智能集团股份有限公司 | Path planning method and device for cleaning robot, cleaning robot and medium |
| CN112985405B (en) * | 2021-02-18 | 2023-06-09 | 湖南国科微电子股份有限公司 | Robot full-coverage path planning method, device, equipment and medium |
| CN113064432A (en) * | 2021-03-22 | 2021-07-02 | 深圳市商汤科技有限公司 | Path covering method and device, electronic equipment and storage medium |
| CN113219993A (en) * | 2021-06-04 | 2021-08-06 | 深圳飞鼠动力科技有限公司 | Path planning method and cleaning robot |
| CN113219992A (en) * | 2021-06-04 | 2021-08-06 | 深圳飞鼠动力科技有限公司 | Path planning method and cleaning robot |
| CN113791625A (en) * | 2021-09-30 | 2021-12-14 | 深圳市优必选科技股份有限公司 | Full coverage path generation method and device, terminal equipment and storage medium |
| CN114323047A (en) * | 2021-12-31 | 2022-04-12 | 北京工业大学 | Full-coverage path planning algorithm based on polygon decomposition |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104714551A (en) * | 2015-03-23 | 2015-06-17 | 中国科学技术大学 | Indoor area covering method suitable for vehicle type mobile robot |
| CN108151716A (en) * | 2017-11-09 | 2018-06-12 | 广州极飞科技有限公司 | Flight instruments mapping operating area planing method, device and terminal |
| CN108896048A (en) * | 2018-06-01 | 2018-11-27 | 浙江亚特电器有限公司 | Paths planning method for mobile carrier |
| CN109947114A (en) * | 2019-04-12 | 2019-06-28 | 南京华捷艾米软件科技有限公司 | Robot complete coverage path planning method, device and equipment based on grating map |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100791386B1 (en) * | 2006-08-18 | 2008-01-07 | 삼성전자주식회사 | Method and system of cell decomposition in mobile robot |
-
2019
- 2019-07-03 CN CN201910596968.8A patent/CN110332943B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104714551A (en) * | 2015-03-23 | 2015-06-17 | 中国科学技术大学 | Indoor area covering method suitable for vehicle type mobile robot |
| CN108151716A (en) * | 2017-11-09 | 2018-06-12 | 广州极飞科技有限公司 | Flight instruments mapping operating area planing method, device and terminal |
| CN108896048A (en) * | 2018-06-01 | 2018-11-27 | 浙江亚特电器有限公司 | Paths planning method for mobile carrier |
| CN109947114A (en) * | 2019-04-12 | 2019-06-28 | 南京华捷艾米软件科技有限公司 | Robot complete coverage path planning method, device and equipment based on grating map |
Non-Patent Citations (2)
| Title |
|---|
| "2D Occupancy Grid Mapping With Inverse Range Sensor Model";Saif Eddine Hadji 等,;《Control Conference (ASCC), 2015 10th Asian IEEE》;20150910;1-6页 * |
| "Automatic operation planning for GPS-guided machinery";Stall A，;《Proceedings of 4th European Conference on Precision Agriculture．Berlin: Germany，2003》;20031231;657-664页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110332943A (en) | 2019-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110332943B (en) | Method for planning full-coverage path of robot with rapid traversal | |
| CN110398964B (en) | Low-energy-loss robot full-coverage path planning method and system | |
| CN107340768B (en) | Path planning method of intelligent robot | |
| CN107990903B (en) | Indoor AGV path planning method based on improved A-x algorithm | |
| CN110531760B (en) | Boundary exploration autonomous mapping method based on curve fitting and target point neighborhood planning | |
| CN105320140B (en) | A kind of sweeping robot and its clean paths planning method | |
| CN108664022B (en) | Robot path planning method and system based on topological map | |
| CN105717929B (en) | Mobile robot mixed path planing method under a kind of multiresolution obstacle environment | |
| CN107065865A (en) | A kind of paths planning method based on the quick random search tree algorithm of beta pruning | |
| CN109947114A (en) | Robot complete coverage path planning method, device and equipment based on grating map | |
| KR100791386B1 (en) | Method and system of cell decomposition in mobile robot | |
| CN111610786B (en) | Mobile robot path planning method based on improved RRT algorithm | |
| CN109945873A (en) | A kind of mixed path planing method for indoor mobile robot motion control | |
| CN110464262B (en) | Method for getting rid of difficulties of sweeping robot | |
| CN106970623A (en) | Intelligent cleaning device and its trellis paths operational method | |
| CN107357295B (en) | Path searching method and chip based on grid map and robot | |
| US10037027B2 (en) | System and method for determining an energy-efficient path of an autonomous device | |
| Yakovlev et al. | Grid-based angle-constrained path planning | |
| CN114161416B (en) | Robot path planning method based on potential function | |
| CN110579213A (en) | sweeping robot sweeping path planning navigation algorithm applied to low-cost chip | |
| CN114895691B (en) | Path planning method and device of swimming pool cleaning robot | |
| CN108917769A (en) | A kind of adaptive grating map creating method of robot based on nine fork trees | |
| CN113031616A (en) | Cleaning robot return path planning method and system and cleaning robot | |
| CN112237403B (en) | Covering path generation method for cleaning device and cleaning device | |
| CN114119804A (en) | Plane model bounding polygon generation method and device and furniture contour identification method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |