CN108319270B - Automatic dust collection robot optimal path planning method based on historical data analysis - Google Patents
Automatic dust collection robot optimal path planning method based on historical data analysis Download PDFInfo
- Publication number
- CN108319270B CN108319270B CN201810228764.4A CN201810228764A CN108319270B CN 108319270 B CN108319270 B CN 108319270B CN 201810228764 A CN201810228764 A CN 201810228764A CN 108319270 B CN108319270 B CN 108319270B
- Authority
- CN
- China
- Prior art keywords
- data
- point
- line segment
- data link
- link list
- 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
- 239000000428 dust Substances 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000007405 data analysis Methods 0.000 title claims description 6
- 238000004140 cleaning Methods 0.000 claims abstract description 18
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 230000033001 locomotion Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
-
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
Abstract
The automatic dust collection robot comprises two driving wheels and two driving motors connected with the driving wheels, wherein encoders are mounted on the driving motors so as to be mounted on an obstacle detection device at the front part of the automatic dust collection robot, the driving motors, the encoders and the obstacle detection device are connected with a controller, the controller is internally provided with the optimal path planning method, and the optimal path planning method comprises five steps: (1) setting a data link list L0(ii) a (2) When the automatic dust collection robot detects the obstacle, the current position coordinate is recorded and stored in a data linked list L0(ii) a (3) Obtaining a data link list L0Center point O and contour line segment S ofj(m, n) and stores a data link list L1(ii) a (4) Reserved data link list L1A contour line segment without any position point between the middle point and the central point O; (5) calculating a data chain table L1And as the next cleaning direction.
Description
Technical Field
The invention relates to an automatic dust collection robot optimal path planning method based on historical data analysis, and belongs to the technical field of intelligent household appliance control.
Background
With the acceleration of the life rhythm of people and the requirement of more and more abundant life contents, the intelligent household appliances are promoted to advance our lives. Among them, the automatic dust collection robot is greatly helpful. The cleaning work at home is very heavy and frequent. The automatic dust collection robot can automatically clean the household floor. The automatic dust collection robot utilizes a self-carried rechargeable battery to supply power to various electrical appliances, wherein a dust collection motor forms enough vacuum in the automatic dust collection robot, garbage on the ground is sucked into an inner dust box through a strip-shaped suction port, and the automatic dust collection robot can freely walk by a driving motor and a driving wheel. The automatic dust collection robot realizes the cleaning of the ground through the self walking process.
Because the existing automatic dust-collecting robot does not have very precise positioning and planning capability, the efficiency of cleaning the path becomes a very urgent problem to be solved. The current common strategy is a random path, the automatic dust collection robot walks randomly on the ground, and any planning method is abandoned, so that the strategy causes low cleaning efficiency. In order to control the speed, the automatic dust collection robot is provided with an encoder on a driving motor, and can calculate the relative movement distance and the rotation angle so as to realize position calculation. Then, the situation of the cleaning path can be analyzed from the position data recorded by the automatic dust collection robot in the near term, so that a basis is provided for the next optimal cleaning path planning.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and adopts the analysis of historical position data to obtain the direction which is most probably not cleaned, thereby obtaining the optimal cleaning path without increasing any hardware cost.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an optimal path planning method of an automatic dust collection robot based on historical data analysis is disclosed, the automatic dust collection robot comprises two driving wheels, two driving motors connected with the driving wheels, encoders mounted on the driving motors, a supporting wheel and an obstacle detection device mounted in front of the automatic dust collection robot, the driving motors, the encoders and the obstacle detection device are connected with a controller, the controller realizes free movement of the automatic dust collection robot by setting the speed and the direction of the two driving wheels respectively, and can calculate the relative movement distance and the rotation direction of the automatic dust collection robot according to signals of the encoders, the coordinates (x, y) of the current position can be calculated by taking the initial position as an origin, and the optimal path planning method is arranged in the controller, the optimal path planning method comprises the following steps:
(1) setting a data link list L0={Pi(xi,yi) N-1, x, wherein i =0, 1, 2iAnd yiIs a coordinate value, N is a data link list L0Length of (2), data link list L0Coordinate data of a stop position after detecting an obstacle for the automatic dust collection robot in the near term;
(2) the automatic dust collection robot moves forwards in a linear motion mode and continuously detects obstacles; when the obstacle is detected, the automatic dust collection robot stops, records the coordinates (x, y) of the current position, and stores the coordinates into a data linked list L0Then entering step 3;
(3) and obtaining the data chain table L0Center point of (x)o，yo) Extracting the data chain table L by taking the central point O as the center0Contour line segment S in (1)j(m, n) and stores a data link list L1Wherein M =0, 1, 2<N, the contour line segment Sj(m, n) represents a data link list L0Point P inm(xm,ym) And Pn(xn,yn) A line segment;
(4) a central point O and a data chain table L1Contour line segment S in (1)j(m, n) form a triangular region, calculate OPmDirection angle theta1=
，OPnDirection angle theta2=
And a data link list L0Point P ini(xi,yi) Andthe center point O forms a direction angle theta3=
If for the data link list L0All points P ini(xi,yi)，
At the same time
If all the points are true, no point appears in the triangular area, indicating the contour line segment SjIf the direction corresponding to (m, n) is not cleared, then the data link list L1The contour line segment S is reservedj(m, n); on the contrary, in the data link list L1Deleting contour line segment Sj(m,n)；
(5) Calculating a data chain table L1Contour line segment S in (1)jLength W of (m, n)j=
Comparing the contour line segment SjLength W of (m, n)jThe size of (1) is determined by taking the contour line segment S with the maximum lengthmax(m, n), contour line segment SmaxDirection angle α = (m, n) with respect to center point O
(ii) a The automatic dust collection robot selects a direction angle alpha as the next cleaning direction.
In step 2, the coordinates (x, y) of the current position are stored in a data link list L0The method comprises the following steps:
let Pi(xi,yi)=Pi-1(xi-1,yi-1) ，i=1,2,3.....N-1;
Then P0(x0,y0) = (x, y), link table operation is completed.
In step 3, the data link list L0Center point of (x)o，yo) The coordinate calculation method comprises the following steps:
searching data link list L0Maximum and minimum values of the middle coordinate data: x is the number ofmax，xmin，ymax，ymin；
Calculating xo=
，yo=
。
In step 3, the contour line segment S in the data link list L0 is extracted with the center point O as the centerj(m, n) using the following steps:
calculating a linked list of data L0Point P ini(xi,yi) And a center point O (x)o，yo) Distance D ofi=
Finding the point P with the largest distanceM(xM,yM)；
With PM(xM,yM) For the vertex, calculate the data link list L0Point P ini(xi,yi) And a center point O (x)o，yo) Angle formed beta =
Taking the point with the maximum angle beta as a contour point and marking as PN(xN,yN) Thus forming data S0(M, N) and storing a data link list L1；
Then with PN(xN,yN) Continuously searching contour points for the vertex and storing the contour points into a data chain table L1Up to the return point PM(xM,yM) And ending and returning.
The implementation of the invention has the positive effects that: 1. an optimal cleaning path is selected, and the cleaning efficiency is improved; 2. the working mode is reliable, the realization is easy, and the system cost is not increased.
Drawings
FIG. 1 is a schematic view of an automatic dust suction robot;
FIG. 2 is a first optimal path planning method;
FIG. 3 is a second method for optimal path planning;
fig. 4 is a third optimal path planning method.
Detailed Description
The invention will now be further described with reference to the accompanying drawings in which:
referring to fig. 1, a method for planning a random path of an automatic vacuum robot includes two driving wheels 1, two driving motors 2 connected to the driving wheels 1, an encoder installed on the driving motors 2, and a supporting wheel 3, wherein the supporting wheel 3 plays a supporting role and is not used for driving. Wherein, the driving motor 2 and the encoder are connected with the controller. The controller realizes the free movement of the automatic dust collection robot by respectively setting the speed and the direction of the two driving wheels 1, can calculate the relative movement distance and the rotation direction of the automatic dust collection robot according to the signals of the encoder, and can calculate the coordinates (x, y) of the current position by taking the initial position as the origin of coordinates. Due to factors such as mechanical clearance, calculation error and ground slip, the coordinates (x, y) will have accumulated errors, that is, the errors will be larger and larger as time goes on, but the coordinates (x, y) will still have a value in a period of time.
The automatic dust collection robot further comprises an obstacle detection device arranged at the front part of the automatic dust collection robot, and the obstacle detection device is also connected with the controller. The obstacle detection device may employ a sensor such as an ultrasonic sensor, an infrared sensor, a laser radar sensor, or a combination of two or more sensors.
The automatic dust collection robot performs cleaning work simultaneously in the walking process, so that the selection of the walking path directly determines the cleaning efficiency and is in a very important position.
The controller is internally provided with an optimal path planning method, and the optimal path planning method comprises the following steps:
(1) setting a data link list L0={Pi(xi,yi) N-1, x, wherein i =0, 1, 2iAnd yiIs a coordinate value, N is a data link list L0Length of (2), data link list L0Coordinate data of a stop position after detecting an obstacle for the automatic dust collection robot in the near term;
data link list L0The length N of (a) should not be too large, otherwise too large error results in poor planning effect.
(2) The automatic dust collection robot moves forwards in a linear motion mode and continuously detects obstacles; when the obstacle is detected, the automatic dust collection robot stops, records the coordinates (x, y) of the current position, and stores the coordinates into a data linked list L0Then entering step 3;
in step 2, the coordinates (x, y) of the current position are stored in a data link list L0The method comprises the following steps:
let Pi(xi,yi)=Pi-1(xi-1,yi-1) ，i=1,2,3.....N-1;
Then P0(x0,y0) = (x, y), link table operation is completed.
(3) And obtaining the data chain table L0Center point of (x)o，yo) Extracting the data chain table L by taking the central point O as the center0Contour line segment S in (1)j(m, n) and stores a data link list L1Wherein M =0, 1, 2<N, the contour line segment Sj(m, n) represents a data link list L0Point P inm(xm,ym) And Pn(xn,yn) A line segment;
in step 3, the data link list L0Center point of (x)o，yo) The coordinate calculation method comprises the following steps:
searching data link list L0Maximum and minimum values of the middle coordinate data: x is the number ofmax，xmin，ymax，ymin；
Calculating xo=
，yo=
。
Referring to FIGS. 2-3, in step 3, the contour line segment S in the data link list L0 is extracted centered on the center point Oj(m, n) using the following steps:
calculating a linked list of data L0Point P ini(xi,yi) And a center point O (x)o，yo) Distance D ofi=
Finding the point P with the largest distanceM(xM,yM)；
With PM(xM,yM) For the vertex, calculate the data link list L0Point P ini(xi,yi) And a center point O (x)o，yo) Angle formed beta =
Taking the point with the maximum angle beta as a contour point and marking as PN(xN,yN) Thus forming data S0(M, N) and storing a data link list L1；
Then with PN(xN,yN) Continuously searching contour points for the vertex and storing the contour points into a data chain table L1Up to the return point PM(xM,yM) And ending and returning.
(4) A central point O and a data chain table L1Contour line segment S in (1)j(m, n) form a triangular region, calculate OPmDirection angle theta1=
，OPnDirection angle theta2=
And a data link list L0Point P ini(xi,yi) A direction angle theta formed with the center point O3=
If for the data link list L0All points P ini(xi,yi)，
At the same time
If all the points are true, no point appears in the triangular area, indicating the contour line segment SjIf the direction corresponding to (m, n) is not cleared, then the data link list L1The contour line segment S is reservedj(m, n); on the contrary, in the data link list L1Deleting contour line segment Sj(m,n)；
Referring also to FIG. 3, for the data link list L0All points P ini(xi,yi) All satisfy the condition
At the same time
Then, P is describediIs located at triangle OPmPnOuter, contour line segment Sj(m, n) is an uncleaned direction with respect to the center point O.
(5) Calculating a data chain table L1Contour line segment S in (1)jLength W of (m, n)j=
Comparing the contour line segment SjLength W of (m, n)jThe size of (1) is determined by taking the contour line segment S with the maximum lengthmax(m, n), contour line segment SmaxDirection angle α = (m, n) with respect to center point O
(ii) a The automatic dust collection robot selects a direction angle alpha as the next cleaning direction.
Referring to fig. 4, the direction having the largest angle is selected among all the non-cleaning directions.
In conclusion, the automatic dust collection robot selects the non-cleaned area as the cleaning direction of the next step through the analysis of the historical data, so that the probability of entering the non-cleaned area is increased, the path repetition is reduced, the cleaning efficiency is effectively improved, the scheme does not need to increase any hardware cost, the working mode is flexible and reliable, and the realization is easy. Meanwhile, the scheme is also suitable for the automatic dust collection robot to find the path planning of the charging seat.
Claims (2)
1. An automatic dust collection robot optimal path planning method based on historical data analysis comprises two driving wheels, two driving motors connected with the driving wheels, encoders mounted on the driving motors, and a supporting wheel, and an obstacle detection device installed in front of the robot, the driving motor, the encoder and the obstacle detection device are connected with the controller, the controller realizes the free movement of the automatic dust collection robot by respectively setting the speed and the direction of the two driving wheels, and the relative moving distance and the rotating direction of the robot cleaner can be calculated according to the signal of the encoder, the coordinate (x, y) of the current position can be calculated by taking the initial position as the origin of coordinates, and the method is characterized in that: the controller is internally provided with an optimal path planning method, and the optimal path planning method comprises the following steps:
(1) setting a data link list L0={Pi(xi,yi) N-1, x, wherein i =0, 1, 2iAnd yiIs a coordinate value, N is a data link list L0Length of (2), data link list L0For the automatic dust-collecting robot described recently to detect the seat at the stopping position after the obstacleMarking data;
(2) the automatic dust collection robot moves forwards in a linear motion mode and continuously detects obstacles; when the obstacle is detected, the automatic dust collection robot stops, records the coordinates (x, y) of the current position, and stores the coordinates into a data linked list L0Then entering step 3;
(3) and obtaining the data chain table L0Center point of (x)o，yo) Extracting the data chain table L by taking the central point O as the center0Contour line segment S in (1)j(m, n) and stores a data link list L1Wherein M =0, 1, 2<N, the contour line segment Sj(m, n) represents a data link list L0Point P inm(xm,ym) And Pn(xn,yn) A line segment;
the data linked list L0Center point of (x)o，yo) The coordinate calculation method comprises the following steps: searching data link list L0Maximum and minimum values of the middle coordinate data: x is the number ofmax，xmin，ymax，ymin(ii) a Calculating xo=
，yo=
；
The data linked list L is extracted by taking the central point O as the center0Contour line segment S in (1)j(m, n) using the following steps:
calculating a linked list of data L0Point P ini(xi,yi) And a center point O (x)o，yo) Distance D ofi=
Finding the point P with the largest distanceM(xM,yM)；
With PM(xM,yM) For the vertex, calculate the data link list L0Point P ini(xi,yi) And a center point O (x)o，yo) Angle formed beta =
Taking the point with the maximum angle beta as a contour point and marking as PN(xN,yN) Thus forming data S0(M, N) and storing a data link list L1；
Then with PN(xN,yN) Continuously searching contour points for the vertex and storing the contour points into a data chain table L1Up to the return point PM(xM,yM) Ending and returning;
(4) a central point O and a data chain table L1Contour line segment S in (1)j(m, n) form a triangular region, calculate OPmDirection angle theta1= 
，OPnDirection angle theta2=
And a data link list L0Point P ini(xi,yi) A direction angle theta formed with the center point O3=
If for the data link list L0All points P ini(xi,yi)，
At the same time
If all the points are true, no point appears in the triangular area, indicating the contour line segment SjIf the direction corresponding to (m, n) is not swept, then the data isChain table L1The contour line segment S is reservedj(m, n); on the contrary, in the data link list L1Deleting contour line segment Sj(m,n)；
(5) Calculating a data chain table L1Contour line segment S in (1)jLength W of (m, n)j=
Comparing the contour line segment SjLength W of (m, n)jThe size of (1) is determined by taking the contour line segment S with the maximum lengthmax(m, n), contour line segment SmaxDirection angle α = (m, n) with respect to center point O
(ii) a The automatic dust collection robot selects a direction angle alpha as the next cleaning direction.
2. The automatic dust collection robot optimal path planning method based on historical data analysis according to claim 1, characterized in that: in step 2, the coordinates (x, y) of the current position are stored in a data link list L0The method comprises the following steps:
let Pi(xi,yi)=Pi-1(xi-1,yi-1) ，i=1,2,3.....N-1;
Then P0(x0,y0) = (x, y), link table operation is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810228764.4A CN108319270B (en) | 2018-03-20 | 2018-03-20 | Automatic dust collection robot optimal path planning method based on historical data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810228764.4A CN108319270B (en) | 2018-03-20 | 2018-03-20 | Automatic dust collection robot optimal path planning method based on historical data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108319270A CN108319270A (en) | 2018-07-24 |
| CN108319270B true CN108319270B (en) | 2021-01-01 |
Family
ID=62898077
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810228764.4A Active CN108319270B (en) | 2018-03-20 | 2018-03-20 | Automatic dust collection robot optimal path planning method based on historical data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108319270B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110867925B (en) * | 2019-11-29 | 2022-03-04 | 小狗电器互联网科技（北京）股份有限公司 | Recharging method, recharging equipment and recharging storage medium |
| CN110989588A (en) * | 2019-11-29 | 2020-04-10 | 小狗电器互联网科技（北京）股份有限公司 | Robot and recharging system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6823230B1 (en) * | 2000-09-07 | 2004-11-23 | Honeywell International Inc. | Tool path planning process for component by layered manufacture |
| CN101077578A (en) * | 2007-07-03 | 2007-11-28 | 北京控制工程研究所 | Mobile Robot local paths planning method on the basis of binary environmental information |
| CN101738195A (en) * | 2009-12-24 | 2010-06-16 | 厦门大学 | Method for planning path for mobile robot based on environmental modeling and self-adapting window |
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
| CN103251358A (en) * | 2012-02-16 | 2013-08-21 | 恩斯迈电子(深圳)有限公司 | Control method of sweeping robot |
| CN104615138A (en) * | 2015-01-14 | 2015-05-13 | 上海物景智能科技有限公司 | Dynamic indoor region coverage division method and device for mobile robot |
| CN105651286A (en) * | 2016-02-26 | 2016-06-08 | 中国科学院宁波材料技术与工程研究所 | Visual navigation method and system of mobile robot as well as warehouse system |
| CN106767819A (en) * | 2016-12-07 | 2017-05-31 | 北京建筑大学 | A kind of indoor navigation data construction method and navigation system based on BIM |
-
2018
- 2018-03-20 CN CN201810228764.4A patent/CN108319270B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6823230B1 (en) * | 2000-09-07 | 2004-11-23 | Honeywell International Inc. | Tool path planning process for component by layered manufacture |
| CN101077578A (en) * | 2007-07-03 | 2007-11-28 | 北京控制工程研究所 | Mobile Robot local paths planning method on the basis of binary environmental information |
| CN101738195A (en) * | 2009-12-24 | 2010-06-16 | 厦门大学 | Method for planning path for mobile robot based on environmental modeling and self-adapting window |
| CN103251358A (en) * | 2012-02-16 | 2013-08-21 | 恩斯迈电子(深圳)有限公司 | Control method of sweeping robot |
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
| CN104615138A (en) * | 2015-01-14 | 2015-05-13 | 上海物景智能科技有限公司 | Dynamic indoor region coverage division method and device for mobile robot |
| CN105651286A (en) * | 2016-02-26 | 2016-06-08 | 中国科学院宁波材料技术与工程研究所 | Visual navigation method and system of mobile robot as well as warehouse system |
| CN106767819A (en) * | 2016-12-07 | 2017-05-31 | 北京建筑大学 | A kind of indoor navigation data construction method and navigation system based on BIM |
Non-Patent Citations (5)
| Title |
|---|
| "combined coverage path planning for autonomous cleaning robots in unstrucured environments";Yu Liu等;《Proceedings of the 7th world congress on intelligent control and automation》;20080627;全文 * |
| "Motion planning for autonomous landmine detection and clearance robots";Ibrahim A. Hameed;《2016 International Workshop on Recent Advances in Robotics and Sensor Technology for Humanitarian Demining and Counter-IEDs (RST)》;20161130;全文 * |
| "家庭清扫机器人路径覆盖系统的设计与实现";周亚楠;《信息科技辑》;20120715;全文 * |
| "平面离散点集的边界搜索算法";陈涛等;《计算机仿真》;20140331;第21卷(第3期);全文 * |
| "自主吸尘机器人的研究现状";朱世强等;《机器人》;20041130;第26卷(第6期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108319270A (en) | 2018-07-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10678251B2 (en) | Cleaning method for a robotic cleaning device | |
| EP3230814B1 (en) | Using laser sensor for floor type detection | |
| CN108852184B (en) | Non-blind area sweeping robot based on deep learning algorithm and sweeping control method thereof | |
| KR100690669B1 (en) | Position-reconizing system for a self-moving robot | |
| CN106200645A (en) | Autonomous robot, control device and control method | |
| WO2021037065A1 (en) | Cleaning robot and control method therefor | |
| CN106821157A (en) | The cleaning method that a kind of sweeping robot is swept the floor | |
| CN106618386B (en) | Cleaning robot | |
| US10534367B2 (en) | Experience-based roadmap for a robotic cleaning device | |
| CN110946508B (en) | Control method and device of sweeping robot using laser radar and camera | |
| CN106175600A (en) | The method controlling mobile robot | |
| CN101714000A (en) | Route planning method of automatic dust collector | |
| CN107678429B (en) | Robot control method and chip | |
| CN214484411U (en) | Autonomous floor cleaner | |
| WO2019056998A1 (en) | Autonomous mobile robot and pile-seeking method therefor, control apparatus and smart cleaning system | |
| CN108319270B (en) | Automatic dust collection robot optimal path planning method based on historical data analysis | |
| CN108469819A (en) | A kind of z font return path planing methods of automatic dust absorption machine people | |
| CN108345308A (en) | A kind of best random walk selection method of automatic dust absorption machine people | |
| CN109827592A (en) | A kind of trapped detection method of sweeping robot | |
| CN204169779U (en) | Clean robot | |
| CN108931980B (en) | Marking method and chip of robot built-in map and indoor cleaning robot | |
| CN103584798A (en) | Intelligent dust collector control system and intelligent dust collector cleaning method | |
| CN202714801U (en) | Intelligent vacuum cleaner control system | |
| CN108873892B (en) | Automatic dust collection robot optimal path planning method based on path density analysis | |
| CN108469264B (en) | Automatic dust collection robot optimal path planning method based on angle analysis |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Bai Di Inventor after: Liu Yu Inventor before: Liu Yu |
|
| CB03 | Change of inventor or designer information | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231008 Address after: Room 502, Unit 1, Building 1, No. 14 Lishan Road, Yiyuan County, Zibo City, Shandong Province, 256100 Patentee after: Bai Di Address before: 310013 no.256, 6th floor, building 2, Huahong building, 248 Tianmushan Road, Xihu District, Hangzhou City, Zhejiang Province Patentee before: HANGZHOU JINGYI INTELLIGENT SCIENCE & TECHNOLOGY Co.,Ltd. |
|
| TR01 | Transfer of patent right |