CN104035444A - Robot map establishing and storing method - Google Patents
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Abstract
本发明公开了一种机器人地图构建存储方法,运用电子罗盘、里程计结合GPS来实现机器人定位和坐标计算,在创建地图数据前机器人首先沿着边界线围绕工作区域运行一圈,建立地图边界数据,并推算出地图数据与存储单元地址的映射关系,然后机器人在区域内运行并建立内部地图数据。地图数据的格式为:{X坐标,Y坐标,地图栅格属性},为了便于数据存取,数据按照一定规律映射在存储空间内。
The invention discloses a method for constructing and storing a robot map, using an electronic compass and an odometer combined with GPS to realize robot positioning and coordinate calculation. Before creating map data, the robot first runs around the working area along the boundary line to establish map boundary data , and deduce the mapping relationship between the map data and the address of the storage unit, and then the robot runs in the area and establishes the internal map data. The format of the map data is: {X coordinate, Y coordinate, map grid attribute}. In order to facilitate data access, the data is mapped in the storage space according to certain rules.
Description
技术领域technical field
本发明属于机器人技术领域,涉及一种构建地图信息的方法。The invention belongs to the technical field of robots and relates to a method for constructing map information.
背景技术Background technique
目前市场上的大多数割草机器人的割草路径都是随机的,因此导致割草机除完草后的草坪略显凌乱,同时割草实现对工作区域的完全遍历需要花费更多时间,导致效率较低,而非智能式割草机虽然能在人工的操作下割出平整的草坪,但是比较费时费力,因此将自动定位技术与割草机相结合来实现割草机的定向定位,并运用地图构建技术实现机器人的自主路径规划,最终割出平整规则的草坪。The mowing paths of most lawn mowing robots currently on the market are random, which leads to a slightly messy lawn after the mower has mowed the grass. At the same time, it takes more time to mow the grass to completely traverse the working area, resulting in The efficiency is low. Although the non-intelligent lawn mower can cut a flat lawn under manual operation, it is time-consuming and laborious. Therefore, the automatic positioning technology is combined with the lawn mower to realize the directional positioning of the lawn mower, and Using map construction technology to realize the robot's autonomous path planning, and finally cut out a smooth and regular lawn.
发明内容Contents of the invention
技术问题:本发明提供一种实现割草机器人的自动路径规划,能够快速完成地图数据存储和读取,提高地图构建和使用的效率的机器人地图构建存储方法。Technical problem: The present invention provides a robot map construction and storage method that realizes the automatic path planning of the mowing robot, can quickly complete the storage and reading of map data, and improves the efficiency of map construction and use.
技术方案:本发明的机器人地图构建与存储方法,包括以下步骤:Technical solution: The robot map construction and storage method of the present invention includes the following steps:
1)将基站坐标设为机器人起始坐标(x0,y0),并设定机器人从基站出发时,工作区域内x坐标的最大值xmax的初始值、最小值xmin的初始值,y坐标的最大值ymax的初始值、最小值ymin的初始值分别为xmax=x0,xmin=x0,ymax=y0,ymax=y0;1) Set the base station coordinates as the starting coordinates of the robot (x 0 , y 0 ), and set the initial value of the maximum value x max and the minimum value x min of the x coordinate in the working area when the robot starts from the base station, The initial value of the maximum value y max of the y coordinate and the initial value of the minimum value y min are respectively x max =x 0 , x min =x 0 , y max =y 0 , y max =y 0 ;
2)然后机器人从基站出发,沿着工作区域的边界线运行一周后返回基站,运行过程中,按照如下方式不断更新x坐标的最大值xmax、最小值xmin,y坐标的最大值ymax、最小值ymin:在任意i时刻分别对比坐标(xi,yi)与上一次更新的xmax、xmin、ymax、ymin的大小关系,若xi<xmin则xmin=xi,否则xmin的值保持不变,若xi>xmax则xmax=xi,否则xmax的值保持不变,若yi<ymin则ymin=yi,否则ymin的值保持不变,若yi>ymax则ymax=yi,否则ymax的值保持不变,其中,(xi,yi)为i时刻时机器人的坐标;2) Then the robot starts from the base station and returns to the base station after running along the boundary line of the working area for a week. During the operation, the maximum value x max and the minimum value x min of the x coordinate and the maximum value y max of the y coordinate are continuously updated as follows , the minimum value y min : at any time i, compare the size relationship between the coordinates (xi , y i ) and the last updated x max , x min , y max , y min , if x i < x min then x min = x i , otherwise the value of x min remains unchanged, if x i > x max then x max = x i , otherwise the value of x max remains unchanged, if y i < y min then y min = y i , otherwise y min The value of remains unchanged, if y i >y max then y max =y i , otherwise the value of y max remains unchanged, where (xi , y i ) is the coordinates of the robot at time i;
3)根据机器人沿边界线运行一周后最终更新得到的xmax、xmin、ymax、ymin,确定表征边界线最大范围的四个坐标点分别为(xa,ymax)、(xb,ymin)、(xmax,ya)、(xmin,yb),其中xa为ymax对应的横坐标、xb为ymin对应的横坐标,ya为xmax对应的横坐标,yb为xmin对应的横坐标;3) According to the x max , x min , y max , and y min that are finally updated after the robot runs along the boundary line for a week, determine the four coordinate points representing the maximum range of the boundary line as (x a , y max ), (x b , y min ), (x max ,y a ), (x min ,y b ), where x a is the abscissa corresponding to y max , x b is the abscissa corresponding to y min , and y a is the abscissa corresponding to x max , y b is the abscissa corresponding to x min ;
然后根据这四个表征边界线最大范围的坐标计算出工作区域内x坐标最大差值Xmax=xmax-xmin和y坐标最大差值Ymax=ymax-ymin;Then calculate the maximum difference of x coordinates X max = x max -x min and the maximum difference of y coordinates Y max = y max - y min in the working area according to the coordinates of the four characterizing the maximum range of the boundary line;
同时计算出工作区域的中心坐标(xc,yc),其中,xc=[(xmax+xmin)2],yc=[(ymax+ymin)/2];At the same time, calculate the center coordinates (x c , y c ) of the working area, where x c =[(x max +x min )2], y c =[(y max +y min )/2];
4)根据下式计算出表征地图大小的参数n:n=[X/2Δ]+1;4) Calculate the parameter n representing the size of the map according to the following formula: n=[X/2Δ]+1;
其中Δ为正方形地图栅格的边长,X为工作区域最大范围内x坐标最大差值Xmax和y坐标最大差值Ymax的较大值,即当Xmax≥Ymax时,X=Xmax,而Xmax<Ymax时,X=Ymax;Among them, Δ is the side length of the square map grid, and X is the larger value of the maximum difference X max of x coordinates and the maximum difference Y max of y coordinates within the maximum range of the working area, that is, when X max ≥ Y max , X=X max , and X max < Y max , X = Y max ;
5)根据表征地图大小的参数n实现地图数据与存储单元地址的映射,具体方法为:将所有地图栅格的数据按照{xi,yi,地图栅格属性}的格式存储在存储单元中,其中每个地图栅格数据占用存储单元空间大小为m字节,地图中心坐标(xc,yc)存储在存储单元的起始地址,该起始地址在存储单元中与最小地址的偏移量为k,k≥0,其他坐标(xi,yi)的存储位置根据下式求得的该坐标与最小地址的偏移量来确定:5) Realize the mapping between the map data and the address of the storage unit according to the parameter n representing the size of the map. The specific method is: store the data of all map grids in the storage unit in the format of {xi , y i , map grid attribute} , where each map raster data occupies a storage unit with a size of m bytes, the coordinates of the map center (x c , y c ) are stored in the starting address of the storage unit, and the offset between the starting address and the minimum address in the storage unit The displacement is k, k≥0, and the storage location of other coordinates ( xi , y i ) is determined according to the offset between the coordinate and the minimum address obtained from the following formula:
M(2n+1)m+Nm+k;M(2n+1)m+Nm+k;
其中,M、N由以下方法确定:Among them, M and N are determined by the following methods:
L1=(xi-xc)/Δ,L2=(yi-yc)/Δ;L 1 =(x i -x c )/Δ, L 2 =(y i -y c )/Δ;
当L1>0时,M=2|L1|,当L1<0时,M=2|L1|-1;When L 1 >0, M=2|L 1 |, when L 1 <0, M=2|L 1 |-1;
当L2>0时,N=2|L2|,当L2<0时,N=2|L2|-1。When L 2 >0, N=2|L 2 |, and when L 2 <0, N=2|L 2 |−1.
本发明方法的优选方案中,步骤5)中的地图栅格属性由多种与机器人地图相关的元素组成,包括用于表征地图栅格内的环境的属性和表征机器人是否经过此地图栅格的属性。In the preferred solution of the method of the present invention, the map grid attribute in step 5) is composed of various elements related to the robot map, including attributes used to characterize the environment in the map grid and whether the robot passes through the map grid Attributes.
本系统的方法能够完成机器人地图的创建与存储,从而实现机器人的自主定位导航,在创建地图数据时机器人首先围绕工作区域运行一圈,根据传感器计算出的坐标值计算出地图边界的数据,并生成地图数据与存储单元地址的映射关系,然后机器人在边界区域内建立内部地图数据,并更新数据存储器中的地图属性;在地图坐标建立后,机器人按照地图运行,机器人通过定位后读取当前坐标以及相邻四个坐标的数据,根据前进方向以及相邻坐标的栅格特性来判断机器人的下一步动作和运行方向,在机器人工作电压不足时,机器人自动存储当前坐标与航向并返回基站充电,完成充电后读取上次记录的坐标和航向,并自动规划最优路径到达坐标位置,然后继续工作。本发明方法中,机器人通过定位导航传感器构建出地图数据,并将地图数据与存储单元地址一一映射,方便大量地图数据的存储与读取。The method of this system can complete the creation and storage of the robot map, so as to realize the autonomous positioning and navigation of the robot. When creating the map data, the robot first runs around the working area, calculates the data of the map boundary according to the coordinate values calculated by the sensor, and Generate the mapping relationship between the map data and the address of the storage unit, and then the robot establishes the internal map data in the boundary area, and updates the map attributes in the data storage; after the map coordinates are established, the robot runs according to the map, and the robot reads the current coordinates after positioning And the data of the adjacent four coordinates, according to the forward direction and the grid characteristics of the adjacent coordinates to judge the next action and running direction of the robot. When the working voltage of the robot is insufficient, the robot automatically stores the current coordinates and heading and returns to the base station for charging. After charging, read the coordinates and heading recorded last time, and automatically plan the optimal path to reach the coordinate position, and then continue to work. In the method of the present invention, the robot constructs the map data through the positioning and navigation sensor, and maps the map data with the address of the storage unit one by one, so as to facilitate the storage and reading of a large amount of map data.
有益效果:本发明与现有技术相比,具有以下优点:Beneficial effect: compared with the prior art, the present invention has the following advantages:
本发明能够很好地实现地图数据的构建与存储,该方法中机器人地图数据为栅格形式,使用栅格地图的属性能够很好地描述地图环境,鉴于栅格地图的数据量较大的原因,在建立地图时,机器人从基站出发,沿着绕边界线运行,运行过程中不断推算当前的坐标,在运行一周后获取表征地图大小的四个重要特征坐标点,分别为地图横坐标最大和最小的两个点,以及纵坐标最大和最小的两个点,然后根据这四个特征坐标点自主生成地图数据与存储单元地址映射关系,通过地址映射的建立将地图数据与存储单元紧密结合起来,不仅能够实现地图数据的快速读取与存储,同时采用参数调节的方式可以使该方法在不同的地图大小下均能适用,使机器人能够在任何工作环境下运行。The present invention can well realize the construction and storage of map data. In this method, the map data of the robot is in the form of a grid, and the attributes of the grid map can be used to describe the map environment well. In view of the large amount of data of the grid map , when building a map, the robot starts from the base station and runs around the boundary line. During the operation, the current coordinates are continuously calculated. After running for a week, four important characteristic coordinate points representing the size of the map are obtained, which are the maximum and maximum abscissa coordinates of the map and The two smallest points, as well as the two points with the largest and smallest vertical coordinates, and then independently generate the map data and storage unit address mapping relationship according to these four characteristic coordinate points, and closely combine the map data with the storage unit through the establishment of address mapping , not only can realize the fast reading and storage of map data, but also adopt the method of parameter adjustment to make the method applicable under different map sizes, so that the robot can run in any working environment.
通过该方法建立地图数据后,地图数据具有唯一性,即存储单元的内容与整个环境地图坐标一一对应,当机器人运行过程中需要调用地图时,只需要计算出当前坐标对应于映射图中的参数即可快速读取地图数据,能够保证机器人对环境地图有一个很清晰的认知。After the map data is established by this method, the map data is unique, that is, the content of the storage unit corresponds to the coordinates of the entire environment map. When the robot needs to call the map during operation, it only needs to calculate the current coordinates corresponding to the coordinates in the map. The map data can be quickly read through parameters, which can ensure that the robot has a clear understanding of the environmental map.
附图说明Description of drawings
图1为栅格地图示意图;将地图用栅格的形式表示,每个格点中的数字代表该栅格内的属性;Figure 1 is a schematic diagram of a grid map; the map is represented in the form of a grid, and the numbers in each grid point represent the attributes in the grid;
图2为沿边界运行图;通过该方式确定地图的大小与参数;Figure 2 is a diagram of running along the boundary; the size and parameters of the map are determined in this way;
图3为存储单元地址映射图;表示地图数据与存储单元地址的映射关系。FIG. 3 is a storage unit address mapping diagram; showing the mapping relationship between map data and storage unit addresses.
具体实施方式Detailed ways
下面对本发明技术方案进行进一步详细说明。The technical solution of the present invention will be further described in detail below.
本发明方法中,所述的机器人主要是割草机器人,它是一种四轮式机器人,后两轮为差动轮,能够实现机器人任意方向的运动,机器人由锂电池供电,在一定的区域范围内自主工作,该区域由边界线人为圈定,可以为任意形状和大小,边界线为通电导线,导线内通有特定的边界信号,该边界信号由充电基站发出,同时基站能够给机器人提供充电的来源,当机器人电量不足时会自动沿着边界线返回基站充电,充满电后自动出基站工作,机器人通过安装在机器人内部的感应线圈接收边界线发出的电磁信号来识别机器人处于边界线的内部或外部。同时,该机器人还能够通过霍尔传感器识别外部障碍物,当机器人被抬起或者遇到障碍物时霍尔传感器会发出信号,从而实现机器人的环境识别。In the method of the present invention, the robot is mainly a mowing robot, which is a four-wheeled robot, and the rear two wheels are differential wheels, which can realize the movement of the robot in any direction. The robot is powered by a lithium battery. Work autonomously within the range. The area is artificially delineated by the boundary line, which can be of any shape and size. The boundary line is an electrified wire, and there is a specific boundary signal in the wire. The boundary signal is sent by the charging base station, and the base station can provide charging for the robot. When the power of the robot is low, it will automatically return to the base station along the boundary line to charge, and it will automatically go out of the base station to work after it is fully charged. The robot will recognize that the robot is inside the boundary line by receiving the electromagnetic signal sent by the boundary line through the induction coil installed inside the robot. or external. At the same time, the robot can also identify external obstacles through the Hall sensor. When the robot is lifted or encounters an obstacle, the Hall sensor will send a signal, thereby realizing the environment recognition of the robot.
在建立地图时,机器人能够运用电子罗盘、里程计、GPS实现自主定位导航,通过电子罗盘测量航向角,然后结合里程计航位推算出机器人的相对坐标,再根据GPS的坐标进行绝对定位和误差消除,最终得到机器人任意时刻的坐标(xk,yk)。具体推算方法如下:When building a map, the robot can use the electronic compass, odometer, and GPS to realize autonomous positioning and navigation, measure the heading angle through the electronic compass, and then calculate the relative coordinates of the robot based on the odometer dead position, and then perform absolute positioning and error based on the GPS coordinates Eliminate, and finally get the coordinates (x k , y k ) of the robot at any time. The specific calculation method is as follows:
割草机器人在运行时可以认为是在一定的二维平面的运行,构建一个局部的平面直角坐标系,设x为局部平面直角坐标系的正东方向,y为局部平面直角坐标系的正北方向。设割草机器人初始时刻t0的位置R0坐标为(x0,y0),航向角为θ0,根据t1-t0时间段内的运行距离以及航向角可以推算出t1时刻的坐标位置。The mowing robot can be regarded as running on a certain two-dimensional plane during operation, and constructs a local plane Cartesian coordinate system. Let x be the due east direction of the local plane Cartesian coordinate system, and y be the true north of the local plane Cartesian coordinate system. direction. Let the coordinates of the position R 0 of the lawn mowing robot at the initial time t 0 be (x 0 , y 0 ), and the heading angle be θ 0 . According to the running distance and heading angle during the time period t 1 -t 0 , the position at the time t 1 can be calculated. coordinate location.
设R0和R1之间的距离为S0,该量由机器人上的里程计测量得出,当t1-t0足够小时认为机器人做直线运动,因此可以可以得到R1的坐标为:Let the distance between R 0 and R 1 be S 0 , which is measured by the odometer on the robot. When t 1 -t 0 is small enough, the robot is considered to be moving in a straight line, so the coordinates of R 1 can be obtained as:
x1=x0+S0 cos θ0 x 1 =x 0 +S 0 cos θ 0
y1=y0+S0 sin θ0 y 1 =y 0 +S 0 sin θ 0
根据上式递推可以得到R2的坐标为:According to the above formula recursion, the coordinates of R2 can be obtained as:
x2=x1+S1 cos θ1=x0+S0 cos θ0+S1 cos θ1 x 2 =x 1 +S 1 cos θ 1 =x 0 +S 0 cos θ 0 +S 1 cos θ 1
y2=y1+S1 sin θ1=y0+S0 sin θ0+S1 sin θ1 y 2 =y 1 +S 1 sin θ 1 =y 0 +S 0 sin θ 0 +S 1 sin θ 1
按照此规律递推可以推算出任意时刻tk位置Rk的坐标为:According to this rule, the coordinates of the position R k at any time t k can be deduced as:
将基站坐标设为机器人起始坐标(x0,y0),并设定机器人从基站出发时,工作区域内x坐标的最大值xmax的初始值、最小值xmin的初始值,y坐标的最大值ymax的初始值、最小值ymin的初始值分别为xmax=x0,xmin=x0,ymax=y0,ymax=y0;然后机器人从基站出发,沿着工作区域的边界线运行一周后返回基站,运行过程中,按照如下方式不断更新x坐标的最大值xmax、最小值xmin,y坐标的最大值ymax、最小值ymin:Set the coordinates of the base station as the initial coordinates of the robot (x 0 , y 0 ), and set the initial value of the maximum value x max of the x coordinate, the initial value of the minimum value x min , and the y coordinate of the robot when it starts from the base station. The initial value of the maximum value y max and the initial value of the minimum value y min are respectively x max = x 0 , x min = x 0 , y max = y 0 , y max = y 0 ; then the robot starts from the base station, along The boundary line of the working area returns to the base station after a week of operation. During the operation, the maximum value x max and minimum value x min of the x coordinate, and the maximum value y max and minimum value y min of the y coordinate are continuously updated as follows:
在任意i时刻分别对比坐标(xi,yi)与上一次更新的xmax、xmin、ymax、ymin的大小关系,若xi<xmin则xmin=xi,否则xmin的值保持不变,若xi>xmax则xmax=xi,否则xmax的值保持不变,若yi<ymin则ymin=yi,否则ymin的值保持不变,若yi>ymax则ymax=yi,否则ymax的值保持不变,其中,(xi,yi)为i时刻时机器人的坐标;根据机器人沿边界线运行一周后最终更新得到的xmax、xmin、ymax、ymin,确定表征边界线最大范围的四个坐标点分别为(xa,ymax)、(xb,ymin)、(xmax,ya)、(xmin,yb),其中xa为ymax对应的横坐标、xb为ymin对应的横坐标,ya为xmax对应的横坐标,yb为xmin对应的横坐标;然后根据这四个表征边界线最大范围的坐标计算出工作区域内x坐标最大差值Xmax=xmax-xmin和y坐标最大差值Ymax=ymax-ymin;同时计算出工作区域的中心坐标(xc,yc),其中,xc=[(xmax+xmin)2],yc=[(ymax+ymin)2];然后根据下式计算出表征地图大小的参数n:n=[X/2Δ]+1;本方法中的地图栅格为正方形栅格,即每个地图栅格的长与宽均相同,将整个环境地图分割为多个相邻的正方形栅格方块,正方形地图栅格的边长为Δ,X为工作区域最大范围内x坐标最大差值Xmax和y坐标最大差值Ymax的较大值,即当Xmax≥Ymax时,X=Xmax,而Xmax<Ymax时,X=Ymax;根据表征地图大小的参数n实现地图数据与存储单元地址的映射,具体方法为:将所有地图栅格的数据按照{xi,yi,地图栅格属性}的格式存储在存储单元中,每个地图栅格数据占用存储单元空间大小为m字节,其中地图栅格属性可以由多种与机器人地图相关的元素组成,包括用于表征地图栅格内的环境的属性,例如运用数字0,1,2……的形式分别表示此地图栅格内部有树木、边界线、充电基站等外物;还包括表征机器人是否经过此地图栅格的属性,用于实现机器人的地图遍历,地图中心坐标(xc,yc)存储在存储单元的起始地址,该起始地址在存储单元中与最小地址的偏移量为k,k≥0,其他坐标(xi,yi)的存储位置根据下式求得的该坐标与最小地址的偏移量来确定:At any time i, compare the relationship between the coordinates ( xi , y i ) and the last updated x max , x min , y max , and y min . If x i < x min , then x min = x i , otherwise x min The value remains unchanged, if x i > x max then x max = x i , otherwise the value of x max remains unchanged, if y i < y min then y min = y i , otherwise the value of y min remains unchanged, If y i > y max , then y max = y i , otherwise the value of y max remains unchanged, where ( xi , y i ) is the coordinates of the robot at time i; according to the final update obtained after the robot runs along the boundary line for one week x max , x min , y max , y min , the four coordinate points to determine the maximum range of the boundary line are (x a ,y max ), (x b ,y min ), (x max ,y a ), ( x min ,y b ), where x a is the abscissa corresponding to y max , x b is the abscissa corresponding to y min , y a is the abscissa corresponding to x max , y b is the abscissa corresponding to x min ; then according to These four coordinates representing the maximum range of the boundary line calculate the maximum difference of x coordinates X max = x max -x min and the maximum difference of y coordinates Y max = y max -y min in the working area; at the same time, calculate the center of the working area Coordinates (x c , y c ), where, x c =[(x max +x min )2], y c =[(y max +y min )2]; then calculate the parameters representing the size of the map according to the following formula n: n=[X/2Δ]+1; the map grid in this method is a square grid, that is, the length and width of each map grid are the same, and the entire environmental map is divided into multiple adjacent square grids Grid square, the side length of the square map grid is Δ, X is the larger value of the maximum difference X max of x coordinates and the maximum difference Y max of y coordinates within the maximum range of the working area, that is, when X max ≥ Y max , X =X max , and when X max <Y max , X=Y max ; realize the mapping between map data and storage unit addresses according to the parameter n representing the size of the map. y i , the format of the map raster attribute} is stored in the storage unit, and each map raster data occupies a storage unit space of m bytes, where the map raster attribute can be composed of various elements related to the robot map, including It is used to characterize the attributes of the environment in the map grid, such as using numbers 0, 1, 2... to indicate that there are trees, boundary lines, charging base stations and other foreign objects inside the map grid; it also includes whether the robot passes through this The attribute of the map grid, which is used to realize the map traversal of the robot, The coordinates of the center of the map (x c , y c ) are stored in the starting address of the storage unit, and the offset between the starting address and the minimum address in the storage unit is k, k≥0, other coordinates (xi , y i ) The storage location of is determined according to the offset between the coordinate and the minimum address obtained by the following formula:
M(2n+1)m+Nm+k;M(2n+1)m+Nm+k;
其中,M、N由以下方法确定:Among them, M and N are determined by the following methods:
L1=(xi-xc)/Δ,L2=(yi-yc)/Δ;L 1 =(x i -x c )/Δ, L 2 =(y i -y c )/Δ;
当L1>0时,M=2|L1|,当L1<0时,M=2|L1|-1;When L 1 >0, M=2|L 1 |, when L 1 <0, M=2|L 1 |-1;
当L2>0时,N=2|L2|,当L2<0时,N=2|L2|-1。When L 2 >0, N=2|L 2 |, and when L 2 <0, N=2|L 2 |−1.
然后割草机器人不断更新地图数据相应坐标内的地图栅格属性,将任意坐标下与属性相关的数据快速存到相对应的存储单元中去,实现整个地图的建立。Then the mowing robot continuously updates the map grid attributes in the corresponding coordinates of the map data, and quickly stores the data related to the attributes at any coordinates into the corresponding storage unit to realize the establishment of the entire map.
在地图建立后,割草机器人运行时同样根据电子罗盘、里程计和GPS数据推算出当前所在坐标位置,当机器人运行到坐标(xk,yk)处时,只需计算出此时映射图中相对应的参数i和j即可得到与之相对应的存储单元地址,然后读出该地址后m字节的代表地图属性的内容,同时从相对应的地图存储单元中再直接读出与该坐标相邻的4个坐标,然后根据坐标的栅格属性结合程序算法实现自身运动状态控制,在运行过程中通过霍尔传感器和边界传感器测出的信息来判断构建的地图是否正确,如果发现当前地图与存储的地图不同时更新存储的地图数据。After the map is established, the mowing robot also calculates the current coordinate position according to the electronic compass, odometer and GPS data when it is running. When the robot runs to the coordinates (x k , y k ), it only needs to calculate the map at this time Corresponding parameter i and j in can get the storage unit address corresponding thereto, then read out the content representing the map attribute of m bytes behind the address, and read directly from the corresponding map storage unit at the same time The 4 coordinates adjacent to the coordinates, and then according to the grid properties of the coordinates combined with the program algorithm to realize the control of its own motion state, during the operation, the information measured by the Hall sensor and the boundary sensor is used to judge whether the constructed map is correct. If found Update the stored map data when the current map is different from the stored map.
以上仅是本发明的优选实施方式,应当指出:对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干可以预期的改进和等同替换,这些对本发明权利要求进行改进和等同替换后的技术方案,均落入本发明的保护范围。The above are only preferred embodiments of the present invention, and it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some conceivable improvements and equivalent replacements can also be made, which are essential to the present invention. The technical solutions after the claims are improved and replaced by equivalents all fall into the protection scope of the present invention.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20010018640A1 (en) * | 2000-02-28 | 2001-08-30 | Honda Giken Kogyo Kabushiki Kaisha | Obstacle detecting apparatus and method, and storage medium which stores program for implementing the method |
US20040039498A1 (en) * | 2002-08-23 | 2004-02-26 | Mark Ollis | System and method for the creation of a terrain density model |
CN101388043A (en) * | 2008-09-26 | 2009-03-18 | 北京航空航天大学 | A OGC high-performance remote sensing image map service method based on small block pictures |
CN102138769A (en) * | 2010-01-28 | 2011-08-03 | 深圳先进技术研究院 | Cleaning robot and cleaning method thereby |
CN102999927A (en) * | 2012-11-23 | 2013-03-27 | 中国科学院亚热带农业生态研究所 | Fine partition method of soil pollutant content spatial distribution |
-
2014
- 2014-06-27 CN CN201410305239.XA patent/CN104035444B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20010018640A1 (en) * | 2000-02-28 | 2001-08-30 | Honda Giken Kogyo Kabushiki Kaisha | Obstacle detecting apparatus and method, and storage medium which stores program for implementing the method |
US20040039498A1 (en) * | 2002-08-23 | 2004-02-26 | Mark Ollis | System and method for the creation of a terrain density model |
CN101388043A (en) * | 2008-09-26 | 2009-03-18 | 北京航空航天大学 | A OGC high-performance remote sensing image map service method based on small block pictures |
CN102138769A (en) * | 2010-01-28 | 2011-08-03 | 深圳先进技术研究院 | Cleaning robot and cleaning method thereby |
CN102999927A (en) * | 2012-11-23 | 2013-03-27 | 中国科学院亚热带农业生态研究所 | Fine partition method of soil pollutant content spatial distribution |
Cited By (52)
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US11960304B2 (en) | 2015-03-18 | 2024-04-16 | Irobot Corporation | Localization and mapping using physical features |
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CN105160122B (en) * | 2015-09-08 | 2018-02-23 | 王红军 | A kind of method for measuring similarity of the environmental characteristic based on grating map |
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US11448775B2 (en) | 2017-05-26 | 2022-09-20 | Positec Power Tools (Suzhou) Co., Ltd | Positioning apparatus and method and self-moving device |
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Application publication date: 20140910 Assignee: Rui Rui Intelligent Technology (Kunshan) Co., Ltd. Assignor: Southeast University Contract record no.: 2018320000011 Denomination of invention: Robot map establishing and storing method Granted publication date: 20160824 License type: Exclusive License Record date: 20180109 |