SECTORING OF MAPS FOR ROBOT NAVIGATION
An optical triangulation sensor for distance measurement is described herein. In accordance with one embodiment, the apparatus comprises a light source for the generation of structured light, an optical reception device, at least one attachment element and a carrier with a first groove on a lateral surface of the carrier, wherein the light source and/or optical reception device is at least partially arranged in the first groove and is held in place on the carrier by the attachment element.
1 . A method for the automatic sectoring of a map of an area of robot operation of an autonomous mobile robot, the method comprising:
detecting obstacles;
determining, using sensors arranged on the robot, a size and position on the map;
analyzing, using a processor, the map,
generating, based on a specifiable criterion, hypotheses regarding possible borders of zones, a function of individual detected obstacles, or both hypotheses regarding possible borders of zones and a function of individual detected obstacles; and
sectoring the map of the area of robot operation into zones based on the generated hypotheses.
2 . The method of claim 1 , wherein detecting obstacles comprises detecting boundary lines that are impassable lines for the robot.
3 . The method of claim 1 , wherein the function of individual detected obstacles is a door, inner wall, outer wall, piece of furniture, chair leg, table leg, type of floor covering, a change in the type of floor covering, or a combination thereof.
4 . The method of claim 1 , wherein the specifiable criterion for the generation of hypotheses comprises a distance between two boundary lines lies within a specified interval, two boundary lines lie on one straight line, function designations of the area of operation specified by the user, or a combination thereof.
5 . The method of claim 1 , wherein the generated hypotheses concerning the function of individual detected obstacles are displayed on a human machine interface and a user is afforded a possibility of rejecting or confirming an automatedly generated hypothesis.
6 . The method of claim 1 , wherein in an event that at least two hypotheses contradict each other, at least one is rejected.
7 . The method of claim 1 , further comprising assigning points each hypothesis, wherein a number of points is dependent on the specifiable criterion that leads to the hypothesis.
8 . The method of claim 7 , wherein the hypothesis is rejected if a certain minimum number of points is not reached.
9 . The method of claim 1 , wherein a degree of probability is assigned to each of the hypotheses.
10 . The method of claim 1 , wherein an attribute is assigned to a sub-zone of the zone, wherein the attribute influences processing of the sub-zone by the mobile robot and depends on one or more generated hypotheses.
11 . The method of claim 1 , further comprising:
acquiring images of the area of robot operation by a camera arranged on the robot; and
recognizing in the images, via digital image processing, objects in the area of robot operation.
12 . The method of claim 11 , wherein generating, based on the specifiable criterion, the hypotheses regarding the possible borders of zones, the function of the individual detected obstacles, or both the hypotheses regarding the possible borders of zones and the function of the individual detected obstacles comprises generating, based on detected objects, via digital image processing, the hypotheses regarding the possible borders of zones, the function of the individual detected obstacles, or both the hypotheses regarding the possible borders of zones and the function of the individual detected obstacles.